Evaluating in Vitro Culture Medium of Gut Microbiome with

Article pubs.acs.org/jpr

Evaluating in Vitro Culture Medium of Gut Microbiome with Orthogonal Experimental Design and a Metaproteomics Approach Leyuan Li,†,∥ Xu Zhang,†,∥ Zhibin Ning,† Janice Mayne,† Jasmine I. Moore,† James Butcher,† Cheng-Kang Chiang,† David Mack,‡ Alain Stintzi,*,† and Daniel Figeys*,†,§ †

Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario K1H 8M5, Canada ‡ Department of Paediatrics, CHEO Inflammatory Bowel Disease Centre and Research Institute, University of Ottawa, Ottawa, Ontario K1H 8L1, Canada § Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada S Supporting Information *

ABSTRACT: In vitro culture based approaches are time- and cost-effective solutions for rapidly evaluating the effects of drugs or natural compounds against microbiomes. The nutritional composition of the culture medium is an important determinant for effectively maintaining the gut microbiome in vitro. This study combines orthogonal experimental design and a metaproteomics approach to obtaining functional insights into the effects of different medium components on the microbiome. Our results show that the metaproteomic profile respond differently to medium components, including inorganic salts, bile salts, mucin, and short-chain fatty acids. Multifactor analysis of variance further revealed significant main and interaction effects of inorganic salts, bile salts, and mucin on the different functional groups of gut microbial proteins. While a broad regulating effect was observed on basic metabolic pathways, different medium components also showed significant modulations on cell wall, membrane, and envelope biogenesis and cell motility related functions. In particular, flagellar assembly related proteins were significantly responsive to the presence of mucin. This study provides information on the functional influences of medium components on the in vitro growth of microbiome communities and gives insight on the key components that must be considered when selecting and optimizing media for culturing ex vivo microbiotas. KEYWORDS: gut microbiome, in vitro culturing, metaproteomics, orthogonal design, functional insights

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INTRODUCTION The gut microbiome plays important roles in host physiology and has been linked to the maintenance and improvement of health, while its dysbiosis is often associated with diseases including diabetes, obesity, inflammatory bowel disease, irritable bowel syndrome, and mood disorders.1 In addition, it is important to note that the gut microbiome also influences drug metabolism and toxicity.2 There is increasing interest in targeting the gut microbiota using drug therapeutics,3 with a concomitant increasing interest in understanding both how the gut microbiome responds to therapies and how this interaction contributes to host therapeutic responses. In vivo animal models and human cohort trials have been used to study the function and dynamics of host-associated microbiomes.4−8 However, logistical constraints preclude testing the effects of multiple compounds on the microbiome using these models. Instead, compounds are generally tested against a single bacterium9,10 and occasionally cultured microbiomes.11,12 It is promising to adopt in vitro culturing of the whole gut microbiome because it reveals the direct interaction between gut microbiome and drugs. This approach can serve as largescale initial screenings by providing invaluable clues to guide further studies. © 2017 American Chemical Society

In vitro culturing of gut microbiome is typically achieved by means of static batch cultures.13 For example, we have adopted static batch cultures in conjunction with our stable isotopically labeled microbiota (SILAMi)-based metaproteomic strategy to assess the microbiome response to fructo-oligosaccharide and different monosaccharides.11 In addition, long-term in vitro observations can be achieved by continuous flow culture, e.g., three-stage continuous culturing systems,14 a simulator of the human intestinal microbial ecosystem,15 chemostat16 models, or microfluidic culture, e.g., modular microfluidics-based human-microbial co-culture model (HuMiX)17 and gut-on-achip18 models. Regardless of the technical approach, nutritional components in the culture medium are key determinants for the performance of in vitro culturing systems of gut microbiomes. For decades, most experiments have been conducted with basal culture medium (BCM), which contains peptone and casein, yeast extract, inorganic salts, bile salts, vitamin K, and hemin.11,19−25 Many studies have employed modified versions of the BCM medium to study microbial communities.16,26,27 Received: June 29, 2017 Published: November 13, 2017 154

DOI: 10.1021/acs.jproteome.7b00461 J. Proteome Res. 2018, 17, 154−163

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Journal of Proteome Research Table 1. L8 (27) Orthogonal Array column number and factor namea orthogonal design no. medium medium medium medium medium medium medium medium BCMb

