Effects of Bacterial Colonization on the Porcine Intestinal Proteome

Effects of Bacterial Colonization on the Porcine Intestinal Proteome Marianne Danielsen,†,‡ Henrik Hornshøj,§ Richard H. Siggers,|,⊥ Bent Borg Jensen,† Andrew G. van Kessel,⊥ and Emøke Bendixen*,† Department of Animal Health, Welfare and Nutrition, Faculty of Agricultural Sciences, University of Aarhus, 8830 Tjele, Denmark, Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense M, Denmark, Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, 8830 Tjele, Denmark, Human Nutrition Department, Preventative Nutrition, Copenhagen University, 1870 Frederiksberg C, Denmark, and Department of Animal and Poultry Science, University of Saskatchewan, Canada Received January 23, 2007

The gastrointestinal tract harbors a complex community of bacteria, of which many may be beneficial. Studies of germ-free animal models have shown that the gastrointestinal microbiota not only assists in making nutrients available for the host but also contributes to intestinal health and development. We studied small intestinal protein expression patterns in gnotobiotic pigs maintained germ-free, or monoassociated with either Lactobacillus fermentum or non-pathogenic Escherichia coli. A common reference design in combination with labeling with stable isobaric tags allowed the individual comparison of 12 animals. Our results showed that bacterial colonization differentially affected mechanisms such as proteolysis, epithelial proliferation, and lipid metabolism, which is in good agreement with previous studies of other germ-free animal models. We have also found that E. coli has a profound effect on actin remodeling and intestinal proliferation, which may be related to stimulated migration and turnover of enterocytes. Regulations related to L. fermentum colonization involved individual markers for immunoregulatory mechanisms. Keywords: iTRAQ • gnotobiotic • germ-free • quantitative proteomics • LC-MS/MS • Escherichia coli • Lactobacillus fermentum • gut • small intestine • pig

Introduction The mammalian gastrointestinal tract harbors an abundant and diverse microbial community as evidenced by numerous recent molecular analyses.1,2 To date, investigation of hostmicrobial relationships has primarily been focused on pathogenicity, due to the obvious and sometimes catastrophic harm to the host. More recently, however, it has become increasingly clear that contributions of the nonpathogenic microbiota (normally described as commensals) play an important role for gut health, function, and development. For recent reviews in this field, see refs 3-5, 7. It has been estimated that the “microbiome”, the sum of intestinal bacterial genomes, contains up to 100 times more genes than the human genome.6,23 The intestinal microbiome permits synthesis of a large variety of microbial products * To whom correspondence should be addressed. Dr. Emøke Bendixen, Department of Animal Health, Welfare and Nutrition, Faculty of Agricultural Sciences, University of Aarhus, Research Centre Foulum, Blichers Alle´, P.O. Box 50, DK-8830 Tjele, Denmark. E-mail, [email protected]; Telephone number, + 45 8999 1246; Fax, + 45 8999 1500. † Department of Animal Health, Welfare and Nutrition, University of Aarhus. ‡ University of Southern Denmark. § Department of Genetics and Biotechnology, University of Aarhus. | Copenhagen University. ⊥ University of Saskatchewan.

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through the metabolism of dietary constituents and host secretions. These microbial products are recognized by a number of host response pathways that have only recently been identified.8,9 Thus, the intestinal microbial community has a profound impact on the physiology of the mammalian host as demonstrated clearly in studies comparing conventionally reared and germ-free animals.10,11 Subsequently, colonization of germ-free animals with different commensal bacterial species has permitted identification of species specific host response pathways. The documented roles of commensal bacteria in different aspects of gastrointestinal function include angiogenesis,12 innate immunity,13,14 lipid metabolism,15,16 and glycan receptor structure.17-19 Furthermore, there is growing evidence that commensal bacteria also have an impact on systemic health, as changes in the microbiota composition are speculated to be related to the increasing incidences of allergic and autoimmune diseases seen in the Western world.5,20-22 Genome-wide elucidation of host response pathways in gnotobiotic (germ-free and ex-germ-free animals with a defined microbiota) animal models has so far been limited to investigation at the transcription level.8,14,15,24,25 The aim of this study was to analyze the host responses in a porcine gnotobiotic model at the proteome level. We studied quantitative protein expression in the small intestine of germ-free pigs and pigs monoassociated with either of two commensal bacterial species 10.1021/pr070038b CCC: $37.00

 2007 American Chemical Society

Porcine Gut Proteome

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commonly found in the neonatal pig intestine: Lactobacillus fermentum or a nonpathogenic strain of Escherichia coli. The use of isobaric tags for relative and absolute quantitation (iTRAQ), together with an experimental design including a common reference sample, allowed quantitative protein expression comparison of gut tissues from a total of 12 animals distributed into the 3 treatment groups. Our results revealed that E. coli specifically seemed to have a profound effect on cell proliferation and enterocyte migration, whereas L. fermentum may have an immunoregulatory role. This is the first time host responses to gut colonization have been studied at the proteome level. Our results showed a surprisingly good correlation with other studies across different host animals, microbiota, and methods of analysis.

