Evaluating in Vitro Culture Medium of Gut Microbiome with Orthogonal

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Evaluating in vitro culture medium of gut microbiome with orthogonal experimental design and metaproteomics approach Leyuan Li, Xu Zhang, Zhibin Ning, Janice Mayne, Jasmine I. Moore, James Butcher, Cheng-Kang Chiang, David R. Mack, Alain Stintzi, and Daniel Figeys J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00461 • Publication Date (Web): 13 Nov 2017 Downloaded from http://pubs.acs.org on November 13, 2017

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Journal of Proteome Research

Evaluating in vitro culture medium of gut microbiome with orthogonal experimental design and metaproteomics approach Leyuan Li 1,†, Xu Zhang 1,†, Zhibin Ning 1, Janice Mayne 1, Jasmine I. Moore 1, James Butcher 1

, Cheng-Kang Chiang 1, David Mack 2, Alain Stintzi 1,*, and Daniel Figeys 1,3,*

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 obtain 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. Multi-factor analysis of variance (ANOVA) 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/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. * Correspondence: DF:[email protected]; AS:[email protected] 1 Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada 2 Department of Paediatrics, CHEO Inflammatory Bowel Disease Centre and Research Institute, University of Ottawa, Ottawa, ON, Canada 3 Canadian Institute for Advanced Research, Toronto, Ontario, Canada † Both authors contributed equally to this work

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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 therapeutics3, 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 since it reveals the direct interaction between gut microbiome and drugs. This approach can serve as large scale initial screenings by providing invaluable clues to guide further studies. 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 Simulator of the Human Intestinal Microbial Ecosystem,15 and chemostat 16 models, or microfluidic culture, e.g. modular microfluidics-based human-microbial co-culture model - HuMiX17 and gut-on-a-chip18 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/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 2 ACS Paragon Plus Environment

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communities. 16, 26, 27 These modifications typically involved the addition of several nutrients in order 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 with 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 basing on the number of parameters and the number of levels for each parameters to be evaluated. Multi-factor 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 3 ACS Paragon Plus Environment


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orthogonal experimental design was employed to create a matrix of conditions allowing for the individual and combined 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.

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). Three replicates of each medium were conducted and the culture samples were harvested at 12 h, 24 h and 48 h after inoculation for metaproteomic analysis. 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 MgSO4⋅7H2O, 0.01 g L-1 CaCl2⋅2H2O, and 2.0 g L-1 NaHCO3);20 Media 5-8 were supplemented with high concentration 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 (cat #48305, Sigma-Aldrich);24 Media 2, 3, 5, and 8 were supplemented with 4.0 g L-1 mucin (cat #M1778, Sigma-Aldrich);27, 36 and Media 2, 4,


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6, and 8 were supplemented with the three most abundant SCFAs in human gut (33 mM acetic acid, 9 mM propionic acid and 1 mM butyric acid).27 Table 1 The L8 (27) orthogonal array A B Orthogonal Inorganic Bile salts design no. salts

G Mucin

Medium 1

LCIS

-

-

-

-

-

-

Medium 2

LCIS

-

-

+

+

+

+

Medium 3

LCIS

+

+

-

-

+

+

Medium 4

LCIS

+

+

+

+

-

-

Medium 5

HCIS

-

+

-

+

-

+

Medium 6

HCIS

-

+

+

-

+

-

Medium 7

HCIS

+

-

-

+

+

-

Medium 8

HCIS

+

-

+

-

-

+

LCIS

+

BCM a

Column number and factor name a C D E F Blank 1 SCFAs Blank 2 Blank 3

b

-

-

Columns A, B, D and G were assigned with inorganic salts, bile salts, SCFAs and

mucin, respectively. Unassigned columns are marked as Blank 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, D×G; column E: A×D, B×G; column F: A×G, B×D. b

The classical BCM medium, which was not included in the orthogonal experiment

design.