1 2 3 4 5 6 7 8

A, inorganic salts

B, bile salts

C, blank 1

D, SCFAs

E, blank 2

F, blank 3

G, mucin

LCIS LCIS LCIS LCIS HCIS HCIS HCIS HCIS LCIS

− − + + − − + + +

− − + + + + − −

− + − + − + − + −

− + − + + − + −

− + + − − + + −

− + + − + − − + −

a

Columns A, B, D, and G were assigned with inorganic salts, bile salts, SCFAs, and mucin, respectively. Unassigned columns are marked as blanks 1−3. In the orthogonal design of experiment, blank columns indicate either two-factor interactions (represented by symbol “×”) or experimental error. In our design, according to the interaction table of the L8 (27) orthogonal array,45 the three empty columns covers all possibilities of two-factor interactions without confounding with the factor columns, i.e., column C: A × B and D × G; column E: A × D and B × G; and column F: A × G and B × D. bThe classical BCM medium, which was not included in the orthogonal experiment design.

effects of the media components to be assessed using a minimal number of conditions. We then employed a quantitative metaproteomics approach to assess how a microbiota community responds to each combination of factors. This study provides an insight into the functional influences of multiple medium components on the in vitro growth of microbiome communities at the metaproteome level.

These modifications typically involved the addition of several nutrients to improve the diversity of bacterial species that could be cultured. A recent study on culturing “unculturable” gut microbiome has demonstrated that a substantial proportion of the intestinal bacteria are culturable on YCFA agar.28 Compared to BCM medium, YCFA medium is supplemented with short-chain fatty acids (SCFAs) and high concentrations of inorganic salts. YCFA was originally used to culture Faecalibacterium prausnitzii, a well-known abundant human intestinal commensal bacterium.29 While these individual efforts are useful, none of these studies have systemically evaluated the impact of key media components on microbial communities. Notably, the different media used in culturing microbiota can differ dramatically in their composition of inorganic salts, bile salts, SCFAs, and mucin. Understanding how these components impact in vitro cultured microbiotas (both alone and in combination) would greatly aid researchers in determining the optimal medium compositions for their specific studies. The orthogonal experimental design (Taguchi method) has been widely applied to bacterial culturing studies to identify factors with major effects on microbial growth and to determine optimal conditions.30 In this method, the experimental scheme is arranged with a standardized orthogonal array, which is selected based on the number of parameters and the number of levels for each parameters to be evaluated. Multifactor analysis of variance (ANOVA) is then used to examine the effects of many variables with a minimized number of tests while keeping the pair wise balancing property.31 Recent advances in gut metaproteomics facilitates research on the functional role of the gut microbiota in health and disease.32 Besides numerous human gut microbiome studies, metaproteomics has also been applied in animal-based gut microbiome studies, e.g., host and microbiome originated proteins in disease mouse models,33 functional changes between mouse cecal and fecal microbiota,34 and diet’s impact on mouse cecal microbiota.35 Combining orthogonal experimental design with metaproteomic approach will enable a comprehensive insight into microbiota’s functional changes in response to multiple medium compositions. Therefore, in this study we determined the effects of four medium components (inorganic salts, bile salts, SCFAs, and mucin) on the in vitro culturing of mouse gut microbiome. The orthogonal experimental design was employed to create a matrix of conditions allowing for the individual and combined