Experimental Section Animal Model. The pigs (Large White × White Duroc) were delivered by caesarean section and aseptically transferred into 1 of 3 gnotobiotic isolators with 4 pigs in each, balanced for litter of origin and sex. All animals were bottle-fed at 3-h intervals with a 1:1 mixture of Similac (milk formula containing 4.7 g/100 mL protein; 12.2 g/100 mL lipid; 24.3 g/100 mL carbohydrate; Abbott Labaratories, Abbott Park, IL) and sterilefiltered porcine serum for the first 24 h postpartum, to ensure a supply of porcine immunoglobulins. For the rest of the trial, the pigs were fed at 8-h intervals with a 2:1 mixture of milk formula and water so that the troughs contained milk formula at all times. For more details on the experimental procedures, see Shirkey et al.26 One isolator was maintained germ-free (GF), whereas the pigs in the two other isolators were monoassociated with grampositive L. fermentum (LF) and nonpathogenic gram-negative E. coli (EC), respectively. L. fermentum and E. coli inoculants were isolated from cecum of a healthy adult pig and cultured for 18 h at 37 °C in a tryptic soy broth (BBL, Sparks, MD). Each pig was inoculated with 2 mL of their respective bacterial culture, which was added to the bottle during feeding at 24 and 30 h postpartum. The L. fermentum and E. coli cultures contained 108 and 109 colony-forming units (CFU)/mL, determined by viable cell counts.26 The microbial status of each animal was monitored routinely by culturing feces samples. Furthermore, cecal contents were collected at slaughter for bacterial identification and enumeration. It was confirmed that the GF group was maintained germfree throughout the study, and that the LF and EC groups were monoassociated with Lactobacillus sp. and Escherichia sp., respectively.26 Tissue Collection. The pigs were euthanized by CO2 asphyxiation and exsanguinated on day 13. A 10-cm tissue section was excised at 50% of the length of the small intestine (SI), rinsed with cold physiologic saline (Bimeda-MTC, Cambridge, Canada), blotted dry on paper, frozen in liquid nitrogen, and stored at -80 °C for proteome analysis.26 Protein Extraction. Tissue samples (10 cm) were ground with a mortar and pestle in liquid nitrogen and a subsample (200 mg) was homogenized in 1 mL TES buffer (10 mM TrisHCl, pH 7.6; 1 mM EDTA, 0.25 M sucrose) using a TissueLyser (Qiagen, Switzerland). Homogenized samples were centrifuged at 10 000× g for 30 min at 4 °C. The supernatant was stored at -80 °C until use. Protein concentrations were determined using the Pierce BCA Protein Assay Kit (Bie and Berntsen, Rodovre, Denmark) using BSA as standard, according to the manufacturer’s manual.

Figure 1. Each iTRAQ 4-plex contained a common reference sample (label 114), a germ-free tissue sample (label 115), a L. fermentum monoassociated tissue sample (label 116), and an E. coli monoassociated tissue sample (label 117). The reference sample was constructed as a pool of aliquots from all 12 tissue samples. In all, 4 iTRAQ experiments were conducted to allow individual analyses of all 12 experimental gut tissues.

