Characterizing Kiwifruit Carbohydrate Utilization - American Chemical

Article pubs.acs.org/jpr

Characterizing Kiwifruit Carbohydrate Utilization in vitro and its Consequences for Human Faecal Microbiota Douglas I. Rosendale,* Paul A. Blatchford,* Ian M. Sims,‡ Shanthi G. Parkar,* Susan M. Carnachan,‡ Duncan Hedderley,* and Juliet Ansell*,±,* *

The New Zealand Institute for Plant & Food Research Limited, Private Bag 11600, Palmerston North 4442, New Zealand Industrial Research Limited, P.O Box 31 310, Lower Hutt 5040, New Zealand ± Riddet Institute, Massey University, Palmerston North 4442, New Zealand ‡

S Supporting Information *

ABSTRACT: It is well accepted that our gut bacteria have coevolved with us in relation to our genetics, diet and lifestyle and are integrated metabolically with us to affect our gut health adversely or beneficially. “Who is there” may vary quite widely between individuals, as might “how they do it”, but “what they make” may be less variable. Many different individual species of bacteria can perform the same saccharolytic functions and so the availability of substrate (host or diet-derived) along with the degradative enzymes they possess may be key drivers of gut ecology. In this case study, we discuss detailed microbial ecology and metabolism analysis for three individuals following 48 h of in vitro faecal fermentation, using green kiwifruit as the substrate. In parallel, we have analyzed the chemical changes to the kiwifruit carbohydrates present in the fermenta to close the circle on substrate usage/degradative enzymes possessed/microbes present/microbial byproducts produced. In the absence of host carbohydrate, we see that kiwifruit carbohydrates were differentially utilized to drive microbial diversity, yet resulted in similar byproduct production. The starting ecology of each individual influenced the quantitative and qualitative microbial changes; but not necessarily the metabolic byproduct production. Thus, we propose that it is the consistent functional changes that are relevant for assessment of gut health benefits of any food. We recommend that in this era of large scale genotype/−omics studies that hypothesis-driven, bottom-up research is best placed to interpret metagenomic data in parallel with functional, phenotypic data. KEYWORDS: gut microbial ecology, kiwifruit, diversity, metabolism, health

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INTRODUCTION Microbes become established in the gut in a process-designated succession influenced by extrinsic factors such as the microbial load of the immediate environment, food and feeding habits, antibiotic therapy, and the composition of maternal microbiota.1 The environment is extremely diverse and molecular methods have now confirmed that more than 500 different species of bacteria make up the indigenous flora of the gut,2 although any given individual probably possesses fewer than 100 of these.3 It has been proposed that these can be divided into three enterotypes, phylogenetically characterized by the main contributors Bacteroides spp, Prevotella spp. or Ruminococcus spp. respectively. Most gut bacteria fall into one of four phyla: Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria. The first two enterotypes are comprised predominantly of members of the Bacteroidetes phyla, while the third is dominated by Firmicutes, with members of the other phyla merely cocontributing. Recently there has been much interest in relating various phyla ratios to health and disease, for example, an ongoing debate exists on whether a Firmicutes/ Bacteroidetes ratio is implicated in obesity and, by association, type 2 diabetes.4−7 A variety of other disease states have been linked to alterations in the normal gut microbiota, including © 2012 American Chemical Society

atopic eczema and asthma, rheumatoid arthritis and inflammatory bowel disease (IBD).6,8−10 Interestingly, the functional differences in the gut microbiota between lean, healthy individuals and obese or IBD sufferers can be characterized as less flexible or adaptable in the way they interact with external stimuli,11 including primary stage substrate utilization or endstage byproduct formation. A gut microbiota that is metabolically flexible and able to adapt to varied substrates and provide varied byproducts is regarded as advantageous. Greater diversity (of microbes) is also considered a good thing as it confers resilience in response to disturbance.12 A retrospective study of the impact of lifestyle on microbial diversity patterns found positive associations between a number of factors (including farm living, restricted antibiotic use and consumption of fermented vegetables) and diversity of microbes.13 This corresponds with the findings of Muegge and co-workers14 that the microbiota adapts to diet even across mammalian lineages. Here we attempt to use a dietary carbohydrate source to explore whether this can drive the microbiota of individuals toward greater diversity. We Received: July 16, 2012 Published: October 17, 2012 5863

dx.doi.org/10.1021/pr300646m | J. Proteome Res. 2012, 11, 5863−5875

Journal of Proteome Research

Article

Scheme 1. Workflow Depicting Study Process and Analysis Stepsa

a

Green kiwifruit were subjected to simulated digestion and a batch fermentation with faecal samples from each of three donors, sampled at three time points, yielding fermenta slurry which were further processed (unbordered entries) to result in varyingly treated extracts (boxes with heavy borders) for analyses (bold font).

different microbiota respond to and utilize this complex carbohydrate source. Collectively, these analyses straddle phylogenetic, ecological and quantitative metabolic approaches to illustrate how this combined approach might find use in further in vivo studies.

hypothesize that each individual’s ability to respond to this carbohydrate source will depend on the flexibility of their microbiota’s carbohydrate-degrading capability. Commonly, research in gut health is either comprised of studies of microbial ecology at the genomic level accompanied by metagenomic analyses of gene pathways and gene families, or relies upon quantitative changes in beneficial gut microbes and fermentation end products, notably increases in proportions of acetate, propionate and butyrate.15 Advances in metagenomic studies are starting to uncover some of the associations between gut health and microbial adaptation through the possession of hydrolytic enzymes by particular bacterial strains16 and the measurement of organic acid byproducts to inform specific health benefits. However, neither approach monitors the actual degradation of the dietary carbohydrate during this process, nor measures any change in the fermentative capability of the microbiota as it responds to this fermentable substrate. In this study we conduct a detailed analysis of the ability of the faecal microbiota from three healthy individuals to ferment carbohydrate from kiwifruit in an in vitro system, where we have deliberately minimized the potentially confounding effects of host carbohydrates and other host-driven pressures on the microbiota. We use green kiwifruit as it is commonly consumed as a whole fresh fruit, it is well characterized, possessing a range of soluble pectic, insoluble hemicellulosic and insoluble cellulosic polysaccharides, and any changes to the chemical structure and composition of these cell wall polysaccharides by the digestive process are already established.17 We accompany microbial population and metabolic byproduct data with glycosidase activity and detailed carbohydrate analyses of the remaining fermenta after incubation at three time points (Scheme 1), to assess how

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MATERIALS AND METHODS

In vitro digestion of substrates

Simulated gastric and ileal digestion of green kiwifruit obtained from ZESPRI Group Ltd., New Zealand was carried out at 37 °C, using previously published procedures.18 Briefly, 25 g of fruit was first incubated with pepsin (P7000, Sigma, USA) at a final concentration of 0.19% under acidic conditions (pH 2.5). The digesta was then incubated at pH 6.0 with pancreatin (final concentration 0.22%, P7545, Sigma, St. Louis, MO) and amyloglucosidase (final concentration 5.9 U/mL, Megazyme, Wicklow, Ireland). The “digesta” so prepared, was then transferred into dialysis bags (14 000 MWCO, Thermofisher Scientific, New Zealand) and dialyzed at 4 °C for 24 h, with five changes of distilled water and low speed stirring using a magnetic stirrer (Chiltern, Simi Valley, CA). Water controls (no kiwifruit) were included. Simple Sugar Analysis