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MATERIALS AND METHODS

Culture Medium Preparation and in Vitro Gut Microbiome Culturing

Fresh intestinal luminal contents were collected from 14 C57bl/6j mice (female/male = 1:1) At 8−10 weeks of age. The Animal Usage Protocol (2009-012) was approved by the Animal Care Committee at The University of Ottawa. The intestinal samples were mixed in PBS pre-reduced With 0.1% (W/V) L-cysteine hydrochloride, filtered with sterile gauze in an anaerobic workstation (5% H2, 5% CO2, and 90% N2 at 37 °C) and immediately inoculated into each medium for static culturing at a final inoculum concentration of 2% (w/v). A total of three replicates of each medium were conducted, and the culture samples were harvested at 12, 24, and 48 h after inoculation for metaproteomic analysis. A total of eight different culture media according to an L8 (27) orthogonal array (Table 1) and the classical BCM medium were included in this study and were freshly prepared according to Table 1. Briefly, all media contained 2.0 g L−1 peptone water, 2.0 g L−1 yeast extract, 0.5 g L−1 L-cysteine hydrochloride, 2 mL L−1 Tween 40, 5 mg L−1 hemin, and 10 μL L−1 vitamin K1. Media 1−4 were supplemented with low-concentration inorganic salts (LCIS) corresponding to BCM medium (0.1 g L−1 NaCl, 0.04 g L−1 K2HPO4, 0.04 g L−1 KH2PO4, 0.01 g L−1 MgSO 4 .7H 2 O, 0.01 g L −1 CaCl 2 ·2H 2 O, and 2.0 g L −1 NaHCO3);20 media 5−8 were supplemented with highconcentration inorganic salts (HCIS) corresponding to YCFA medium (with a minor modification),29 (1.0 g L−1 NaCl, 0.4 g L−1 K2HPO4, 0.4 g L−1 KH2PO4, 0.1 g L−1 MgSO4·7H2O, 0.1 g L−1 CaCl2·2H2O, and 4.0 g L−1 NaHCO3); Media 3, 4, 7, and 8 were supplemented with 0.5 g L−1 bile salts (catalog no. 48305, Sigma-Aldrich);24 media 2, 3, 5, and 8 were supplemented with 4.0 g L−1 mucin (catalog no. M1778, Sigma-Aldrich);27,36 and media 2, 4, 6, and 8 were supplemented with the three most155


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Journal of Proteome Research

unique peptides were included for protein quantification with the minimum ratio count set as 1. Using Perseus (version 1.5.2.4), the LFQ intensity of each protein group was log10transformed and filtered with the criteria that the protein should be identified by ≥1 unique peptides in ≥95% of the samples (Q95).40 The protein group abundances were Z-scorenormalized across all samples and analyzed by hierarchical clustering and principal component analysis (PCA) to determine if any intrinsic clustering or outliers existed within the data set. Missing values were estimated with the KNN algorithm41 in MetaboAnalyst (version 3.0, http://www. metaboanalyst.ca/). Pearson’s correlation coefficients were calculated to evaluate the reproducibility between each pair of replicates. The taxonomic annotation (phylum level) of the leading protein in each protein group was directly extracted from the annotation file (184sample.uniq_gene.NR.anno.merge.gz) published by Xiao et al.38 using an in-house Perl script.

abundant SCFAs in human gut (33 mM acetic acid, 9 mM propionic acid, and 1 mM butyric acid).27 Trypsin Digestion, Desalting, and LC−MS/MS Analysis

Protein extractions were carried out as previously described37 with minor modifications. Briefly, the culture samples were centrifuged at 300g and 4 °C for 5 min to remove debris. Supernatants were carefully collected and were subjected to two more centrifugations. The supernatants were transferred to 2 mL tubes and centrifuged at 14000g and 4 °C for 20 min. Next, the supernatants were discarded and the pellets lysed with sonication in 200 μL of lysis buffer (4% sodium dodecyl sulfate and 8 M urea in 50 mM Tris−HCl buffe at , pH 8.0; for every 10 mL of lysis buffer, one Roche PhosSTOP tablet and one Roche cOmplete Mini tablet were added). The protein lysates were precipitated in acidified acetone−ethanol buffer at −20 °C overnight to remove sodium dodecyl sulfate. After centrifugation at 16000g for 25 min, the pellets were washed three times with ice-cold acetone. The precipitated proteins were subsequently dissolved in 6 M urea in 50 mM ammonium bicarbonate (pH 8) for trypsin digestion. In-solution trypsin digestion and desalting was performed by strictly following the procedures described by Zhang et al.37 Tryptic peptides were dissolved in 0.1% formic acid, and 4 μg of protein was loaded for liquid chromatography−tandem mass spectrometry (LC− MS/MS) analysis with an Agilent 1100 Capillary LC system (Agilent Technologies, San Jose, CA) and an LTQ-Orbitrap XL mass spectrometer (Thermo Electron, Waltham, MA). Peptides were separated with a tip column (75 μm i.d. × 15 cm) packed with 1.9 μm/120 Å ReproSil-Pur C18 resin (Dr. Maisch GmbH, Ammerbuch, Germany) with a 360 min gradient of 5 to 25% acetonitrile (v/v) at a flow rate of 300 nL/min, with 0.1% formic acid (FA) in water as solvent A and 0.1% FA in acetonitrile as solvent B. The full MS scan range was 350−1800 m/z, with a mass resolution of 60 000 at m/z 400. Each full MS scan was followed by five MS/MS scans performed in ion trap by CID (collision energy, 35%) in positive mode. The isolation window for MS2 is 3 (± 1.5) Da, and the minimum signal threshold was set to 1000. The charge-state rejection function was enabled with default charge state of 2, and charge states with unknown; single charge states were excluded for subsequent MS/MS analysis. The dynamic exclusion settings were: repeat count 1, repeat duration 30 s, and exclusion duration 90 s. Technical reproducibility was assessed by randomly selecting four samples for quality control (QC). These QCs were reprocessed by repeating all steps starting from after the protein precipitation. All samples were run on LC−MS/MS in a randomized order.