Protein Digest and Peptide Labeling with iTRAQ Reagents. In addition to the 12 samples, a “common reference” sample was created by pooling small intestinal tissue from all 12 experimental samples. This reference sample was divided into 4 identical samples, one for each 4-plex of iTRAQ. The 16 samples were treated in parallel for sample preparation and labeling of peptides with iTRAQ. The protein in each sample was precipitated using ice cold acetone. Stock reagents, buffers, and the four isobaric tagging reagents were obtained from the iTRAQ Reagent Multi-plex kit (Applied Biosystems, Forster City, CA), and labeling was performed according to the manufacturer’s manual. In brief, the precipitated protein (100 µg) was resuspended in 20 µL of digestion buffer (0.5 M TEAB, 0.1% SDS). Cystein residues were reduced with 2.5 mM TCEP (60 °C for 1 h) and then blocked with 10 mM methylmethanethiosulfate (MMTS) at room temperature for 10 min. The proteins were digested with trypsin (Applied Biosystems, Forster City, CA) and incubated at 37 °C over night. Each sample was passed through a 0.2 µm centrifuge filter (National Scientific Company, Rockwood, TN) for 5 min at 5.000× g to remove all impurities that could interfere with later HPLC separation. Isobaric tagging iTRAQ reagent (1 unit in ethanol) was added directly to the protein digests and incubated at room temperature for 1 h. The 4 GF samples were labeled with mass 115, the 4 LF samples with mass 116, and the 4 EC samples with mass 117. The 4 reference samples were labeled with mass 114 and then pooled for normalization. Samples were combined in 1:1:1:1 ratios into 4-plexed samples, each containing a common reference sample and a sample from each treatment group (Figure 1). The use of a common reference sample allowed comparison of the protein expression ratios across multiple 4-plexes. After labeling, the 4-plexed samples were dried down and stored at -80 °C. SCX Fractionation. The iTRAQ labeled samples were redissolved in 0.03% FA, 5% acetonitrile in water. Fifty micrograms (50 µg) of protein digest were injected into an Agilent 1100 Series capillary HPLC equipped with a Zorbax Bio-SCX Series II, 0.8 × 50 mm column (Agilent Technologies, Palo Alto, CA). Peptides were eluted with gradient of increasing NaCl (0 min, Journal of Proteome Research • Vol. 6, No. 7, 2007 2597

research articles 0% B; 5 min, 0% B; 10 min, 1.5% B; 16 min 4% B; 30.5 min, 10% B; 40.5 min, 50% B; 61 min, 100% B; 81.5 min, 100% B). Buffer A contained 0.03% FA and 5% acetonitrile in water, buffer B contained 0.03% FA, 5% acetonitrile and 1M NaCl in water and the flow rate was 20 µL/min. Fractions were collected for every 1 min for 60 min and pooled into 10 samples. LC-MS/MS. The SCX fractions were further separated on an Agilent 1100 Series nanoflow HPLC system (Agilent Technologies, Palo Alto, CA) prior to mass spectrometric identification. The systems were equipped with an isocratic pump working at 20 µL/min (0.1% FA and 3% acetonitrile in water) for fast sample loading onto an enrichment-column (Zorbax 300SB C18, 0.3 × 5 mm, 5 µm particle, Agilent Technilogies, Palo Alto, CA). This ensured sample de-salting and concentration. The enrichment column was then switched into the nanoflow path (300 nL/min), the peptides were eluted and further separated on an analytical column (Zorbax 300SB C18, 75 µm × 150 mm, 3.5 µm particles) with a gradient of increasing organic solvent (0 min, 5% B; 7 min, 5% B; 70 min, 40% B; 73 min, 95% B; 78 min, 95% B; 83 min, 5% B; 100 min, 5% B). Buffer A contained 5% acetonitrile, 0.1% FA in water and buffer B 5% water, 0.1% FA in acetonitrile. The eluted peptides were sprayed through a nanospray needle (Fused Silica Emitters, OD 360 µm, ID 75 µm, Proxeon Biosystems, Odense, Denmark) directly into the Q-star XL mass spectrometer (Applied Biosystems, Forster City, CA). Database Searching and Data Analysis. The raw data files were searched with the ProQUANT 1.0 software (Applied Biosystems) using the ProGroup algorithm for protein grouping and confidence scoring and searched against the mammalian KBMS3.0.2004121 protein database from Celera Discovery Systems (Applied Biosystems). There was no processing (e.g., smoothing) of the raw data files prior to database searching. The database allowed for iTRAQ reagent labels at N-terminal residues, internal K and Y residues, and MMTS-labeled cysteine as fixed modifications, deamidation, O-phosphorylation (STY), and oxidation (M) as variable modifications and one missed cleavage. Search parameters within ProQUANT were set with an MS tolerance of 0.15 Da and an MS/MS tolerance of 0.1 Da. Each 4-plex was SCX-fractionated and analyzed twice to gain higher reproducibility and proteome coverage as recommended by Chong et al. (2006).27 The two data sets from each iTRAQ experiment were pooled in ProGroup Viewer (Applied Biosystems). Confidence of protein identification was selected in ProQUANT to a ProtScore of 1.3, equivalent to 95% confidence and a minimum of two peptides per protein. To compare the quantitative information across multiple iTRAQ experiments, the iTRAQ ratios were set relative to 114, the common reference sample. The four raw data excel files corresponding to the four iTRAQ experiments were converted into four tab-delimited text files and concatenated into a single data tab-delimited text file. Log2 ratios were calculated for all ratios relative to the common reference sample 114. The data text file was transposed into a log2 ratio data matrix with Panther ID (www.pantherdb.org) as the row protein ID and 12 columns representing the 12 combinations of the four iTRAQ experiments and the three treatments. All data processing was carried out using Perl scripting. Identification of differentially expressed proteins between sample pairs from the three different treatments was carried out in R 2.3.1, a statistical programming environment (www.r-project.org) using the limma package from Bioconductor.28 For each protein Panther ID, contrasts and t-tests were made between the two treatments 2598