The kiwifruit puree and in vitro digested fruit (digesta) were analyzed for reducing sugars. Aliquots (1.0 mL) of the digesta before and after dialysis were collected and analyzed using the dinitrosalicylic acid colorimetric assay.19 On the basis of the absorbance at 570 nm, glucose equivalents were calculated. 5864



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In vitro Fermentation of Substrates

underlined, the letter N denoting the 10-base barcode sequence and the remaining capital letters the template-specific HDA primers. The 10-base Multiplex Identifier (MID) barcoded regions used in the PCR corresponded to MIDs 20−28 of the Roche 454 FLX titanium MID sequences. Twenty-five μL of HotStarTaq master mix (Qiagen, Melbourne, Australia) was mixed with 1 μL template DNA and 100 nM of each primer (total reaction volume 50 μL). PCR conditions were as follows: Initial denaturation 95 °C for 15 min then thirty cycles of 1 min denaturation, 45 s 65 °C annealing, 1 min 72 °C extension. The PCR products were gel purified using the E-Gel SizeSelect 2% Agarose system (LifeTechnologies), pooled in equimolar quantities, and submitted for sequencing using the Roche 454 GS FLX Titanium platform (Macrogen Inc. Korea). Sequence data were sorted by MID sequence, processed for quality and BLAST aligned with reference to the SILVA rRNA database.

Growth media, substrates, distilled water and buffers required for the fermentation were left in the anaerobic chamber (Forma Scientific, Inc., model 1028, Marietta, OH) maintained at room temperature in an atmosphere of 5% CO2, 10% H2 and 85% N2 for at least 24 h before the experiment, and then at 37 °C for at least 1 h before initiating the fermentation. Collection and Preparation of Faecal Inoculums

Fresh faecal samples were obtained under Northern X Regional Ethics Committee approval (NTX/08/11/112) from three healthy volunteers with no a priori selection criteria beyond self-assessed good health and abstinence from antibiotics for 3 months (male aged 43, female aged 55 and male aged 50) and immediately blended to prepare a 25% (w/v) slurry in sterile phosphate buffered saline (PBS; 10 mM phosphate, 150 mM NaCl, pH 7.4). Sterile carbohydrate-free basal medium (CFBM) was prepared as a 5× concentrate, using proportions recommended previously.20 Reactions were set up in sterile polypropylene tubes to include green kiwifruit digesta with final concentrations equivalent to 25% (w/v) fresh weight of fruit, or water controls, with 1% (w/v) faecal slurry in 1× CFBM. These were incubated anaerobically at 37 °C with shaking at 70 rpm. Aliquots of the fermenta were collected at 0, 24, and 48 h (Scheme 1), and stored at −80 °C for further analyses.

Colorimetric Sugar Analyses

Total carbohydrate was measured colorimetrically by the phenol-sulfuric acid method23 using glucose (0−80 μg) as the standard. Total uronic acid was measured colorimetrically by the m-hydroxydiphenyl method,24 after first hydrolyzing the samples in sulfuric acid,25 using galacturonic acid (0−12.5 μg) as the standard. Sugar Analysis of Soluble and Insoluble Fiber Fractions

The culture media used to grow bacterial strains were purchased from Oxoid (Adelaide, Australia). Bacteroides f ragilis NZRM 964 (Institute of Environmental Science & Research Limited (ESR), Wellington, New Zealand) and Enterococcus faecalis AGR 991 (AgResearch Grasslands, Palmerston North, New Zealand) were grown in brain heart infusion broth (BHI); Lactobacillus reuteri DPC 16 (Bioactives Research Ltd., Auckland, New Zealand) in de Man-Rogosa-Sharpe (MRS) broth; and Bif idobacterium adolescentis ATCC 15703 (ESR) in MRS broth supplemented with 0.05% cysteine (Sigma-Aldrich, Sydney, Australia). All bacterial strains were grown anaerobically for 24 h at 37 °C in gas jars using a GasPak system (Oxoid). To prepare standard curves, freshly grown bacterial strains representative of each group were resuspended in lysozyme solution (180 μL, 20 mg/mL in water) and incubated for 18 h at 37 °C. Subsequently, genomic DNA from bacterial isolates was extracted by following the QIAamp DNA stool mini kit protocol (Qiagen, Melbourne, Australia) for isolation of genomic DNA from Gram positive bacteria. The real-time PCR quantification was carried out using a LightCycler 480 instrument (Roche Diagnostics, Mannheim, Germany) in duplicate, as previously described.21 Different primers and annealing temperatures were used as required for total bacteria,22 Bacteroides-Prevotella-Porphyromonas (order Bacteroidales), Bif idobacterium spp., Enterococcus spp. and Lactobacillus spp.21

Samples of fermenta (10 mL) were thawed, transferred to 50 mL Falcon tubes, centrifuged (3500× g, 10 °C, 25 min) and the supernatants removed. The pellets were washed with ethanol (80% v/v), centrifuged and freeze-dried to give insoluble fiber fractions. Ethanol (4 volumes 96% v/v) was added to each of the supernatants, mixed, covered and allowed to stand overnight at 4 °C. The samples were centrifuged to pellet the ethanol-insoluble material. The pellets were redissolved in distilled water and freeze-dried to give soluble fiber fractions. The neutral constituent sugar compositions of the fiber fractions were determined by gas chromatography−mass spectrometry (GC−MS) of alditol acetate derivatives after hydrolysis of the polysaccharides present to their component monosaccharides. Each sample was analyzed in duplicate. The soluble samples were hydrolyzed with trifluoracetic acid (TFA) and resulting monosaccharides from the pectic polysaccharide hydrolysates (Scheme 1) analyzed as alditol acetates by GC−MS as described by Carnachan and coworkers.17 The insoluble fiber fractions were analyzed after hydrolysis of the polysaccharides present to their component monosaccharides, using a two-step hydrolysis procedure. In the first step, noncellulosic polysaccharides were hydrolyzed with TFA to result in TFA-soluble hemicellulosic polysaccharide products (Scheme 1), and in the second step, a two-stage sulfuric acid hydrolysis was used to hydrolyze remaining polysaccharides, mostly cellulose26 (Scheme 1), as described previously.17 These were measured by GC−MS as described above.