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 300 × g, 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 14,000 × g, 4°C for 20 min. Then, the supernatants were discarded and the pellets lysed with sonication in 200 µL lysis buffer (4 % sodium dodecyl sulfate and 8 M urea in 50 mM Tris-HCl buffer, pH 8.0; for every 10 mL 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 16,000 × g for 5 ACS Paragon Plus Environment


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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 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 and single charge state 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. 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 database 38 comprising 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 Dalton. 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 6 ACS Paragon Plus Environment

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samples was carried out with the MaxLFQ algorithm.39 Both razor and unique peptides were included for protein quantification with the minimum ratio count set as 1. Using Perseus (version 1.5.2.4), LFQ intensity of each protein group was log10 transformed 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-score normalized 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. 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. Multi-factor analysis of variance: Multi-factor analysis of variance (ANOVA) was employed as previously described31, 43 using F-test to analyze the significance of each factor 7 ACS Paragon Plus Environment


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(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 multi-factor ANOVA. 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 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.

Results Metaproteomic response of cultured microbiome to medium compositions Orthogonal experimental design methods31 use standard arrays to minimize the number of experiments while reflecting full information of all the factors that may affect the experimental outcome. One of the most commonly used orthogonal arrays for two-level factors is the L8 (27) array. The columns of the orthogonal array are balanced by having equal numbers of factor levels and balanced level combinations between any two columns.47 We can thus use orthogonal design to identify significant influencing factors (i.e. medium components) and interactions in the study.47 In this study, the L8 (27) orthogonal array was adopted to evaluate the influences of different medium components when culturing a gut microbiome in vitro. Four of the seven columns were assigned with medium composition factors, and the three unassigned columns were used to evaluate experimental errors and two-factor interactions (Table 1). The classical BCM medium composition was also included as a comparison. Pooled gut microbiomes were cultured for 12 h, 24 h 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 sample-quality (< ½ average peptide identification). Therefore, 83 high-quality MS raw files 8 ACS Paragon Plus Environment

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were obtained with a total of 2,879,562 MS/MS spectra. 27,060 peptides corresponding to 7,413 protein groups were identified across all samples with a false discovery rate (FDR) threshold of 1%. To obtain an accurate estimation of the multi-factor effects based on orthogonal experimental design, stringent data filtering criteria was used to identify 1,860 protein groups present in more than 95% samples. We first evaluated the data quality of this dataset through Pearson’s correlation analysis. The average Pearson’s correlation coefficient between the four sample/QC pairs was 0.94 ± 0.02 (mean ± SD, n = 4) and the correlation between each group of 3 culture replicates ranged from 0.889 ± 0.045 to 0.960 ± 0.001 (n = 3), (Supporting information Figure S1 and Table S1). These results demonstrate high reproducibility using our metaproteomics approach.

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. 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 h, 24 h and 48 h after inoculation for metaproteomic analysis. PLS-DA and multi-factor analysis of variance methods were used for revealing the effect of each factor and possible interactions on the microbiome.



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Principal component analysis (PCA) using the log-transformed LFQ intensity of protein groups showed a time dependent 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 high-abundant and low-abundant proteins prior to the PCA. This revealed that the microbial communities clearly separate based on 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.

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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Multi-factor ANOVA identified significant contributions of different medium components to the metaproteome 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 (Supporting Information, Table S2). Cross validation showed high performance for the inorganic salts, bile salts and mucin PLS-DA models (R2 > 0.95, Q2 > 0.99, Supporting Information, 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 design-based 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 multi-factor ANOVA with each cluster at each time point (Figure 3B and Supporting Information Table S3). In 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/envelope biogenesis (M). Taxonomic analysis using protein sequences of each cluster suggested that most of the proteomic responses were taxonomy-dependent (Figure 3C). 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.



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Figure 3 Contributions of factors to the metaproteome of cultured microbiome. (A) Hierarchical clustering and heat map of combined PLS-DA VIP > 1.0 protein groups, eight clusters containing > 10 proteins were numbered and were included in the multi-factor ANOVA calculations. (B) Contributions and significances of factors at all time points (multi-factor 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.


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The cultured microbiome’s responses to inorganic salts were significant in Clusters I (48 h, α < 0.05; 24 h and 12 h, α < 0.1), V (48 h and 24 h, α < 0.0001; 12 h, α < 0.00001) and VIII (48 h 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 (Supplementary Information, 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 h and 24 h, α < 0.01; 12 h, α < 0.05), IV (48 h and 24 h, α < 0.05; 24 h, α < 0.1) and VI (48 h and 24 h, α < 0.01; 12 h, α < 0.001). Clusters II and IV, both of which had high proportion of sequences assigned to Firmicutes (Figure 3C), contained 117 protein 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 adaption 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 h and 12 h, α < 0.1), III (for all time points, α < 0.01), VII (48 h 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 which, more than 50% of total protein LFQ intensity were contributed by Firmicutes-originated flagellin and related 13 ACS Paragon Plus Environment


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hook-associated protein FlgL, which was increased in the absence of mucin in medium. Moreover, Cluster VII had a significant increase over time (two-way ANOVA, P < 0.0001; F=232.21, DFn=2, DFd=768).