PLS-DA Modeling

All missing values of the log10-transformed and Q95-filtered protein group abundance data were imputed using the KNN algorithm,41 and then partial least-squares discriminant analyses (PLS-DA) was performed in MetaboAnalyst for discriminating proteins differentially abundant in response to each medium component. For the establishment of each PLS-DA model, protein group data from all time points were labeled with the two levels in the factor before each analysis. Cross-validation with R2 and Q2 were used to evaluate the performance of the PLS-DA models.42 Identification of the differential proteins in response to each medium factor was achieved using the variable importance in projection (VIP); a protein with a VIP score higher than one was considered as an important feature for group discrimination in the model. LFQ intensities of the proteins with ≥1.0 VIPs in all PLS-DA models were combined, then each row (LFQ intensities of a protein across all samples) was Z-score normalized. Hierarchical clustering of rows was performed with Pearson’s correlation of the normalized data. Each cluster represents a group of proteins with a similar expression pattern in response to different medium components. Multifactor Analysis of Variance

Multifactor analysis of variance (ANOVA) was employed as previously described31,43 using an F-test to analyze the significance of each factor (using Microsoft Excel). For each row cluster obtained above, the average of Z-scores of the clustered proteins in each sample was calculated as the response of factors in the multifactor ANOVA.

Metaproteomics Data Processing

Protein/peptide identification and quantification were carried out using the MetaPro-IQ algorithm, which uses gut microbial gene catalog databases and an iterative database search strategy.37 The mouse gut microbial gene catalog database38 composed of 2 572 074 genes was obtained from the GigaScience Database (http://gigadb.org). The search was performed with up to two missed trypsin cleavages, and precursor mass tolerances of 10 ppm and fragment ion mass tolerance of 0.8 Da. Cysteine carbamidomethylation and oxidation of methionine were set as a fixed modification and a potential modification, respectively. The identified protein lists were generated with a target-decoy strategy at a FDR cutoff of 0.01. Label-free quantification (LFQ) across all samples was carried out with the MaxLFQ algorithm.39 Both razor and

Protein−Protein Interaction Analyses

The proteins with VIP values ≥1.0 were aligned against the COG database (ftp://ftp.ncbi.nih.gov/pub/COG/COG2014/ data) with DIAMOND using the default parameters (e-value cutoff of 0.001).44 The COG ID of the best hit for each protein was used for protein−protein interaction network analysis on the STRING database (version 10.0, http://string-db.org/) and visualized using enhancedGraphics on Cytoscape.45,46 LFQ intensity of each protein cluster involved in the STRING networks were used for scaling the graph. The relative circle size of each protein group is presented according to its total LFQ intensity, while the ratios of LFQ intensities among groups were used in the column chart. 156


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Figure 1. Experimental workflow. The orthogonal experimental design method was used to evaluate the gut microbiome cultured with media different in four components, i.e., (1) inorganic salts, (2) bile salts, (3) SCFAs, and (4) mucin. A total of eight groups of medium compositions were generated according to the L8 (27) orthogonal array (see Table 1). In addition, the classical BCM medium composition was added. The cultured mice gut microbiome samples were harvested at 12, 24, and 48 h after inoculation for metaproteomic analysis. PLS-DA and multifactor analysis of variance methods were used for revealing the effect of each factor and possible interactions on the microbiome.

Figure 2. Principal component analyses (PCAs) with all samples. (A) PCA with all samples based on protein groups’ LFQ intensities showed trends of protein changes over time. (B) PCA based on Z-score normalized LFQ intensities showed discriminations induced by different medium compositions on different principal components.