Pyrosequencing of Microbiota

Microbial Glycosidase Assays

DNA prepared by the methods outlined above was used as a template to amplify variable regions V2−V3 of the 16S rRNA gene (position 336−535 in the Escherichia coli rRNA gene) using primers HDA-1 (cgtatcgcctccctcgcgccatcagACTCCTACGGGAGGCAGCAGT) and HDA-2 (ctatgcgccttgccagcccgctcagNNNNNNNNNNGTATTACCGCGGCTGCTGGCAC), where the sequences of the forward and reverse primers are shown in lower case, the four-base library “key” sequence is

The potential ability of the collective faecal microbiota of the donors to adjust to and catabolise complex carbohydrates to monosaccharides was examined through the use of model substrates. These substrates were selected from the NCBI database panel of (exo)-glycosidases possessed by Bacteroides thetaiotaomicrometer VPI-5482 (http://www.ncbi.nlm.nih.gov/ bioproject/399),27 a highly metabolically flexible carbohydratedegrading gut anaerobe. These substrates model some host-

Real-time PCR Quantification of Microbiota

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Figure 1. Microbial composition of all three donors at all time points as determined by 16S rRNA pyrosequencing. The four most abundant phyla are shown: Bacteroidetes (red), Firmicutes (green), Actinobacteria (blue) and Proteobacteria (orange). Taxa at the family level are depicted by different shades of the phylum color as shown in the legend. Only families and phyla of greater than 1% total read composition were included in this graph.

standard curve. Data were expressed as Units/mL fermenta, where 1 Unit equals 1 nmol/min.

derived glycosidic linkages and plant cell wall linkages typical of the endogenous and dietary polysaccharides which the gut microbiota have the ability to degrade and would be exposed to in vivo.28,29 All reagents were purchased from Sigma-Aldrich, St Louis, MO, unless otherwise specified. Glycosidase model substrates were all para-nitrophenyl-1-linked sugars as follows: α-galactopyranoside, β-galactopyranoside, α-fucopyranoside, αglucopyranoside, β-glucopyranoside, α-arabinofuranoside, αmannopyranoside, α-rhamnopyranoside, β-xylopyranoside and β-galacturonide, all corresponding to kiwifruit glycosidic linkages;17 and α-N-acetyl-galactosaminide, β-N-acetyl-glucosaminide, and β-glucuronide corresponding to the outstanding common host glycosidic linkages from mucin and, with the glucuronide, connective tissue (sulfatase and sialidases activities were not assessed); and α-arabinopyranoside to capture a class of ginsenosidases, representing glycosidase activity associated by virtue of their constitutive expression30,31 with changes in abundance of certain bacteria. To ensure coverage of the variety of carbohydrate-degrading strategies employed by bacteria such as the extremes exemplified by the extracellular cellulosomal model and the cell-associated sequestration model,32 whole fermenta slurry, incorporating both extracellular material and whole intact cells, was used. This fermenta slurry (5 μL) was added to each of the 2.5 mM para-nitrophenyl-1-glycoside substrate stocks prepared in 25 mM Na succinate buffer pH 6.5, giving a final concentration of 0.625 mM substrate and immediately incubated at 37 °C for 150−210 min. Each substrate-fermenta combination was incubated in triplicate in 20-μL volumes in the wells of 384-well microplates (Greiner Bio-One, Germany). The reactions were terminated and para-nitrophenol yellow color developed by the addition of 0.5 M glycine buffer pH 9.6 (50 μL). The free para-nitrophenol, liberated from the sugars on a 1:1 molar ratio, was measured by absorbance at 405 nm using a SPECTRAmax plus microplate reader (Molecular Devices Pty Ltd., Surrey Hills, VIC). Sample blank values were subtracted, and the mean nmol of product calculated by averaging replicates and comparing with a para-nitrophenol

Cellulosidase Assay

The ability of faecal microbial enzymes to degrade cellulose to glucose monomers were assessed by incubation of the fermenta with cellulose, followed by detection of free D-glucose in a subsequent enzyme assay. Cellulose (Sigma-Aldrich C-6288) suspensions (2% (w/v)) in 25 mM Na succinate buffer pH 6.0 (45.0 uL) were dispensed into wells on an optically clear 96well microplate (Costar 3596, Corning, NY), to which was added fermenta (5.0 μL) in triplicate, and incubated at 37 °C with shaking at 300 rpm for 1 h. Reaction slurry (40 uL) was then transferred to U-well 96-well plates (Falcon 353077, BD Labware, Franklin Lakes, NJ) and centrifuged at 200× g for 5 min at 4 °C to remove the insoluble, undigested cellulose. Zero time controls were prepared by mixing the cellulose suspension with fermenta immediately prior to centrifugation. Supernatants (30 uL) were collected in an optically clear 96-well microplate, to which was added glucose oxidase-peroxidasesubstrate mixture (K-GLUC 03/11, Megazyme International Ireland Ltd., Co. Wicklow, Ireland) and incubated at 50 °C for 10 min. A D-glucose standard curve was included on the same plate. The resulting colored solution was immediately measured for absorbance at 510 nm, and glucose concentration calculated from the standard curve. Zero-time control values were subtracted from experimental values, to determine the amount of glucose liberated from cellulose. Data were expressed as μg glucose liberated/min. Analysis of Organic Acids

The organic acid metabolites of microbial metabolism; lactate, formate, succinate acetate, propionate, butyrate and isobutyrate, were measured by gas chromatography with flame ionization detection (GC-FID), an established method33 with slight modifications.21 Calibration standard mixtures also containing 5 mM 2-ethyl butyrate were extracted and derivatized alongside the samples. Analysis was performed on a Shimadzu gas chromatograph 5866



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Table 1. Microbial Quantification using qPCR, Expressed as log10 CFU/mL Fermenta, with Control Values (No Green Kiwifruit Carbohydrate) Shown in Parentheses and Shannon Diversity Indices Calculated from the Pyrosequencing Data donor 1 Bacteroidales Bifidobacteria Enterococcus Lactobacillus Total bacteria Shannon diversity index

donor 2

donor 3

0h

24 h

48 h

0h

24 h

48 h

0h

24 h

48 h

7.27 (6.91) 4.92 (4.98) 4.49 (4.46) 5.32 (5.08) 7.37 (7.07) 0.485

6.95 (7.21) 5.99 (5.51) 4.40 (4.37) 5.34 (5.02) 7.47 (7.44) 1.051

6.92 (6.71) 6.10 (5.18) 4.44 (4.35) 5.39 (4.69) 7.42 (7.05) 1.087

7.41 (6.46) 4.88 (4.19) 4.59 (4.45) 5.52 (4.96) 7.71 (7.72) 0.943

7.11 (7.17) 6.51 (5.07) 4.45 4.34) 5.24 (4.99) 7.67 (7.53) 0.866

7.14 (6.92) 6.60 (4.80) 4.23 (4.15) 5.2 (4.69) 7.56 (7.31) 0.922

6.89 (6.96) 3.28 (3.48) 4.41 (4.32) 5.47 (5.54) 7.65 (7.28) 0.593

7.50 (6.95) 3.64 (3.38) 4.58 (4.21) 5.67 (4.81) 8.12 (7.30) 0.906

7.44 (6.62) 4.71 (3.42) 4.20 (4.09) 5.31 (4.47) 7.81 (6.99) 0.731

Table 2. Constituent Sugar Composition of the Soluble Pectic Polysaccharide Fraction of in vitro Fermented Green Kiwifruit, Expressed as Mean (n = 2) % of Dry Weight donor 1 b