Figure 4 Evaluation of mucin’s effect on cultured microbiome. (A) Mucin starvation increased flagellin and related hook-associated protein FlgL (marked with red block, 51.22% of total LFQ intensity in Cluster 7) and co-upregulated several proteins with STRING functional links (as shown by lines linking each protein), size of circles indicate relative abundance of the proteins. B and C, partial contribution of mucin within the orthogonal design dataset: (B) Proportion of flagellar assembly protein groups in the whole metaproteomic dataset, 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 S4 A-C. *** represent the statistical P values at the 0.001 level.

The current orthogonal experimental design also identified a significant two-factor interaction between inorganic salts and mucin in Cluster I (48 h 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 as compared with HCIS-mucin, LCIS+mucin, and LCIS-mucin medium, respectively). Of the 39 protein groups in Cluster I, 17 were 14 ACS Paragon Plus Environment

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classified as translation, ribosomal structure and biogenesis and cell wall/membrane/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/colicins, and Ser/Thr protein kinase RdoA. We did not identify a major impact of SCFA supplementation in our various media combinations. Only one significant response towards 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 over-imputation before the datasets were subjected to multi-factorial ANOVA. However, this approach disfavors lower abundance proteins that do not meet the Q95 criteria. Here, we re-examined the dataset by looking into the effects of mucin presence on proteins related to the flagellar apparatus. Firstly, we looked at the partial contribution of mucin within the orthogonal design dataset. For an in depth-view of whether the flagellar apparatus generally responded to mucin in this microbiome sample, all the 7,413 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 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 (Supplementary information, Figure S4). Secondly, we compared the classical BCM medium and Medium 3 as these media only differ by the presence of mucin. We compared the effects of mucin on the cultured microbiome 15 ACS Paragon Plus Environment


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using Q50 filtering criteria. At each time point, significantly differentially expressed protein groups were obtained (t-test; P < 0.005, FDR < 0.05; Supplementary 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 absence of mucin (Figure S5A and B). Apart from flagellin and FlgL, among the cell-motility related proteins, only MotA was found to have a significant difference between the two groups at the 12 h time point (Supplementary information, Figure S5C).

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 condition, 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, 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 (Supporting Information, 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 16 ACS Paragon Plus Environment

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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 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 in vitro culture 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 O-glycans 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, Outer membrane receptor for ferrienterochelin and colicins, has been reported to be statistically more abundant in 17 ACS Paragon Plus Environment


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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 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).

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 of all factor combinations with a minimized number of experiments through multi-factor 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 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 that the stringent filter criteria disfavors low abundance proteins that cannot be sensitively quantified by the MS, our approach enables an informative multi-factorial 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.

Supporting Information Supporting information Tables S1-S4: Table S1. Pearson’s correlation coefficients across all samples; Table S2. List of PLS-DA VIP ≥ 1.0 proteins in each pair of comparison; Table S3. Multi-factor analyses of variance tables; Table S4. List of clustered proteins, COG categories and fold changes. Supporting information Figures S1-S5: Figure S1. Evaluation 18 ACS Paragon Plus Environment

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of data quality; Figure S2. Cross validation of PLS-DA models; Figure S3. Heat map of PLS-DA VIP > 1.0 proteins; Figure S4. Significant effects of mucin on other cell motility-related proteins; Figure S5. Single factor comparison of mucin’s effect on cell motility-related proteins.

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 coupled with 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 least squares – discriminant analysis; SCFA, short-chain fatty acid; VIP, variable importance in projection; YCFA medium, medium with yeast extract, casitone, fatty acid.

Acknowledgements DF 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 number 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).

Author contributions DF, AS, LL, XZ and ZN designed the study. LL, XZ, JM and JIM performed the experiments. LL, XZ and ZN analyzed the data. LL, XZ and DF wrote the manuscript, and AS, ZN, JM, JB, CC, and DM contributed to the editing and revision of the manuscript. All authors read and approved the final manuscript.