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Danielsen et al. Table 1. Proteins Identified Across the 4 iTRAQ Experimentsa proteins identified in

proteins

all 4 iTRAQ experiments 3 iTRAQ experiments 2 iTRAQ experiments only 1 iTRAQ experiment total

286 104 125 253 768

a Proteins were grouped by Panther ID to eliminate redundancy in protein naming. Only proteins identified in at least 3 individual iTRAQ experiments (a total of 390 proteins) were considered for further data analysis. The complete number of proteins identified in each iTRAQ experiment prior to grouping by Panther ID is provided in Supporting Information Table S1.

in each possible pair of comparisons LF/GF, EC/GF and EC/ LF. Finally, top tables with significantly (P e 0.05) regulated proteins were generated. Only proteins that were significantly regulated in at least 3 of the 4 iTRAQ 4-plexes for a given comparison were considered as differentially expressed. In the end, a list of 61 Panther proteins was identified with significant expression differences in the three treatment comparisons. We used the annotated mammalian database from Celera Discovery Systems for database searching. However, because the porcine genome is not fully represented in this database, many of the proteins were assigned to human and mouse proteins. A top-scoring protein found in all iTRAQ experiments may therefore have different protein names and accession numbers. Grouping of proteins across multiple iTRAQ experiments by Panther ID links these proteins together; however, different protein isoforms and similar but not identical proteins may also be grouped together. Manual inspection of data for the differentially expressed proteins ensured that only one protein was present in each Panther ID group, although the existence of protein isoforms was not ruled out.

Results and Discussion Common Reference Design Allows Comparison between Multiple iTRAQ Analyses. In this study, tissue samples from a total of 12 pigs, divided into 3 different treatment groups, were compared. An overview of the experimental design and iTRAQ labeling scheme is shown in Figure 1. In principle, iTRAQ-based experiments allow a direct comparison of up to 4 individual samples and thereby 4 experimental conditions.29 Hence, to study a larger animal population, it has been common practice to pool several individual animal samples according to treatment groups, thereby keeping the comparative study within the scale of a single iTRAQ 4-plex. In this study, we have chosen to use a common reference design, which allows the pairwise comparison of individual proteome profiles of all the 12 tissue samples, as explained in Figure 1. The common reference design implies that 4 sets of iTRAQ 4-plexes were needed to analyze the 12 samples, in that every iTRAQ 4-plex contained the same common reference sample, consistently labeled with iTRAQ-114. Hence, all protein expression levels obtained from all the 12 tissue samples were related to the expression levels of one common reference sample. This common reference sample was constructed by pooling of aliquots from all of the 12 individual tissue samples from the experimental model, to make sure that all proteins could be matched and quantitated within each and every 4-plex. Because all 4-plexes contained the common reference sample labeled with mass 114, this experimental design allowed comparison across different sets of iTRAQ 4-plexes. All 4 individual tissue samples in each of the 3 treatment groups were consistently labeled with the same

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Table 2. Differentially Regulated Proteins (P < 0.05) Are Based on Pair Wise Comparison between Treatment Groupsa LF/GF protein name D-amino

acid oxidase (Z) Ornithine aminotransferase Carbamoyl-phosphate synthase Ubiquitin-conjugating enzyme E2N (Z) Ubiquitin carboxylterminal proteinase Glutamate carboxypeptidase 40S ribosomal protein S11 40S ribosomal protein S16 Elongation factor TU Aldehyde dehydrogenase 1A1 Glyceraldehyde phosphate dehydrogenase Fructose-bisphosphate aldolase A Hexosaminidase A Dihydrolipoamide S-succinyltransferase 2-oxoglutarate dehydrogenase Acyl-CoA -binding protein Glycerol kinase 15-oxoprostaglandin 13-reductase 2,4-dienoyl CoA reductase Fatty acid-binding protein, epidermal Fatty acid-binding protein, hepatic (M) Glycerol-3-phosphate dehydrogenase Farnesyl diphosphate synthase (M, Z) Vitamin D-dependent calcium-binding protein Calnexin (Z) Annexin A13 Membrane metallo endopeptidase Serine protease, MSTP034 Cathepsin D Proteasome activator complex Peptidase 4 Collagen-binding protein 2 APRIL Pcph proto-oncogene protein Predicted histone H3 Ribonucleoprotein, mCBP