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RESULTS

biomes were cultured for 12, 24, and 48 h in the nine media with triplicates, resulting in 81 cultured samples (Figure 1). In addition, four samples were randomly selected for MS QC runs, and two samples were screened out due to ineligible samplequality ( 1.0 protein groups; 8 clusters containing >10 proteins were numbered and were included in the multifactor ANOVA calculations. (B) Contributions and significances of factors at all time points (multifactor ANOVA; †, *, **, and *** represent the statistical F value at α = 0.10, 0.05, 0.01, and 0.001 levels, respectively, which were also highlighted with different shades of red background). (C) Proportion of unique peptide matched phyla in each cluster.

Multifactor ANOVA Identification of Significant Contributions of Different Medium Components to the Metaproteome

Principal component analysis (PCA) using the log-transformed LFQ intensity of protein groups showed a timedependent effect of culturing on the metaproteome for all nine different media (Figure 2A). To assess the effect of specific components on these microbial communities, we performed a Z-score normalization to normalize the weights of highabundant and low-abundant proteins prior to the PCA. This revealed that the microbial communities clearly separate on the basis of the presence of inorganic salts, bile salts, and mucin on principal components 1, 2, and 3, respectively (Figure 2B). These findings suggest that inorganic salts and bile salts had the highest effects in modulating the functional activities of microbiotas during in vitro culturing. The microbial communities showed no trend for clustering based on the addition of SCFA; however, this may be because the impact of SCFA addition was overshadowed by the other three medium components in the orthogonal experimental design.

A PLS-DA approach was employed to identify the differentially expressed proteins relating to inorganic salt concentration, bile salts, mucin, and SCFAs, which identified 203, 187, 190, and 159 differential proteins, respectively, with the threshold of VIP > 1.0 in the first component of the PLS-DA (Table S2). Crossvalidation showed high performance for the inorganic salts, bile salts and mucin PLS-DA models (R2 > 0.95 and Q2 > 0.99; Figures S2 and S3) and acceptable performance for SCFA (R2 > 0.95, Q2 > 0.70). The differential proteins for all PLS-DA models were then combined to comprehensively analyze the effects of medium components using the orthogonal designbased analysis of variance. There were 374 differential proteins across all conditions. Hierarchical clustering analysis revealed eight different protein clusters with >10 proteins (Figure 3A). As the proteins in each cluster shared similar response patterns to different media, we performed multifactor ANOVA with each cluster at each time point (Figure 3B and Table S3). In 158


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Figure 4. Evaluation of mucin’s effect on cultured microbiome. (A) Mucin starvation increased flagellin and related hook-associated protein FlgL (marked with a red block; 51.22% of total LFQ intensity in cluster 7) and co-up-regulated several proteins with STRING functional links (as shown by lines linking each protein), size of circles indicate relative abundance of the proteins. (B,C) Partial contribution of mucin within the orthogonal design data set. (B) Proportion of flagellar assembly protein groups in the whole metaproteomic data set, and proportion of flagellin and FlgL’s intensity among the flagellar assembly protein groups. (C) Comparison of flagellin and FlgL abundances between mucin-enriched and mucin-starved medium (t test, P = 0.0004), other cell-motility related proteins with responses are shown in Figure S4A−C. Triple asterisks represent the statistical P values at the 0.001 level.

groups that were up-regulated in the absence of bile salts (Table S4). Among these were 83 protein groups involved in four COG categories, i.e., carbohydrate and amino acid transport and metabolism, energy production, and conversion and translation, ribosomal structure and biogenesis. The highest F value for cluster VI was present at 12 h, and decreased at 24 and 48 h, which might indicate a quick response of the microbiome to bile salts and possible adaptation over time. In this cluster, 10 out of the 47 proteins were OMRs for iron and cobalamin acquisition (with all matched sequences assigned to Bacteroidetes). The microbiome’s responses to mucin were significant in clusters I (48 h, α < 0.05; 24 and 12 h, α < 0.1), III (for all time points, α < 0.01), VII (48 and 24 h, α < 0.01; 12 h, α < 0.1). STRING protein−protein interaction network suggested that all the 18 types of COG-classified proteins in cluster VII were functionally linked (Figure 4A). Among these, more than 50% of total protein LFQ intensity were contributed by Firmicutesoriginated flagellin and related hook-associated protein FlgL, which was increased in the absence of mucin in medium. Moreover, cluster VII had a significant increase over time (twoway ANOVA, P < 0.0001; F = 232.21, DFn = 2, and DFd = 768). The current orthogonal experimental design also identified a significant two-factor interaction between inorganic salts and mucin in cluster I (48 and 24 h, α < 0.05; 12 h, α < 0.1; Table S3). In particular, in the presence of HCIS and mucin, the abundance of cluster I proteins increased (5.3, 4.1, and 4.2 mean fold increase compared with HCIS − mucin, LCIS + mucin, and LCIS − mucin medium, respectively). Of the 39 protein groups in cluster I, 17 were classified as translation, ribosomal structure and biogenesis and cell wall, membrane, and envelope biogenesis proteins (Table S4). Proteins with high fold changes (>10 fold) in this cluster include multidrug efflux pump subunit AcrA (membrane-fusion protein), OMRs for ferrienterochelin and colicins, and Ser and Thr protein kinase RdoA. We did not identify a major impact of SCFA