Availability of data The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE67 partner repository with the dataset identifier PXD006808. 19 ACS Paragon Plus Environment


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Conflict of Interest Disclosure D.F. and A.S. have co-founded Biotagenics, a clinical microbiomics company. All other authors declare no competing interest.

References

(1)

Gerritsen, J.; Smidt, H.; Rijkers, G. T.; de Vos, W. M., Intestinal microbiota in human health and disease: the impact of probiotics. Genes Nutr. 2011, 6, (3), 209-240.

(2)

Spanogiannopoulos, P.; Bess, E. N.; Carmody, R. N.; Turnbaugh, P. J., The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat Rev Micro 2016, 14, (5), 273-287.

(3)

Jia, W.; Li, H.; Zhao, L.; Nicholson, J. K., Gut microbiota: a potential new territory for drug

(4)

de la Cuesta-Zuluaga, J.; Mueller, N. T.; Corrales-Agudelo, V.; Velásquez-Mejía, E. P.;

targeting. Nat. Rev. Drug Discovery 2008, 7, (2), 123-129. Carmona, J. A.; Abad, J. M.; Escobar, J. S., Metformin is associated with higher relative abundance of mucin-degrading Akkermansia muciniphila and several short-chain fatty acid– producing microbiota in the gut. Diabetes Care 2017, 40, (1), 54-62. (5)

Shin, N.-R.; Lee, J.-C.; Lee, H.-Y.; Kim, M.-S.; Whon, T. W.; Lee, M.-S.; Bae, J.-W., An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 2014, 63, (5), 727-735.

(6)

Zhang, X.; Zhao, Y.; Xu, J.; Xue, Z.; Zhang, M.; Pang, X.; Zhang, X.; Zhao, L., Modulation of gut microbiota by berberine and metformin during the treatment of high-fat diet-induced obesity in rats. Sci Rep. 2015, 5, 14405.

(7)

Xu, D.; Gao, J.; Gillilland Iii, M.; Wu, X.; Song, I.; Kao, J. Y.; Owyang, C., Rifaximin alters intestinal bacteria and prevents stress-induced gut inflammation and visceral hyperalgesia in rats.

Gastroenterology 2014, 146, (2), 484-496.e4. (8)

Kang, D.-W.; DiBaise, J. K.; Ilhan, Z. E.; Crowell, M. D.; Rideout, J. R.; Caporaso, J. G.; Rittmann, B. E.; Krajmalnik-Brown, R., Gut microbial and short-chain fatty acid profiles in adults with chronic constipation before and after treatment with lubiprostone. Anaerobe 2015, 33, 33-41.

(9)

Haiser, H. J.; Gootenberg, D. B.; Chatman, K.; Sirasani, G.; Balskus, E. P.; Turnbaugh, P. J., Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella

lenta. Science 2013, 341, (6143), 295-298. (10)

Crowther, G. S.; Baines, S. D.; Todhunter, S. L.; Freeman, J.; Chilton, C. H.; Wilcox, M. H., Evaluation of NVB302 versus vancomycin activity in an in vitro human gut model of

Clostridium difficile infection. J. Antimicrob. Chemother. 2013, 68, (1), 168-176. (11)

Zhang, X.; Ning, Z.; Mayne, J.; Deeke, S. A.; Li, J.; Starr, A. E.; Chen, R.; Singleton, R.; Butcher, J.; Mack, D. R.; Stintzi, A.; Figeys, D., In vitro metabolic labeling of intestinal microbiota for quantitative metaproteomics. Anal. Chem. 2016, 88, (12), 6120-6125.

(12)

Maccaferri, S.; Vitali, B.; Klinder, A.; Kolida, S.; Ndagijimana, M.; Laghi, L.; Calanni, F.; Brigidi, P.; Gibson, G. R.; Costabile, A., Rifaximin modulates the colonic microbiota of patients


Page 20 of 25

Page 21 of 25

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


with Crohn's disease: an in vitro approach using a continuous culture colonic model system. J.

Antimicrob. Chemother. 2010, 65, (12), 2556-2565. (13)

Williams, C. F.; Walton, G. E.; Jiang, L.; Plummer, S.; Garaiova, I.; Gibson, G. R., Comparative analysis of intestinal tract models. Annu. Rev. Food Sci. Technol. 2015, 6, (1), 329-350.