total peptides

3 14 33

prot score

panther id

accession

P.value

Amino acid and protein metabolism 2.0 CF11530:SF10 pdb|1AN9_A 9.12 CF11986:SF63 gb|AAB35211.1 34.66 CF10034:SF1

spt|P31327

6

4.33 CF11621:SF25

rf|XP_136032.2

2

2.00 CF10589:SF10

pir|B40085

10

10.01 CF11014:SF16

rf|XP_214525.2

4

2.01 CF11448:SF2

rf|XP_195399.2

5

1.85 CF11545:SF4

rf|XP_236683.1

6

log2 ratio

EC/GF log2 ratio

P.value

EC/LF log2 ratio

P.value

-0.3121 0.0334 -0.5206 0.0006 -0.3768 0.0064

-0.2139 0.0354 -0.2799 0.0091 0.2233 0.0188 0.3055 0.0376 -0.3380 0.0158 -0.3920 0.0068 0.2511 0.0270 0.3543 0.0166

14 16

4.01 CF16868:SF1 rf|NP_003312.3 Carbohydrate metabolism 19.24 CF11699:SF120 sp|P51977 23.43 CF10836:SF15 prf|681085A

0.2576 0.0301 0.4661 0.0263

0.2324 0.0454 0.4225 0.0394 -0.6651 0.0207

17

20.73 CF11627:SF13

0.2863 0.0354

0.3774 0.0306

4 4

2.01 CF11478:SF6 6.00 CF11499:SF30

11

sp|P00883 trm|Q91XG3 rf|XP_216753.2

7 6 6

5.42 CF11569:SF1 spt|Q02218 Lipid metabolism 4.01 CF10702:SF5 pir|NZPG 2.62 CF10196:SF21 trm|Q8IVR5 5.41 CF11695:SF18 trm|O62642

2

2.00 CF11906:SF111 trm|Q9DCI7

4

2.64 CF11623:SF17

10 10

16.62 CF11623:SF4

spt|Q01469

trm|Q14702

4

4.0

trm|Q8WMY2

6

Calcium regulation 6.03 CF11639:SF6 sp|P02632

13 7

-0.5299 0.0098 -0.5569 0.0075

-0.2924 0.0300

0.4122 0.0340 -0.2515 0.0464 -0.3305 0.0128 0.3242 0.0208

0.2636 0.0489

-0.4922 0.0297

0.5681 0.0083 -0.4157 0.0495

3

2.00 CF11802:SF1

gb|AAG39285.1

-0.7713 0.0030 -0.7000 0.0052

6 13

6.00 CF13683:SF88 13.26 CF10660:SF3

pir|KHPGD spt|P58238

-0.4235 0.0243

4 10

4.01 CF11737:SF9 rf|XP_214900.2 17.40 CF11461:SF24 spt|P50454 Cell proliferation 2.01 CF11375:SF4 emb|CAA69265.1 2.64 CF11782:SF7 trm|Q96RX0

11 4

2.51 CF11426:SF2 4.29 CF10288:SF9

rf|XP_344596.1 emb|CAA53546.1

-0.3567 0.0428

0.3753 0.0455

-0.3600 0.0480

5 6

0.3215 0.0209

-0.3975 0.0316 -0.4398 0.0203

8.57 CF11073:SF2 pir|A37273 6.01 CF10502:SF18 rf|XP_343246.1 Proteolysis/ apoptosis 2.03 CF11733:SF48 rf|NP_036740.1

12

-0.2313 0.0348 -0.4266 0.0447

pir|S45379

7.68 CF11728:SF17 CF11525:SF1

0.4172 0.0484

-0.3094 0.0481

0.2659 0.0374 0.2377 0.0192 0.2429 0.0432 0.4284 0.0456 -0.2895 0.0472 -0.3051 0.0386 0.4375 0.0395 0.2413 0.0219

0.4664 0.0304

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Table 2. (Continued) LF/GF protein name