addition, all proteins were aligned against the COG database to determine the functions represented by each cluster. Generally, 57.3% of these responding proteins were assigned to COG categories of energy production and conversion (C), amino acid transport and metabolism (E), carbohydrate transport and metabolism (G), translation, ribosomal structure and biogenesis (J), and cell wall, membrane, and envelope biogenesis (M). Taxonomic analysis using protein sequences of each cluster suggested that most of the proteomic responses were taxonomy-dependent (Figure 3C). A total of 87.5%−96.8% of the proteins of each protein group from clusters I, VI, and VIII were assigned to Bacteroidetes, while 74.4%−95.1% of those from clusters II, IV and VII were assigned to Firmicutes. The cultured microbiome’s responses to inorganic salts were significant in clusters I (48 h, α < 0.05; 24 and 12 h, α < 0.1), V (48 and 24 h, α < 0.0001; 12 h, α < 0.00 001) and VIII (48 and 12 h, α < 0.001; 24 h, α < 0.01). Cluster V, which was increased in LCIS, contained the highest number of proteins among all clusters, indicating a dominant effect of inorganic salt concentration on the microbiome. We found that 19 out of the 79 proteins in cluster V were matched to the Bacteroidetes or Proteobacteria phyla and were annotated as peroxiredoxin, rubrerythrin, thioredoxin, superoxide dismutase, catalase, and alkyl hydroperoxide reductase subunit AhpF (Table S4), which are major components involved in oxidative stress response,48−50 and heat-shock proteins GroEL, GroES, and DnaK, which are involved in general stress response.51 Among the 52 proteins in cluster VIII, 14 proteins (all matching to Bacteroidetes, Table S4) were outer membrane receptor (OMR) proteins related with iron transport and cobalamin acquisition, suggesting an important effect of inorganic salts on iron-related pathways. Bile salts showed significant effects in clusters II (48 and 24 h, α < 0.01; 12 h, α < 0.05), IV (48 and 24 h, α < 0.05; 24 h, α < 0.1) and VI (48 and 24 h, α < 0.01; 12 h, α < 0.001). Clusters II and IV, both of which having high proportion of sequences assigned to Firmicutes (Figure 3C), contained 117 protein 159