(14)

Gibson, G. R.; Cummings, J. H.; Macfarlane, G. T., Use of a three-stage continuous culture system to study the effect of mucin on dissimilatory sulfate reduction and methanogenesis by mixed populations of human gut bacteria. Appl. Environ. Microbiol. 1988, 54, (11), 2750-2755.

(15)

Joly, C.; Gay-Quéheillard, J.; Léké, A.; Chardon, K.; Delanaud, S.; Bach, V.; Khorsi-Cauet, H., Impact of chronic exposure to low doses of chlorpyrifos on the intestinal microbiota in the Simulator of the Human Intestinal Microbial Ecosystem (SHIME®) and in the rat. Environ. Sci.

Pollut. Res. 2013, 20, (5), 2726-2734. (16)

McDonald, J. A. K.; Fuentes, S.; Schroeter, K.; Heikamp-deJong, I.; Khursigara, C. M.; de Vos, W. M.; Allen-Vercoe, E., Simulating distal gut mucosal and luminal communities using packed-column biofilm reactors and an in vitro chemostat model. J. Microbiol. Methods 2015, 108, 36-44.

(17) Shah, P.; Fritz, J. V.; Glaab, E.; Desai, M. S.; Greenhalgh, K.; Frachet, A.; Niegowska, M.; Estes, M.; Jäger, C.; Seguin-Devaux, C.; Zenhausern, F.; Wilmes, P., A microfluidics-based in vitro model of the gastrointestinal human–microbe interface. Nat. Commun. 2016, 7, 11535. (18)

Kim, H. J.; Li, H.; Collins, J. J.; Ingber, D. E., Contributions of microbiome and mechanical deformation to intestinal bacterial overgrowth and inflammation in a human gut-on-a-chip.

PNAS 2016, 113, (1), E7-E15. (19)

Bone, E.; Tamm, A.; Hill, M., The production of urinary phenols by gut bacteria and their possible role in the causation of large bowel cancer. AJCN 1976, 29, (12), 1448-1454.

(20)

Gibson, G. R.; Wang, X., Bifidogenic properties of different types of fructo-oligosaccharides.

Food Microbiol. 1994, 11, (6), 491-498. (21)

Lesmes, U.; Beards, E. J.; Gibson, G. R.; Tuohy, K. M.; Shimoni, E., Effects of resistant starch type III polymorphs on human colon microbiota and short chain fatty acids in human gut models.

J. Agric. Food Chem. 2008, 56, (13), 5415-5421. (22)

Rycroft, C. E.; Jones, M. R.; Gibson, G. R.; Rastall, R. A., A comparative in vitro evaluation of the fermentation properties of prebiotic oligosaccharides. J. Appl. Microbiol. 2001, 91, (5), 878-887.

(23)

Olano-Martin, E.; Mountzouris, K. C.; Gibson, G. R.; Rastall, R. A., In vitro fermentability of dextran, oligodextran and maltodextrin by human gut bacteria. Br. J. Nutr. 2000, 83, (3), 247-255.

(24)

Long, W.; Xue, Z.; Zhang, Q.; Feng, Z.; Bridgewater, L.; Wang, L.; Zhao, L.; Pang, X., Differential responses of gut microbiota to the same prebiotic formula in oligotrophic and eutrophic batch fermentation systems. Sci Rep. 2015, 5, 13469.

(25)

Saulnier, D. M. A.; Gibson, G. R.; Kolida, S., In vitro effects of selected synbiotics on the human faecal microbiota composition. FEMS Microbiol. Ecol. 2008, 66, (3), 516.

(26)

Macfarlane, G. T.; Macfarlane, S.; Gibson, G. R., Validation of a three-stage compound continuous culture system for investigating the effect of retention time on the ecology and metabolism of bacteria in the human colon. Microb. Ecol. 1998, 35, (2), 180-187.

(27)

McDonald, J. A. K.; Schroeter, K.; Fuentes, S.; Heikamp-deJong, I.; Khursigara, C. M.; de Vos, W. M.; Allen-Vercoe, E., Evaluation of microbial community reproducibility, stability and



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

composition in a human distal gut chemostat model. J. Microbiol. Methods 2013, 95, (2), 167-174. (28)

Browne, H. P.; Forster, S. C.; Anonye, B. O.; Kumar, N.; Neville, B. A.; Stares, M. D.; Goulding, D.; Lawley, T. D., Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 2016, 533, (7604), 543-546.