Adenylate kinase 2 Nucleoside diphosphate kinase B23 nucleophosmin Villin 1 (M) Gelsolin (M) Profilin Tropomyosin (Z, M) cAMP-dependent protein kinase YWHAZ protein p96 protein Haptoglobin Fetuin Alpha-1 acid glycoprotein Immunoglobulin gamma-chain (M) Hemoglobin alpha chain Hemoglobin beta chain Chloride intracellular channel protein 1 Serotransferrin Heat shock protein 70 Galectin-3 Chromogranin A IDI1 protein Nuclear receptor binding protein Sulfotransferase (Z, M) Vesicle-associated membrane protein (Z, M)

total peptides

prot score

panther id

3 7

1.40 10.34

CF11667:SF31 CF11349:SF24

5

2.63

CF12182:SF1

9 6 4 26

9.40 3.54 9.40 25.23

3

2.15

14 8

16.48 2.03

6 7 8 3

4.31 10.64 16.00 2.00

trm|Q86V33 pir|A57542 Plasma proteins CF11408:SF22 spt|Q8SPS7 CF13814:SF3 pir|S22395 CF11967:SF1 trm|Q29014 CF10236:SF6 gb|AAA51295.1

8 12

12.20 24.00

CF11442:SF23 CF11442:SF37

7

6.40

CF11030:SF4

19

14.61

CF11485:SF18

31 3 10 4 5

38.76 6.00 4.17 2.01 2.01

CF11684:SF138 CF11346:SF12 CF10583:SF2 CF10885:SF1 CF13902:SF2

spt|P09571 Others dbj|BAA11462.1 rf|NP_002297.1 pir|A32284 gb|AAH06999.1 trm|Q99J45

7 7

1.55 4.45

CF11783:SF40 CF10809:SF3

trm|Q95JC6 rf|NP_003565.3

accession

rf|NP_776314.1 spt|Q01768

log2 ratio

P.value

0.4257

0.0443

gb|AAH09623.1 Actin remodeling CF11977:SF19 spt|P09327 CF11977:SF20 spt|P06396 CF13936:SF6 pdb|1PNE CF10561:SF21 rf|NP_003280.1 Signal transduction CF11635:SF16 trm|Q8K1M2 CF18860:SF34 CF11232:SF3

spt|P01965 pdb|2PGH_B Ion transport spt|O00299

0.5433

0.3685

0.3917

-0.3091

0.0122

0.0406

0.0077

0.0118

EC/GF log2 ratio

P.value

0.2135

0.0356

0.3040 0.3275 0.2387 0.2492

0.0120 0.0224 0.0110 0.0468

-0.2235

0.0436

-0.9332

0.0321

2.1097 0.3371 0.4200

0.0007 0.0287 0.0363

0.4551 0.3786

log2 ratio

P.value

0.6457 -0.4240

0.0470 0.0450

0.2789

0.0182

-0.4870

0.0433

1.6974

0.0032

0.7300

0.0368

-0.8910

0.0229

-0.2907

0.0359

-0.3973 0.4323

0.0058 0.0412

-0.5090

0.0482

0.0198 0.0394

0.5454

0.0408

0.3027

0.0257

-0.6575

0.0018

-0.3484

EC/LF

0.0058

a Protein grouping across the 4 iTRAQ experiments was done by Panther ID. Empty values in the table accentuates that these proteins did not meet the cutoff criteria (P < 0.05) for the given comparison. Proteins found in previous gnotobiotic gene expression studies are denoted (Z) zebrafish and (M) mouse.

iTRAQ label, so that all 3 experimental treatment groups were represented once in each iTRAQ 4-plex. Having several repeats within each treatment group (n ) 4) greatly enhances the analytical power of comparative proteomics and is essential in overcoming the biological variation which exists between individuals. Particularly when the number of biological replicates is limited, the impact of single outliers is minimized by analyzing the individual samples, rather than pooling groups of samples.30 Although this approach tends to reduce the amplitude of differential expression ratios, the confidence is strengthened by the multiple individual analyses. Protein Identification and Quantitation. As recommended by Chong et al. (2006),27 all iTRAQ 4-plexes were run twice to improve proteome coverage. Approximately 600 proteins were identified in each iTRAQ experiments. The numbers of proteins identified in the individual iTRAQ experiments, as well as in the merged files, are given in Table S1 (Supporting Information). Identification of porcine proteins was often based on homology matching to the more complete human and murine protein databases. However, this practice leads to a redundancy in protein naming in the final lists of identified proteins. To overcome this problem, it was advanta2600

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geous to group the identified proteins by Panther ID (www.pantherdb.org), as presented in Table 1. All differentially expressed Panther groups were confirmed manually, and groupings where inconsistency could lead to ambiguous protein identity were expelled from further data analysis. Table 1 shows the numbers and distributions of proteins identified across the individual iTRAQ experiments, and a total of 768 Panther proteins were identified. Only proteins which were identified in at least 3 of the iTRAQ experiments, were considered for further analysis of quantitative proteome data, hence a total of 390 proteins were analyzed for differential expression patterns. How to set relevant cutoff criteria for differentially expressed proteins and genes has been a much debated theme in particular in analyses of microarray data. For a recent review, see Chen at al. 2006.31 The data format of iTRAQ studies is highly comparable to microarray data formats, in that quantitation is based on relative expression ratios within a 4-plexed analysis. Therefore, based on experience from analyses of microarray data, we have chosen to use the p-value (P < 0.05) as cutoff criteria to select proteins that are significantly regulated.