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16 sRNA gene sequencing does not provide information on functional changes. Functional characterizations of the gut microbiome using metaproteomics approach provides vast quantified functional information linking to microbial taxa. Our study took the advantage of metaproteomics in revealing the significant impacts of medium components on specific functions through incorporating intensities of clustered proteins into the orthogonal array. We summarized a detailed list of responding proteins in response to medium components (Table S4), including protein IDs, COG functional information, taxonomic estimation, and fold-changes, for aiding researchers with selecting proper medium for their specific experimental purposes. For general purpose culturing, maintenance of the microbiome both functionally and compositionally would be demanded. Stresses and nutritional starvation are important causes of imbalanced microbiome. Oxidative stress is induced by an imbalance between the generation of reactive oxygen species and the microbial protective mechanisms. The level of oxidative stress has been reported to increase in response to a high-fat diet,52 inflammatory bowel disease,53 and diabetes.54 Qin et al.54 have reported that diabetes patients have dysbiosis of the gut microbiota, and microbial functions are enriched in oxidative stress resistance. Daniel et al.35 have also reported that the metaproteomic profile of high-fat diet mice enriches in enzymes involved in oxidative stress responses, which may be caused by an adaptation to environmental redox potential.55 In this study, the LCIS might resulted in an environment of oxidative stress by producing an inadequate redox potential in the media. Hence the HCIS formula could be considered for minimizing medium-induced stress responses. Flagellin composes the bacterial flagellar filament and rotation of these filaments enables cell motility.56 Studies have demonstrated increased cell motility in gut microbes at the post-exponential phase57 and under nutrient starvation.58 Both in vivo and in vitro studies have shown that many members of the phylum Firmicutes forge on the mucin layer.59−61 Based on our data, it is tempting to speculate that as mucin is consumed and nutrient starvation increases, the Firmicutes respond by increasing the expression of flagella related proteins. Firmicutes grown in the absence of mucin would already be in a “nutrient-starved” condition and would therefore accelerate the expression of these proteins. Hence, our study shows that mucin is an important factor for the in vitro growth of Firmicutes. Commercially available mucins are usually obtained from porcine stomach and have been adopted by the in vitro culturing of gut microbiome27,60 and single bacteria.62 However, there is structural difference between gastric and colonic mucin. Stahl et al. have shown differences between Oglycans released from the porcine gastric mucin and those from the piglet colon.63 Some gut bacteria forage on the diverse and complex mucin O-glycans,64 which plays a role in selection of commensal biota by providing preferential binding sites.65 Therefore, it would be necessary to further investigate different types of mucin for a well-maintained in vitro culture. Results from this study can also be used to select conditions that would favor functional enrichment in the cultured microbiome. For example, COG4771, the outer membrane receptor for ferrienterochelin and colicins, has been reported to be statistically more abundant in patients in ileum Crohn’s disease than in healthy people and thus has been suggested as a potential stool indicator.66 A medium composition enhancing COG4771 could be considered for ileum Crohn’s disease

supplementation in our various media combinations. Only one significant response toward SCFAs was found at the level α < 0.1 in cluster I at 12 h. Partial- and Single-Factor Effect of Mucin on Bacterial Cell Motility

The orthogonal analyses above was performed with stringent filter criteria (i.e., protein observed in 95% of the samples). This filtering prevents artifacts being introduced due to overimputation before the data sets were subjected to multifactorial ANOVA. However, this approach disfavors lower abundance proteins that do not meet the Q95 criteria. Here, we reexamined the data set by looking into the effects of mucin presence on proteins related to the flagellar apparatus. First, we looked at the partial contribution of mucin within the orthogonal design data set. For an in-depth view of whether the flagellar apparatus generally responded to mucin in this microbiome sample, all of the 7413 protein groups were examined against the list of bacterial motility proteins in the KEGG database (http://www.genome.jp/kegg-bin/get_ htext?ko02035.keg). Accordingly, 550 protein groups involved in flagellar assembly were matched including flagellin, FlgL, FlgE, FlgK, FlgG, FliF/YscJ, FlgJ, FliN, FlhF, FliG, MotB, and MotA, and the sum of LFQ intensity of flagellin and FlgL occupied 99.6% of these proteins (Figure 4B). Subtotals of LFQ intensities were calculated according to annotated COG protein names, and each of these COG-annotated proteins were examined with a t test, revealing significant partial contributions of mucin to the expression of flagellin and related hook-associated protein FlgL (Figure 4C), flagellar hook protein FlgE, flagellar motor component MotA, and flagellar biosynthesis/type III secretory pathway M-ring protein FliF/ YscJ (Figure S4). Second, we compared the classical BCM medium and medium 3 because these media only differ by the presence of mucin. We compared the effects of mucin on the cultured microbiome using Q50 filtering criteria. At each time point, significantly differentially expressed protein groups were obtained (t test; P < 0.005, FDR < 0.05; Figure S5A), and 19.4%−41.4% of the total LFQ intensity of these differential proteins were annotated as flagellin and related hook-associated protein FlgL that were more abundant in the absence of mucin (Figure S5A,B). Apart from flagellin and FlgL, among the cellmotility related proteins, only MotA was found to have a significant difference between the two groups at the 12 h time point (Figure S5C).