Rhamnose Fucose Arabinose Xylose Mannose Galactose Glucose Uronic acidc Total sugard

donor 2

donor 3

no donor

0h

24 h

48 h

0h

24 h

48 h

0h

24 h

48 h

0 ha

0.62 0.07 0.54 0.14 2.63 1.44 0.72 10.57 14.10

0.60 0.11 0.20 0.06 1.75 0.61 0.40 3.86 5.84

0.57 0.33 0.24 0.00 0.88 1.45 0.60 2.10 5.29

0.58 0.09 0.55 0.10 2.47 1.45 0.61 11.47 14.85

1.06 0.12 0.27 0.19 1.52 0.63 0.34 6.29 8.90

0.64 0.30 0.31 0.00 0.89 1.46 0.58 2.40 5.69

0.53 0.09 0.36 0.08 2.61 0.96 0.61 5.31 7.94

0.25 0.00 0.03 0.00 0.49 0.24 0.36 0.73 1.61

0.22 0.06 0.00 0.00 0.29 0.29 0.40 0.64 1.61

0.38 0.11 0.70 0.26 2.37 1.65 0.75 11.50 17.72

a

Did not appreciably change over period of incubation (not shown). bNeutral sugars measured by GC−MS as described. cUronic acid measured colorimetrically. dSugars and uronic acid measured as in footnotes a and b. Data does not include mannose that is present in the fermentation media.

in numbers and/or proportion of Firmicutes. Donor 1 exhibited a qPCR-determined increase in microbial numbers, which was not observed with Donor 2. In the absence of kiwifruit, both donors’ microbiota declined over time, consistent with a depletion of fermentable substrate in this closed system. Prior to incubation, donor 3 was characterized by an extremely high (>80% sequence abundance) proportion of Enterobacteriaceae. In the presence of kiwifruit, this abundance decreased at 24 h but then increased to 50% of sequence abundance by 48 h. Bacteroidetes increased over time in both sequence abundance and qPCR. There was a small increase in Actinobacteria by 48 h measured by qPCR, although insufficient to feature at this depth of sequence data. Total bacteria peaked at 24 h. The increase in diversity and numbers at 24 h corresponds to a bloom in members of the Lachnospiraceae family. In the absence of kiwifruit, qPCR revealed that Bacteroidales and total bacteria maintained their numbers at 24 h, but declined by 48 h, while all other bacteria declined from 0 h.

(GC-17A) equipped with a Shimadzu AOC 20i autoinjector and AOC20s autosampler, a flame ionization detector (FID), and an Agilent HP-1 (methyl silicone gum) column (10 m length × 0.53 mm internal diameter × 2.65 μm film thickness). The carrier gas was helium with a total flow rate of 37 mL/min and pressure of 7 kPa. The temperature program began at 70 °C, increasing to 80 °C at 10 °C/min, with a final increase to 255 °C at 20 °C/min, holding for 5 min. The pressure program was set to 7 kPa, increasing to 15 at 0.8 kPa/min, holding for 4 min. Injector and detector temperatures were set at 260 °C. Samples (1 μL) were injected in splitless mode. The instrument was controlled and chromatograms acquired using GC Solutions software (Shimadzu). The acquired GC data from the calibration standard mixtures were used to plot standard curves, so that the sample organic acids could be calculated and expressed as μmol/mL fermenta.

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RESULTS

Bacterial Quantification and Pyrosequencing

Carbohydrate Analysis

Pyrosequencing data describe the changing ecology of the three donors’ faecal microbiota in response to incubation with green kiwifruit carbohydrate using a phylogenetic approach (summarized in Figure 1). These are supported with qPCR data comparing selected members of the microbiota incubated in the presence and absence of green kiwifruit, and their diversity assessed by calculating Shannon diversity indices34,35 for each donor at each time-point (Table 1). All three donors showed changes in the composition and diversity of their microbiota over time. In the presence of green kiwifruit, Donors 1 and 2 both showed a decrease in Bacteroidetes and an increase in Actinobacteria by both sequence abundance and qPCR. Major changes were evident

Carbohydrates remaining after fermentation were quantified according to the workflow outlined in Scheme 1. This meant there were three main carbohydrate fractions separated on the basis of solubility: pectic, hemicellulosic, and cellulosic polysaccharides. These fractions tend to be differentially susceptible to microbial degradation, consistent with their different solubilities and likely degradative strategies required by the microbiota. A. Simple Sugar Analysis of Digesta before Fermentation. The simple sugar content of the undigested green kiwifruit puree, determined by the dinitrosalicylic acid colorimetric assay, was 11.82 g/100 g. After simulated gastric 5867



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Table 3. Constituent Sugar Composition of the Insoluble Hemicellulose Fraction of in vitro Fermented Green Kiwifruit, Expressed as Mean (n = 2) % of Dry Weight donor 1 b

Rhamnose Fucose Arabinose Ribose Xylose Mannose Galactose Glucose Total sugarb a

donor 2

donor 3

no donor

0h

24 h

48 h

0h

24 h

48 h

0h

24 h

48 h

0 ha

0.39 0.15 0.37 0.00 4.15 0.75 2.45 4.91 13.17

0.44 0.13 0.18 0.17 4.34 0.95 2.03 1.69 9.93

0.39 0.09 0.15 0.07 3.78 0.78 1.66 1.26 8.18

0.32 0.13 0.29 0.00 3.10 0.64 2.05 8.20 14.73

0.57 0.13 0.17 0.19 3.15 0.83 2.05 2.21 9.30

0.21 0.09 0.12 0.06 5.20 0.63 1.78 1.32 9.41

0.42 0.08 0.33 0.00 2.54 0.53 1.58 15.02 20.50

0.61 0.04 0.15 0.27 1.41 0.54 1.22 3.99 8.23

0.46 0.00 0.21 0.20 1.15 0.32 1.05 4.75 8.14

0.47 0.16 0.50 0.00 4.69 0.80 4.15 7.52 18.29

Did not appreciably change over period of incubation (not shown). bNeutral sugars measured by GC−MS as described.

Table 4. Constituent Sugar Composition of the Insoluble Cellulosic Fraction of in vitro Fermented Green Kiwifruit, Expressed as Mean (n = 2) % of Dry Weight donor 1 a

Xylose Mannose Glucose Total sugara a

donor 2

donor 3

no donor

0h

24 h

48 h

0h

24 h

48 h

0h

24 h

48 h

0h

0.93 1.55 47.37 49.85

0.94 1.70 55.29 57.93

0.78 1.65 48.57 51.00

0.59 1.17 37.76 39.52

0.56 1.31 39.57 41.44

0.86 1.83 68.87 71.56

0.34 0.72 24.50 25.56

0.26 0.62 17.51 18.39

0.20 0.54 14.15 14.89

0.80 1.52 54.28 56.60

Neutral sugars measured by GC-MS as described.