Porcine Gut Proteome

research articles biological processes, were consistent with the previously presented observations from the gene expression analyses. The consistency is remarkable considering that the observations are bridged across different host animals, microbiota, and methods of analysis. The differentially expressed proteins observed in the current study were sorted into groups of biological processes and summarized in Table 2. Eleven of the protein changes observed in the pig model were conserved in the former gene expression analyses and are marked with an M (mice)14,23 and/or a Z (zebrafish)25 in Table 2.

Figure 2. Distribution of differentially regulated proteins (P < 0.05) between treatment group comparisons.

In all, a total of 61 proteins were found to be differentially expressed (P < 0.05) across the 3 treatment groups, as summarized in Table 2. To estimate how many of the identified proteins originated from the microbiota rather than from the porcine intestinal tissues, data files from one of the iTRAQ experiments, were searched in the Celera combined databases (combined KBMS3.0.2004121), which contain both mammalian and prokaryotic databases (E. coli is well represented). None of the identified proteins had a top-ranking match to any E. coli entries, indicating that microbial proteins were not present at a detectable level in the intestinal proteome samples. Some Host Responses Show Microbial Specificity, Others Are Conserved Regardless of Microbiota. The distribution of regulated proteins between treatment groups is shown in Figure 2. Comparisons of the germ-free vs E. coli monoassociated groups showed the highest number of regulated proteins, namely 37, of which 19 were found to be specific for the EC/ GF comparison (Figure 2). The germ-free vs L. fermentum monoassociation resulted in fewer changes in the proteome patterns and showed 22 regulated proteins, of which only 7 proteins were specific for the LB/GF comparison. Comparing L. fermentum vs E. coli animals revealed 24 regulated proteins, of which 14 were unique for this comparison. Overall, these observations are in agreement with previous morphometric and histological analyses, showing that intestinal responses to L. fermentum monoassociation were more similar to responses seen in germ-free pigs, whereas intestinal responses to E. coli more resembled pigs with conventional microbiota.26 These data also show that there is a microbial specificity for some host gut responses to monoassociation with L. fermentum and E. coli. Similar microbial specificity has been observed for some regulated genes found in microarray studies of monoassociated mice.8 However, this is the first time specific host responses of gut epithelium to individual microorganisms have been shown at protein level. Host Proteome Responses to Gut Colonization Correlate with Previous Gene Expression Studies. Similar gnotobiotic models have previously been studied, where germ-free mice were monoassociated with Bacteroides thetaiotaomicron8 and germ-free mice and zebrafish were colonized with conventional microbiota.14,25 In the monoassociation study of mice, 71 genes were found to be regulated,8 whereas 267 and 212 regulated genes were reported due to conventionalization of mice (jejunum)14 and zebrafish25, respectively. These data were made publicly available (http://gordonlab.wustl.edu and www.theaps.org/publications/pg), and we have compared these results with our proteomics data on monoassociated porcine intestinal tissue. We observed that some differentially regulated individual proteins, and to a larger extent proteins related to specific