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DISCUSSIONS The orthogonal array design is usually adopted for biological process optimization with a single or a few evaluation parameters (e.g., yield, efficiency, etc.) through its powerful ability in estimating the significant factors.30 However, compared with conventional process optimizations, the fact that the gut microbiome is a complex ecosystem dramatically increased the difficulty for optimizing the in vitro culture medium for medical research purposes. To create an ex vivo gut microbial ecosystem mimicking the in vivo conditions, each microbial compartment in the ecosystem should be maintained at a similar functional state as that in vivo. The response of microbiota to culture media, e.g., the BCM medium, has been previously investigated through 16S rRNA gene sequencing and identified key taxonomic variables for differentiation between the microbiota structure before and after culturing.24 However, 160


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Journal of Proteome Research Author Contributions

related in vitro studies. Our results suggest that bile salts and HCIS and the exclusion of mucin would enhance COG4771 (clusters II and VIII in Table S4).

D.F., A.S., L.L., X.Z., and Z.N. designed the study. L.L., X.Z., J.M., and J.I.M. performed the experiments. L.L., X.Z., and Z.N. analyzed the data. L.L., X.Z., and D.F. wrote the manuscript. A.S., Z.N., J.M., J.B., C.C., and D.M. contributed to the editing and revision of the manuscript. All authors read and approved the final manuscript.

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CONCLUSIONS This study demonstrates the usefulness of integrating an orthogonal experimental design with a metaproteomic approach to characterize the effects of four medium component factors, inorganic salts, bile salts, SCFAs, and mucin, on the functionally of the gut microbiome. The standard orthogonal array can reflect the information on all factor combinations with a minimized number of experiments through multifactor analysis of variance, which enabled the identification of significant effects at the metaproteome level of medium components and two-factor interactions on microbiome functions. Inorganic salts, bile salts, and mucin showed markedly regulatory effects on the metaproteome during in vitro culturing. In contrast, SCFAs showed a relatively minor effect compared with the other factors. Moreover, functional and taxonomic-specific responses were found to be corresponding to different clusters of protein groups. The observations in orthogonal experimental design were further validated using partial-contribution and single-factor analyses on the effects of mucin starvation. Despite the fact that the stringent filter criteria disfavors low-abundance proteins that cannot be sensitively quantified by the MS, our approach enables an informative multifactorial view of the microbiome’s response to medium compositions, including interactions between factors. The orthogonal design also allows the identification of the relative importance of different factors affecting the microbiome. This same experimental approach could be used to characterize the impact of a combination of multiple components on the functionality of the microbiota while limiting the number of experiments to perform.

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Notes

The authors declare the following competing financial interest(s): D.F. and A.S. have co-founded Biotagenics, a clinical microbiomics company. All other authors declare no competing interest. Additional mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE67 partner repository and may be accessed at no charge at http://www.proteomexchange.org/ with the data set identifier PXD006808.

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ACKNOWLEDGMENTS D.F. acknowledges a Canada Research Chair in Proteomics and Systems Biology. This work was supported by the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-114), CIHR grant no. GPH-129340, the Natural Sciences and Engineering Research Council of Canada (NSERC, grant no. 210034), and the Ontario Ministry of Economic Development and Innovation (REG1-4450).

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ABBREVIATIONS BCM, basal culture medium; COG, clusters of orthologous groups; FDR, false discovery rate; HCIS, high-concentration inorganic salts; HuMiX, modular microfluidics-based human− microbial co-culture model; KNN, k-nearest neighbor algorithm; LC−MS/MS, liquid chromatography−tandem mass spectrometry; LCIS, low-concentration inorganic salts; LFQ, label-free quantification; MetaPro-IQ, metaproteome identification and quantification; OMR, outer membrane receptor; PCA, principal component analysis; PLS-DA, partial leastsquares discriminant analysis; SCFA, short-chain fatty acid; VIP, variable importance in projection; YCFA, medium medium with yeast extract, casitone, fatty acid

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.7b00461. Figures showing the evaluation of data quality, crossvalidation of PLS-DA models, a heat map of PLS-DA VIP > 1.0 proteins, significant effects of mucin on other cell motility-related proteins, and a single-factor comparison of mucin’s effect on cell motility-related proteins. (PDF) Tables showing Pearson’s correlation coefficients across all samples; a list of PLS-DA VIP ≥ 1.0 proteins in each pair of comparison; multifactor analyses of variance tables; and a list of clustered proteins, COG categories, and fold changes. (XLSX)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: dfi[email protected]. *E-mail: [email protected]. ORCID

Daniel Figeys: 0000-0002-5373-7546 Author Contributions ∥

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