other hand, the rate of glucose disappearance was considerably lower than that of sugars derived from pectic polysaccharides. Comparing the ability of the faecal microbiota from the three donors to liberate and utilize the sugars from these soluble fiber fractions, donor 3 utilized 80% of the total sugars (excluding mannose), present at 0 h by 48 h, while donors 1 and 2 utilized about 60% of the total sugars. Donor 1 showed a more rapid utilization of the total sugars than donor 2; donor 1 had consumed 61% of the sugars at 24 h, while donor 2 consumed only 39% sugars of the sugars at 24 h, increasing to 62% at 48 h. C. Hemicellulosic and Cellulosic Polysaccharides. Analysis of the insoluble fiber fractions using the two-step procedure gives the neutral sugar compositions of the hemicellulosic insoluble polymers, obtained by TFA hydrolysis (Table 3), and the cellulosic insoluble polymers, as shown by the presence of almost exclusively glucose in the H2SO4 hydrolysates (Table 4). In terms of total % dry weight sugars liberated and utilized from this carbohydrate fraction, on balance the microbiota from donor 3 used slightly more of the hemicelluloses than the other two donors (Table 3). The utilization of cellulosic glucose differed greatly among the three faecal donors (Table 4). Overall, there was little change in the glucose content during fermentation by faecal inocula from donors 1 and 2. For donor 3, however, there was a consistent utilization of this fraction, liberating as much as 10.7% of the dry weight of this fraction, predominantly as glucose, much more than from either of the other fractions (4% and 2% for pectic and hemicellulosic fractions, respectively), or the other donors (which effectively showed net losses from this fraction).

and ileal digestion and dialysis, no simple sugars were detected in the retentate. Thus, dialysis simulated the passive absorption of the sugars along the intestinal lining, and only those carbohydrates that escaped host digestion were retained for in vitro fermentation, analogous to the delivery of these carbohydrates to the large bowel microbiota. B. Pectic Polysaccharides. Colorimetric analysis showed that uronic acid was the predominant sugar present in the soluble fiber fractions of in vitro-digested green kiwifruit prior to fermentation (Table 2). Previous analysis of a similar fraction from in vitro-digested kiwifruit showed that the uronic acid was entirely galacturonic acid.17 Analysis of the neutral sugars in the soluble fiber fractions showed the presence of rhamnose, arabinose and galactose, which are typical of pectic polysaccharides, and fucose, xylose and glucose, which are usually components of hemicellulosic polysaccharides (Table 2). However, the major neutral sugar detected was mannose: which has previously been detected in only small amounts in fractions from in vitro-digested kiwifruit.17 The CFBM used for the fermentation experiments contained 5 g/L yeast extract, typically possessing about 10% (w/w) mannan. Analysis of fermenta samples using faecal inocula from donor 1, in which no kiwifruit digesta was added to the medium, showed the presence of mannose that was rapidly fermented and had almost disappeared after 24 h (data not shown). From these results it was concluded that mannose present in the soluble fiber fractions was derived primarily from the yeast extract that was part of the growth medium and not from the kiwifruit polysaccharides. Changes in sugar composition during fermentation were consistent with degradation and utilization of pectic polysaccharides, the principal polysaccharide components of the water-soluble fiber fraction of in vitro-digested green kiwifruit.17 Pectic polysaccharides were extensively fermented by bacteria present in the faecal inoculum from all three donors. On the

Integrating Carbohydrate Data from the Three Fractions

The relative abilities of the faecal microbiota from each of the donors to degrade kiwifruit carbohydrates differently were graphically summarized using Principal Component Analysis (PCA). PCA were performed (GenStat Release 11.1, VSN 5868



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Figure 2. Principal component analyses (PCA) summary of metabolic parameters from faecal microbiota from donors 1, blue ◆; 2, red ■; and 3, green ▲, after fermentation for 0, 24, and 48 h with digested green kiwifruit material. Dimension one, x axis; dimension two, y axis; axes cross at 0,0. (A) PCA plot illustrating carbohydrate removal. Vector loadings shown for sugars Ara, arabinofuranose; Fuc, fucose; Gal, galactose; Glc, glucose; Man, mannose; Rha, rhamnose; Rib, ribose; and Xyl, xylose, with purple entries prefixed by P denoting pectic neutral sugars; orange HC prefix denoting hemicellulosic neutral sugars; Black C prefix denoting cellulosic neutral sugars; and uronic acids in bold font. Relative size of colored circular overlays at each donor-time point to compare sum of all sugars (as % dry weight remaining subtracted from no-donor blank) removed by microbiota from that donor at that point. (B) PCA plot of glycosidase activities from the three donors corresponding to neutral sugars and uronic acid in (A), (inset: cellulosidase activities from the three donors, data expressed as mean (n = 3) μg/min, I bar shows LSD). PCA plot shows vector loadings from enzymes a_Glc, α-glucosidase; b_Glc, β-glucosidase; a_Gal, α-galactosidase; b_Gal, β-galactosidase; a_GlcNAc; α-N-acetylgalactosaminidase; b_GlcNAc, β-N-acetyl-glucosaminidase; a_Rha, α-rhamnosidase; a_Fuc, α-fucosidase; a_Araf, α-arabinofuranosidase; a_Arap, αarabinopuranosidase; a_Man, α-mannosidase; b_xyl, β-xylosidase; b_GlcU, β-glucuronidase; β-GalU, β-galacturonidase. (C) PCA plot of organic acids produced by the microbiota from the three donors, with vector loadings shown for formate, acetate, propionate, butyrate, iso-butyrate, lactate and succinate.

International Ltd., UK) using the combinations of neutral sugars released from fiber and time as groups. The samples were positioned (Figure 2A) according to the first two principal components, where principal component 1 (x axis) accounted for 47% of the variance between donor samples and principal component 2 (y axis) accounted for 32% of the variance. The liberated neutral sugars and uronic acid are plotted using the correlations with principal component (PC) 1 and PC 2. The first dimension revealed carbohydrate degradation over time, as more sugars and uronic acids were released, predominantly from the pectic and hemicellulosic polysaccharide fractions. In order of carbohydrate utilization, as represented by rightwards movement across the x axis, were donor 3, then donor 1, followed by donor 2. Donor 3 showed the most pronounced utilization over the first 24 h; while donor 2 had the greatest increase in utilization over the last 24 h period. The second discriminating dimension was driven by the hemicellulosic and cellulosic sugars, and illustrated the most substantial difference between donor 3 and donors 1 and 2: the ability to utilize insoluble carbohydrates. These differences also appeared to reflect the relative total sugar usage by the respective donor microbiota (circles, and

Tables 2−4); and to reflect the trends evidenced by the glycosidase activities measured for each donor (Figure 2B). Microbial Glycosidase and Cellulosidase Activities

Data for glycosidase activities against substrates most closely modeling the sugars and linkages found in the green kiwifruit were graphically summarized using PCA (Figure 2B). The complete list of activities is presented in Supporting Information Table S1. The ability to liberate glucose from cellulose (cellulosidase activity), was measured by glucose oxidase-peroxidase-linked assay (Figure 2B inset). Note that this activity is likely to represent the activity of a combination of enzymes, first liberating oligosaccharides, and subsequently releasing free Dglucose, in a cooperative manner. Here it is shown that only low cellulosidase activities were observed from the fermenta, with donor 3 possessing the highest activity, apparent after 48 h, and consistent with the sugar analyses (Figure 2A). The other glycosidase activities were generally characterized by a peak at 24 h, followed by a decline at 48 h, the latter graphically represented as convergence at coordinates 0,0 (Figure 2B). This is consistent with increased expression and/ or abundance of microbial consortia able to catalyze the removal of the para-nitrophenol group from the sugar residue, 5869