(29)

Duncan, S. H.; Hold, G. L.; Harmsen, H. J. M.; Stewart, C. S.; Flint, H. J., Growth requirements and fermentation products of Fusobacterium prausnitzii, and a proposal to reclassify it as

Faecalibacterium prausnitzii gen. nov., comb. nov. Int. J. Syst. Evol. Microbiol. 2002, 52, (6), 2141-2146. (30)

El-Moslamy, S. H.; Elkady, M. F.; Rezk, A. H.; Abdel-Fattah, Y. R., Applying Taguchi design and large-scale strategy for mycosynthesis of nano-silver from endophytic Trichoderma harzianum SYA.F4 and its application against phytopathogens. Sci Rep. 2017, 7, 45297.

(31)

Taguchi, G., Introduction to quality engineering: designing quality into products and processes.

(32)

Petriz, B. A.; Franco, O. L., Metaproteomics as a complementary approach to gut microbiota in

Asian Productivity Organization: Tokyo, Japan, 1986; pp 1-191. health and disease. Front. Chem. 2017, 5, 4. (33)

Mayers, M. D.; Moon, C.; Stupp, G. S.; Su, A. I.; Wolan, D. W., Quantitative metaproteomics and activity-based probe enrichment reveals significant alterations in protein expression from a mouse model of inflammatory bowel disease. J. Proteome Res. 2017, 16, (2), 1014-1026.

(34)

Tanca, A.; Manghina, V.; Fraumene, C.; Palomba, A.; Abbondio, M.; Deligios, M.; Silverman, M.; Uzzau, S., Metaproteogenomics reveals taxonomic and functional changes between cecal and fecal microbiota in mouse. Front. Microbiol. 2017, 8, 391.

(35)

Daniel, H.; Gholami, A. M.; Berry, D.; Desmarchelier, C.; Hahne, H.; Loh, G.; Mondot, S.; Lepage, P.; Rothballer, M.; Walker, A.; Böhm, C.; Wenning, M.; Wagner, M.; Blaut, M.; Schmitt-Kopplin, P.; Kuster, B.; Haller, D.; Clavel, T., High-fat diet alters gut microbiota physiology in mice. ISME J. 2014, 8, (2), 295-308.

(36)

Vermeiren, J.; Van den Abbeele, P.; Laukens, D.; Vigsnæs, L. K.; De Vos, M.; Boon, N.; Van de Wiele, T., Decreased colonization of fecal Clostridium coccoides/Eubacterium rectale species from ulcerative colitis patients in an in vitro dynamic gut model with mucin environment.

FEMS Microbiol. Ecol. 2012, 79, (3), 685-696. (37)

Zhang, X.; Ning, Z.; Mayne, J.; Moore, J. I.; Li, J.; Butcher, J.; Deeke, S. A.; Chen, R.; Chiang, C.-K.; Wen, M.; Mack, D.; Stintzi, A.; Figeys, D., MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota. Microbiome 2016, 4, (1), 31.

(38)

Xiao, L.; Feng, Q.; Liang, S.; Sonne, S. B.; Xia, Z.; Qiu, X.; Li, X.; Long, H.; Zhang, J.; Zhang, D.; Liu, C.; Fang, Z.; Chou, J.; Glanville, J.; Hao, Q.; Kotowska, D.; Colding, C.; Licht, T. R.; Wu, D.; Yu, J.; Sung, J. J. Y.; Liang, Q.; Li, J.; Jia, H.; Lan, Z.; Tremaroli, V.; Dworzynski, P.; Nielsen, H. B.; Backhed, F.; Dore, J.; Le Chatelier, E.; Ehrlich, S. D.; Lin, J. C.; Arumugam, M.; Wang, J.; Madsen, L.; Kristiansen, K., A catalog of the mouse gut metagenome. Nat Biotech

2015, 33, (10), 1103-1108. (39)

Cox, J.; Hein, M. Y.; Luber, C. A.; Paron, I.; Nagaraj, N.; Mann, M., Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics 2014, 13, (9), 2513-2526.