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sugar release, which may be attributed to the limits of a model colorimetric substrate’s ability to mimic larger substrates (where endoglycosidase activities would be required, but cannot be measured by this system). Similarly, some substrates may not possess absolute specificity (arabinoses;30,31 fucoses41), so our interpretations should be regarded as tentative.

followed by a decline consistent with the decline in bacteria (Table 1). Interestingly, in the absence of kiwifruit (Supporting Information Table S1) glycosidase activities remained relatively low, as might be expected with declining populations of bacteria (Table 1) and in the absence of an exogenous substrate. Aside from galactosidase activities, donor 3 typically possessed the highest starting (0 h) activities, and possessed the highest activities across the range of substrates. In the absence of kiwifruit, these activities tended to be comparable with those of the other donors, suggesting that this donor’s microbiota was particularly responsive to fermentable carbohydrate. Collectively, these glycosidase and cellulosidase data generally correspond with the microbial population data and sugar analyses: the greatest increase in microbial numbers and both sugar utilization and glycosidase activities occurred over the first 24 h period, with donor 3 possessing the greatest apparent appetite for substrate utilization and growth, and donor 2 the least. The ability of all the donors to utilize noncellulosic sugars appeared more or less equal by 24 h in this closed system. The relationship between sugars and glycosidase activities is shown by PCA in Supporting Information Figure S1. Certainly, the concept of the microbiota altering its substratespecific activities in a substrate-limited batch fermentation has been serendipitously illustrated by the inclusion of yeast mannan in the growth media giving relatively high mannose concentrations in the water-soluble fiber fractions; the rapid decline in mannose over the course of the fermentation was mirrored by a similarly rapidly decline in mannosidase activity. Interestingly, those activities associated with host-derived carbohydrates (Supporting Information Table S1) (N-acetylglucosamine, N-acetyl-galactosamine, and glucuronic acid, commonly of mucin/cell surface and connective tissue origin, respectively) were also seen to change over time. This was notably apparent for donors 1 and 2 in the absence of kiwifruit. It is possible this is a consequence of “host” sugars present on glycosylated porcine enzymes used during the simulated digestion process, as we have unpublished data suggesting these are sufficient to support the growth of both Lactobacillus and Bif idobacteria spp. in the absence of other fermentable carbohydrates. Similarly, trace mucin oligosaccharides may have been present in the faecal inocula. It is known that some metabolically flexible species of Bacteroides will switch toward a more host-derived substrate when the alternative is head-tohead competition with other members for limited dietary resources,36 and this is consistent with the increased Bacteroides spp. abundance observed with donors 1 and 2 in the absence of kiwifruit. Alternatively, some Ruminococcus and Bif idobacteria species possess constitutive enzymes specific for host glycan degradation,37−40 so increases in these activities would also correspond to increases in the abundance of these members of the microbiota with donors 1 and 2. This is similarly illustrated by the inclusion of a para-nitrophenyl-arabinopyranose substrate, modeling ginsenosides from the ginseng (Panax spp.) family, which are degraded by constitutively expressed arabinopyranoses.30,31 Arabinopyranose is not found in kiwifruit, explaining why increases in this activity probably represents only the relative increases in the abundance of members of the microbiota (such as bifidobacterial species) possessing these glycosidases.31 However, we caution that some substrates (para-nitrophenylgalactose, glucose, xylose) do not necessarily correlate with

Organic Acid Production

Organic acid concentrations produced by the faecal microbiota over time in response to incubation in the presence and absence of kiwifruit were measured by GC-FID (Supporting Information Table S2) and graphically summarized using PCA (Figure 2C). This measured organic acid production and a predicted organic acid potential of the faecal microbiota in the presence of kiwifruit are compared in Figure 3. The left-hand column of

Figure 3. Summary of organic acid production from donors 1, 2, and 3 after fermentation for 0, 24, and 48 h with digested green kiwifruit material. The left-hand column shows the observed production of organic acid as measured by GC-FID. The right-hand column shows the predicted organic acid production calculated using the proportion of bacteria (determined by pyrosequencing) that are known producers of a particular organic acid. The innermost ring of the doughnut charts are 0 h, the central ring is at 24 h and the outer ring at 48 h. The various organic acids are indicated by the following colors: acetate (blue), propionate (purple), formate (red), butyrate (green), succinate (teal) and lactate (orange).

the panel shows the observed proportions of acids as measured by GC-FID at 0, 24, and 48 h for all three donors. This graphically illustrates the substantial increase in acetate formation that was observed for all three donors over the 48 h fermentation. The modest but observable increase in butyrate and propionate production over time for all three donors was also evident. The other organic acids, formate, succinate and lactate, are demonstrated to display net decreases in concentration over the course of the fermentation. 5870



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To understand if any correlation was evident between the types of bacteria present and the types of organic acid production observed, we performed an additional analysis, shown in the right-hand column of Figure 3. These doughnut charts show the “organic acid potential” of the bacteria, estimated using pyrosequencing data. The abilities of different genera to produce a certain organic acid through fermentation were determined by previous results reported in the literature.42−51 Genera present at >1% level were arbitrarily assigned a value of 1 for each acid they were reported to produce. Several of the genera we detected had limited information pertaining to fermentation potential, but a general idea of their metabolite production was obtained. The relative abundance of each genus in each donor’s microbiota determined the relative predicted organic acid potential. We could not take into account the mutualistic cross-feeding potential of these metabolites. Donors 1 and 3 demonstrated an increase in the range of different organic acid producers over time. Donor 1 showed an increase in acetate, propionate and butyrate over time, with peaks in acetate and propionate production at 24 h and butyrate at 48 h. These relative proportions were similar to the predictions based on pyrosequencing data, especially the peak propionate and butyrate production. Those acids that are intermediates in the food web, such as succinate, lactate and acetate, were not predicted accurately, as would be expected. Donor 2 showed increases in all acids except formate and lactate. Succinate was not observed. These measured increases were also predicted except for propionate. As with Donor 1, lactate and succinate were overpredicted. Donor 3 had comparable organic acid concentrations to the other two donors and their changes over time were similar. Our predictions were driven by the sequence data, where in the absence of acid production by Proteobacteria, the organic acid producers were dominated by Firmicutes at 24 h and Bacteroidetes at 48 h, corresponding to predicted increases in lactate and formate at 24 h and propionate and succinate at 48 h. The gap between predicted and observed organic acids fits with results from the previous donors, where intermediate acids were overpredicted. The higher than predicted butyrate concentrations at 24 and 48 h are possibly an enduring consequence of the Lachnospiraceae bloom observed at 24 h.

Figure 4. Integration of carbohydrate utilization, microbial glycosidase activities and organic acid correlations from the faecal fermentation data over time. The graphs were derived from correlation analyses between these data from all three donors from incubation periods 24 h (A) and 48 h (B). Nodes represent uronic acids or sugars (■) (Rha, rhamnose; Fuc, fucose; Ara, arabinose; Rib, ribose; Xyl, xylose; Man, mannose; Gal, galactose; Glc, glucose, with the prefix P, HC or C designating pectic, hemicellulosic or cellulosic fraction, respectively); log-transformed organic acid concentrations (◆) (formate, acetate, propionate, butyrate, iso-butyrate, lactate and succinate) and glycosidase activities (●) connected by edges color coded red/blue for positive and negative correlations, respectively. Significance of correlation depicted by bold (P < 0.05) or dashed (P < 0.2) edges.

between these two nodes. Note that these correlations are associative, but not necessarily causative. On the graph, the edges were then color coded red/blue to represent the sign of the correlation (positive or negative, respectively). Significance was depicted as uninterrupted (straight) or interrupted (dashed) lines for P < 0.05 or 0.2, respectively. Here it is shown that all displays of this in vitro system were highly complex, with relatively fewer significant (P < 0.05) correlations at 24 h (Figure 4A), compared to many at 48 h (Figure 4B). Given the low number of donors (n = 3) it is unsurprising that so many values correlated, and that the most significant correlations could not be tied to related biochemical species and pathways (for example, the expected relationship between succinate, a precursor of propionate in Bacteroidetes metabolism, was not observed). Nevertheless, the number of interactions between nodes indicated high functional complexity, consistent with an adaptable microbiota.11

Integration of Carbohydrate, Organic Acid and Glycosidase Activity Profiles

To explore how fermentation of the kiwifruit carbohydrates affected the in vitro relationships between the faecal bacterial ability to break glycosidic linkages, and their metabolic byproduct production, correlations were derived from organic acid, glycosidase activities and carbohydrate data after fermentation for 24 and 48 h, and displayed using tripartite plots (Figure 4) generated using Cytoscape release 2.8.3 (www. cytoscape.org) to represent the correlational matrices. Correlation coefficients were calculated using the GenStat Correlations function. Significance values for interactions between variables were also obtained using the GenStat Correlations function, and two cutoffs, of P < 0.05 and P < 0.2 used to shortlist interactions which would be plotted. The tripartite plot represents only the correlations between the three types of nodes (organic acids, glycosidase activities and carbohydrates). In that context, the presence of edges, the lines connecting the nodes, represents a functional correlation

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DISCUSSION Microbes require simple sugars as an energy source, which are obtained through enzymatic degradation of dietary (usually plant) or host carbohydrates. Different microbial species liberate simple sugars to a greater or lesser extent, depending on the enzymes they possess (glycosidases, glucuronidases, sulfatases etc.) and the strategies by which they employ them. This process is complex and often involves cooperative metabolism by several species, where the products of one organism are used as nutrients by other “secondary feeders”.32,52 Most bacteria possess multiple enzymes with a broad spectrum of glycosidase activity against plant polysaccharides. However, only a few are cellulolytic and able to break down the most complex insoluble plant material. These 5871



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faecal microbiota was rich in Ruminococcaceae but poor in Bacteroides spp., typical of enterotype 3. The lower amounts of catabolically flexible Bacteroides spp. may explain the comparatively lower glycosidase potential in this donor. At the end of 48 h, donor 2 still retained the characteristics of enterotype 3. The donor 3 profile was difficult to classify because of the high relative abundance of Enterobacteria spp. This dominance by Proteobacteria could be taken as suggestive of intestinal dysbiosis, and possibly a preclinical disposition to inflammation.55 The successive time samples showed a restoration to greater Bacteroidetes and Firmicutes, suggesting that a diet including fermentable fiber may contribute to correcting dysbiosis. We find the concept of categorizing the microbiota into enterotypes, while not strictly necessary to interpret the data, does form a convenient handle by which to grasp the complexities evident even in this intentionally simplified in vitro case study. Here we report a system where the effect of host carbohydrates was deliberately minimized. Yet the hallmarks of enterotype classification are the functional roles attributed to the dominant enterotype members,54 and these roles are largely biased toward host carbohydrate utilization. For example, a significant body of literature33,56−60 suggests the Bacteroides-rich enterotype 1 is likely to be most adapted for and exhibit a preference for host (mucin) glycans. Similarly, the Prevotella-rich enterotype 2, also possessing Desulfovibrio, (both known sulfur utilizers, Prevotella in particular probably possessing mucin-desulfating sulfatase activity,61 known to be important for establishment/persistence in the gut,60 and Ruminococcaceae, which may possess the ability to degrade mucin,40 appear predisposed toward a host-glycan diet. Moreover, enterotype 3 is dominated by Ruminococcus spp., associated with other Ruminococcaceae, also biased toward host glycans, although on the basis of their cellulosomal glycosidase deployment strategies, Ruminococcaceae might suggest a predisposition toward insoluble cellulosic polysaccharides too. If donors 1 and 2 were members of enterotypes 1 and 3, respectively, then this may explain the ecological directional shifts undergone by these microbiota during the course of incubation in in vitro batch culture: they may be suboptimally configured for this lengthy absence of host glycans, and since Bacteroides tend to avoid head-to-head competition for resources (in favor of host glycans),36 the observed decline in Bacteroides is consistent with failing to compete for a niche against the plant carbohydrate-utilizing members of the Firmicutes phyla. The Enterobacteria in donor 3 appears to have adopted a state of high metabolic versatility similar to that adopted by many enteric pathotypes,62 presumably as compensation for the relatively decreased metabolic activity normally expected to be fulfilled by core gut phyla such as Bacteroidetes and Firmicutes. The successive time samples showed a restoration to greater Bacteroidetes, Firmicutes and, to a lesser extent, Actinobacteria. These were accompanied by 2-fold increases in α-Nacetylgalactosaminidase and α-fucosidase activities, consistent with increasing Bacteroides spp.27 and Bif idobacteria.63 These data suggest that in the absence of host carbohydrate-driven selection, supplementation with a complex fermentable fiber may “correct” the microbiota by recreating a type of carbohydrate-rich environment for which the Proteobacteria are ill-equipped to dominate. Similarly, the relative increase in organic acids resulting from kiwifruit carbohydrate fermentation are likely to provide additional control or modulation of