(40)

Starr, A. E.; Deeke, S. A.; Ning, Z.; Chiang, C.-K.; Zhang, X.; Mottawea, W.; Singleton, R.; Benchimol, E. I.; Wen, M.; Mack, D. R.; Stintzi, A.; Figeys, D., Proteomic analysis of ascending


Page 22 of 25

Page 23 of 25

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


colon biopsies from a paediatric inflammatory bowel disease inception cohort identifies protein biomarkers that differentiate Crohn's disease from UC. Gut 2016, 66, (9), 1573-1583. (41)

Hrydziuszko, O.; Viant, M. R., Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline. Metabolomics 2012, 8, (1), 161-174.

(42)

Szymańska, E.; Saccenti, E.; Smilde, A. K.; Westerhuis, J. A., Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics 2012, 8, (Suppl 1), 3-16.

(43)

Venkata Mohan, S.; Sirisha, K.; Sreenivasa Rao, R.; Sarma, P. N., Bioslurry phase remediation of chlorpyrifos contaminated soil: Process evaluation and optimization by Taguchi design of experimental (DOE) methodology. Ecotoxicol. Environ. Saf. 2007, 68, (2), 252-262.

(44)

Buchfink, B.; Xie, C.; Huson, D. H., Fast and sensitive protein alignment using DIAMOND. Nat

Methods 2015, 12. (45)

Morris, J. H.; Kuchinsky, A.; Ferrin, T. E.; Pico, A. R., enhancedGraphics: a Cytoscape app for enhanced node graphics. F1000Research 2014, 3, 147.

(46)

Smoot, M. E.; Ono, K.; Ruscheinski, J.; Wang, P. L.; Ideker, T., Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27, (3), 431-432.

(47)

Roy, R., A primer on the taguchi method. Van Nostrand Reinhold: USA, New York, 1990; pp 261.

(48)

Flint, A.; Stintzi, A.; Saraiva, L. M., Oxidative and nitrosative stress defences of Helicobacter and Campylobacter species that counteract mammalian immunity. FEMS Microbiol. Rev. 2016, 40, 938-960.

(49)

Moon, J.-H.; Lee, J.-H.; Lee, J.-Y., Microarray analysis of the transcriptional responses of

Porphyromonas gingivalis to polyphosphate. BMC Microbiol. 2014, 14, (1), 218. (50)

Hare, N. J.; Scott, N. E.; Shin, E. H. H.; Connolly, A. M.; Larsen, M. R.; Palmisano, G.; Cordwell, S. J., Proteomics of the oxidative stress response induced by hydrogen peroxide and paraquat reveals a novel AhpC-like protein in Pseudomonas aeruginosa. PROTEOMICS 2011, 11, (15), 3056-3069.

(51)

Hecker, M.; Schumann, W.; Völker, U., Heat-shock and general stress response in Bacillus

subtilis. Molecular Microbiology 1996, 19, (3), 417-428. (52)

Qiao, Y.; Sun, J.; Ding, Y.; Le, G.; Shi, Y., Alterations of the gut microbiota in high-fat diet mice is strongly linked to oxidative stress. Appl. Microbiol. Biotechnol. 2013, 97, (4), 1689-1697.

(53)

Morgan, X. C.; Tickle, T. L.; Sokol, H.; Gevers, D.; Devaney, K. L.; Ward, D. V.; Reyes, J. A.; Shah, S. A.; LeLeiko, N.; Snapper, S. B.; Bousvaros, A.; Korzenik, J.; Sands, B. E.; Xavier, R. J.; Huttenhower, C., Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 2012, 13, (9), R79-R79.

(54)

Qin, J.; Li, Y.; Cai, Z.; Li, S.; Zhu, J.; Zhang, F.; Liang, S.; Zhang, W.; Guan, Y.; Shen, D.; Peng, Y.; Zhang, D.; Jie, Z.; Wu, W.; Qin, Y.; Xue, W.; Li, J.; Han, L.; Lu, D.; Wu, P.; Dai, Y.; Sun, X.; Li, Z.; Tang, A.; Zhong, S.; Li, X.; Chen, W.; Xu, R.; Wang, M.; Feng, Q.; Gong, M.; Yu, J.; Zhang, Y.; Zhang, M.; Hansen, T.; Sanchez, G.; Raes, J.; Falony, G.; Okuda, S.; Almeida, M.; LeChatelier, E.; Renault, P.; Pons, N.; Batto, J.-M.; Zhang, Z.; Chen, H.; Yang, R.; Zheng, W.; Li, S.; Yang, H.; Wang, J.; Ehrlich, S. D.; Nielsen, R.; Pedersen, O.; Kristiansen, K.; Wang, J., A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012, 490, (7418), 55-60.



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

(55)

Xiao, Y.; Cui, J.; Shi, Y.-H.; Sun, J.; Wang, Z.-P.; Le, G.-W., Effects of duodenal redox status on calcium absorption and related genes expression in high-fat diet fed mice. Nutrition 26, (11), 1188-1194.

(56)

Silverman, M.; Simon, M., Flagellar rotation and the mechanism of bacterial motility. Nature

1974, 249, (452), 73-74. (57)

Amsler, C. D.; Cho, M.; Matsumura, P., Multiple factors underlying the maximum motility of

Escherichia coli as cultures enter post-exponential growth. J. Bacteriol. 1993, 175, (19), 6238-6244. (58)

Voigt, B.; Schweder, T.; Sibbald, M. J. J. B.; Albrecht, D.; Ehrenreich, A.; Bernhardt, J.; Feesche, J.; Maurer, K.-H.; Gottschalk, G.; van Dijl, J. M.; Hecker, M., The extracellular proteome of Bacillus licheniformis grown in different media and under different nutrient starvation conditions. Proteomics 2006, 6, (1), 268-281.

(59)

Hong, P.-Y.; Croix, J. A.; Greenberg, E.; Gaskins, H. R.; Mackie, R. I., Pyrosequencing-based analysis of the mucosal microbiota in healthy individuals reveals ubiquitous bacterial groups and micro-heterogeneity. PLoS ONE 2011, 6, (9), e25042.

(60)

Van den Abbeele, P.; Belzer, C.; Goossens, M.; Kleerebezem, M.; De Vos, W. M.; Thas, O.; De Weirdt, R.; Kerckhof, F.-M.; Van de Wiele, T., Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. ISME J 2013, 7, (5), 949-961.

(61)

Nava, G. M.; Friedrichsen, H. J.; Stappenbeck, T. S., Spatial organization of intestinal microbiota in the mouse ascending colon. ISME J. 2011, 5, (4), 627-638.

(62) Ottman, N.; Huuskonen, L.; Reunanen, J.; Boeren, S.; Klievink, J.; Smidt, H.; Belzer, C.; de Vos, W. M., Characterization of outer membrane proteome of Akkermansia muciniphila reveals sets of novel proteins exposed to the human intestine. Front. Microbiol. 2016, 7, 1157. (63)

Stahl, M.; Friis, L. M.; Nothaft, H.; Liu, X.; Li, J.; Szymanski, C. M.; Stintzi, A., l-Fucose utilization provides Campylobacter jejuni with a competitive advantage. PNAS 2011, 108, (17), 7194-7199.

(64)

Tailford, L. E.; Crost, E. H.; Kavanaugh, D.; Juge, N., Mucin glycan foraging in the human gut microbiome. Front. Genet. 2015, 6, 81.

(65)

Juge, N., Microbial adhesins to gastrointestinal mucus. Trends Microbiol. 2012, 20, (1), 30-39.

(66)

Erickson, A. R.; Cantarel, B. L.; Lamendella, R.; Darzi, Y.; Mongodin, E. F.; Pan, C.; Shah, M.; Halfvarson, J.; Tysk, C.; Henrissat, B., Integrated metagenomics/metaproteomics reveals human host-microbiota signatures of Crohn’s disease. PLoS ONE 2012, 7, e49138.

(67)

Vizcaíno, J. A.; Csordas, A.; del-Toro, N.; Dianes, J. A.; Griss, J.; Lavidas, I.; Mayer, G.; Perez-Riverol, Y.; Reisinger, F.; Ternent, T.; Xu, Q.-W.; Wang, R.; Hermjakob, H., 2016 update of the PRIDE database and its related tools. Nucleic Acids Research 2016, 44, (Database issue), D447-D456.


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For TOC only 32x19mm (300 x 300 DPI)

ACS Paragon Plus Environment

Evaluating in Vitro Culture Medium of Gut Microbiome with Orthogonal

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