cellulolytic bacteria, such as Ruminococcus spp. and Fibrobacter spp., which are characterized by an extracellular cellulosomal enzyme system, may not necessarily utilize all the solubilized products; rather, these are utilized by other secondary feeders. For example, solubilized oligo- and polysaccharides may be used by members of the propionogenic Bacteroidetes phyla, characterized by a cell-associated sequestration model of highly flexible carbohydrate utilization, or by members of the butyrogenic Lachnospiraceae family. Acetate produced by all these organisms can then be utilized by further feeders such as Bifidobacteria, which in turn are associated with a number of beneficial microbe-host interactions. Methanogens, acetogens and sulfate reducers all compete for the hydrogen formed by the degraded plant material and in turn this may affect the type of primary carbohydrate degrader present in the gut.32,53 In omnivorous hind-gut fermenters such as humans, the organic acid end-products of microbial fermentation of nondigestible plant polysaccharides contribute approximately 10% of the host’s energy balance. Perhaps more important though is the contribution of these end-products to human health, as reviewed previously.12 In this study we have demonstrated that having minimized any selective pressure from host carbohydrates (mucins), the initial host microbiota had varied ability to ferment and adapt to dietary plant material. Carbohydrate utilization from three fractions (pectic, hemicellulosic and cellulosic fiber) differed among donors from the outset, to which we attribute different ratios of members of the microbiota possessing the catalytic machinery capable of degrading them. Degradation of pectic polysaccharides present in the water-soluble fraction was relatively consistent among all three donors, although total degradation was slightly lower for donors 1 and 2 than donor 3, and was slower for donor 2 than donors 1 and 3. The most pronounced differences among the donors were observed in the degradation of insoluble fiber fractions, and particularly the cellulosic polysaccharides; donor 3 degraded cellulose, while donors 1 and 2 did not. These differences in cellulolytic activity were, unsurprisingly, related to the rates of carbohydratedegrading enzyme activities measured. However, despite the large variation observed between our three subjects in terms of carbohydrate-degrading capability, it is still possible to identify some commonalities in terms of microbial ecology and metabolic end-products. Donors 1 and 2 showed the most similarity from pyrosequencing at the phyla levels, with Bacteroidetes decreasing and Firmicutes and Actinobacteria both increasing over time as the kiwifruit digesta was fermented. This was consistent with qPCR data, which showed an increase in Bif idobacteria (from the Actinobacteria phyla) and a decrease in Bacteroides spp. Donor 3 was dominated at the beginning by Proteobacteria and this has undoubtedly influenced the microbial changes that were observed throughout the fermentation process. In concordance with the other two donors, Firmicutes increased at the 24 h time point for donor 3, but unlike in the other two donors, Bacteroidetes also increased over time. These quantitative and qualitative bacterial results also fit with the organic acid data. If we were to attempting to characterize these donors by interspecies-driven cohorts or enterotypes,54 then we could make the following assessments: The donor 1 starting faecal microbiota was rich in Bacteroides spp., reminiscent of the enterotype 1 profile. Defining donor 1’s enterotype at the 48 h time-point was more complex, indicating that there were significant diet-induced changes to the microbiota. The donor 2 5872



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magnitude of the initial effect. Subsequent investigations in the presence of host (carbohydrate and other) factors which may exert controlling, buffering or other selective pressures may shed further light on the role, responsiveness and duration of individual gut microbial enterotype responses to dietary intervention. We predict that the initial responses to dietary modification in terms of changes in microbial abundance are likely to be far less markedly biased in favor of organisms capable of utilizing exogenous carbohydrate (e.g., Lachnospiraceae) due to the ability of selected members (e.g., Bacteroides) to respond by focusing on endogenous (host) carbohydrates,36 but this remains to be proven. Similarly, the initial impact of more refined (or less diverse) fermentable carbohydrates such as inulin might be more dependent on the capabilities and metabolic flexibilities of the starting microbiota. In conclusion, when evaluating the impact of a dietary substrate there are many factors to take into account and this is best done holistically. Establishing the initial microbial enterotype using 16S rRNA pyrosequencing is a good starting point enabling the quantitative and qualitative microbial changes to be tracked over time. However, these data should not be interpreted in isolation; rather, some measure of the carbohydrate-degrading enzyme capacity is required together with metabolic end-product appearance, and how long this response can be sustained. Collectively they provide the possibility of piecing together the likely impact of primary and secondary feeders in the system upon dietary carbohydrate supplementation, and how all of these combined might affect gut health. It would be advantageous to apply the principles described here to an in vivo system, where the role of host carbohydrates would also play an important role.

microbial growth patterns by a feedback mechanism involving both cross-feeding the growth of secondary feeders such as Bifidobacteria, which can competitively exclude pathogens from substrates or attachment sites, and inhibiting growth of opportunistic pathogens such as Salmonella spp. and Escherichia coli, both belonging to the Enterobacteriaceae family of γProteobacteria.64 The ability to degrade cellulosic carbohydrates by donor 3's faecal microbiota provides an interesting paradox. The ability to degrade cellulosic material is essential to some degree as a first step to breaking down the complex indigestible plant material. In addition, cellulose is also produced by many members of Enterobacteria to provide an extracellular matrix for formation of biofilms that adhere to the gut wall,65 so an enhanced ability to produce cellulosidase may be a defense mechanism by core gut phyla to regain lost territory in their own backyard,66 as we appear to have demonstrated in this study. However, an unforeseen in vivo impact of increased ability to degrade these fractions may be the effect on physicochemical properties of material moving through the large bowel, and hence the effect on gut physiology/bulk/transit/laxation. If more cellulose is broken down, and cellulose is responsible for the physicochemical “feel the benefit” effects of fiber, this may have deleterious and somewhat uncomfortable consequences. Depending on the host’s established microbial enterotype, the impact of complex plant polysaccharides appears to vary from one individual to another. The microbiome of an individual appears to change in response to the dietary carbohydrate provided (in the absence of host carbohydrate). In contrast to this dynamic population plasticity, we found that the metabolic phenotype characterized by the organic acid metabolites generated by the different enterotypes showed a greater robustness/resilience to change, which is very important considering that these organic acids feature in many of the microbe-dependent functionalities to the host. Our studies reveal that fermenta from different donors with different enterotypes yielded very similar organic acid profiles, certainly more similar than those we might crudely predict purely on the basis of relative abundance of known acid-producing members of the microbiota, and these should deliver similar functional benefits to the host. Functional generalizations can probably still be made on the basis of enterotypes alone, but it will be important to encompass functional metabolic measures along with quantitative microbial ecological changes. With reference to the kiwifruit substrate used in this study, it appears that the digestion and fermentation of fresh whole kiwifruit resulted in a consistent initial elevation of members of the microbiota able to utilize the sugar residues present, along with the appropriate elevation in glycosidase activities and resultant organic acid byproducts of fermentation. These microbial increases consistently (although not exclusively) featured Lachnospiraceae members of the Firmicutes phyla, predominantly utilized pectic and some hemicellulosic sugars, and resulted in elevated levels of actetate, butyrate and propionate. From a clinical or health management perspective, it appears that consumption of this complex whole-food carbohydrate source always appears to result in desirable outcomes over the short-term, irrespective of the initial apparent microbial enterotype. This batch-feeding investigation does not inform whether a long-term change is sustainable, but from the data presented here it does appear that starting microbial enterotype may have a greater influence over the duration of outcomes of dietary intervention, rather than the

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ASSOCIATED CONTENT

S Supporting Information *

Supplementary figures and tables. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +64 6 355 6108. Fax: +64 6 351 7050. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was funded by ZESPRI Group Ltd (Mount Maunganui, New Zealand), contract # 24818. We thank Thanuja Herath for performing in vitro digestions of the kiwifruit, Gunaranjan Paturi for extracting DNA and conducting the qPCR, and Halina Stoklosinski for assistance with the GC-FID.

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REFERENCES

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Characterizing Kiwifruit Carbohydrate Utilization - American Chemical

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