We observed groups of proteins belonging to specific biological functions that were impacted by colonization, regardless of which bacteria species colonized the gut. In Figure 3 the expression data of the significantly regulated proteins for 5 selected biological functions is shown (data from Table 2). Proteins related to lipid metabolism, proteolysis, and apoptosis and plasma proteins were markedly affected by bacterial colonization compared to germ-free conditions. Apoptosis regulator activity was also reported by Mutch et al.14 to be specifically regulated in the jejunum of mice. Increased proteolytic and apoptotic activities, possibly a result of angiogenesis, could be related to the formation of capillary networks, which is well-known to be reduced in germ-free animals.12,23 This is supported by our observation that major plasma proteins such as hemoglobin, haptoglobin, immunoglobin G, fetuin and alpha-1 glycoprotein32 were all observed in elevated amounts in colonized tissues and in reduced amounts in germfree animals, suggesting an increased capillarization in colonized gut tissues. Lipid metabolism is another well-represented group among the differentially expressed proteins, which previously have been recognized by others as an altered physiological trait in germ-free models, including gene expression studies.15,16 Four individual proteins, farnesyl diphosphate synthetase, sulfotransferase, tropomyosin, and vesicle-associated membrane protein, are found to be differentially regulated in our proteome studies as well as in gene expression studies of mice and zebrafish. The lipid metabolism protein, farnesyl diphosphate synthetase, was found to be up-regulated in response to monoassociation with both L. fermentum and E. coli in the pig, as well as by monoassociation and conventionalization in the mice and zebrafish. Tropomyosin was also upregulated following colonization with E. coli in the current study, as well with Bacteroides thetaiotaomicron in mice8 and in conventionalized zebrafish.25 Vesicle-associated membrane protein was down-regulated, except in the mice study. Sulfotransferase was only significantly regulated between L. fermentum monoassociated and E. coli monoassociated pigs, and the direction of regulation was not conserved among the gene expression studies. This indicates that sulfotranferase expression, which is a key enzyme in the xenobiotic metabolism of the host,33 seems to be dependent on which microbial species are present in the gut and the host organism. Colonization with E. coli Induces Cell Proliferation and Actin Remodeling. Expression of proteins involved in actin remodeling was up-regulated by colonization of E. coli relative to germ-free tissue. However, no significant regulation of this biological process could be observed in gut tissues that were monoassociated with L. fermentum (Figure 3). Two of the actin remodeling proteins, tropomyosin and gelsolin, were also found to be up-regulated in gene expression studies from similar mouse and zebrafish models,8,25 indicating that induction of actin remodeling is species specific (such as E. coli and B. thetaiotaomicron). Actin remodeling is involved in enterocyte Journal of Proteome Research • Vol. 6, No. 7, 2007 2601

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Figure 3. Differential expression of proteins (log2 ratios) from 5 selected groups of biological function: actin remodeling, cell proliferation, lipid metabolism, plasma proteins, and proteolysis/apoptosis. The 3 panels represent the 3 treatment group comparisons: L. fermentum monoassociated vs germ-free gut tissue, E. coli monoassociated vs germ-free gut tissue, and E. coli monoassociated vs L. fermentum monoassociated gut tissue.

migration from villus crypts to villus tips.34 Villus length has previously been shown to be abnormally long in the germ-free and L. fermentum colonized tissue, whereas in E. coli colonized tissue the length was comparatively reduced.26 This germ-free type morphology is believed to be related to the reduced proliferation and enterocyte turnover rates observed in several studies.35-37 Also, previous studies of the pig model described here showed an increased abundance of proliferating cell nuclear antigens due to E. coli colonization, as well as in pigs with a conventional gut microbiota, while germ-free and L. fermentum colonized piglets did not show this trait.38 Our data support these observations as proteins related to cell proliferation (mainly identified as DNA/RNA metabolism proteins) were up-regulated in the EC/GF comparison, whereas only one cell 2602

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proliferation protein was affected in the LF/GF comparison (Figure 3). Furthermore, E. coli selectively induced the tumor necrosis factor family protein APRIL, known to be a proliferation-inducing ligand.39 Could Colonization with L. fermentum Induce Immune Regulation? Although colonization with E. coli had the most profound effect on germ-free tissue both in terms of morphology and protein expression, L. fermentum colonization induced regulations indicating that immunity and defense mechanisms are influenced by the presence of L. fermentum. Haptoglobin, an acute-phase plasma protein found to be highly expressed in pigs with bacterial infections and inflammation,40,41 was the most markedly induced protein in EC colonized tissue but was not detectable in LF tissue. Membrane

Porcine Gut Proteome

metallo endopeptidase was selectively reduced by L. fermentum colonization. The function of membrane metallo endopeptidase is not clear. It has, however, been shown to down-regulate the onset of inflammation in nematode infected mice,42 and also to inhibit angiogenesis.43 Galectin-3, which was found to be up-regulated in the L. fermentum group relative to the E. coli group, has previously been associated with both innate and adaptive immune responses,44 it is down-regulated in inflamed tissues from patients with inflammatory bowel disease45,46 and has recently been reported to be a marker for regulatory T-cells.47 The development of a balanced immune response is important for preventing allergies and autoimmune diseases (such as inflammatory bowel disease), and regulatory T-cells have recently been identified as key players in the control and suppression of immune responses.5,22 Furthermore, germ-free studies have shown that the presence of a microbiota is needed to obtain fully functional regulatory T-cells.48 These observations indicate that L. fermentum plays an immunoregulatory role in the gut. However, further investigations are needed to understand the mechanisms in more detail. In summary, we have conducted the first proteome study of germ-free intestinal tissue responses to bacterial colonization. Our results correlate well with similar studies across host animals, microbiota, and methods of analysis. In addition, the proteome studies revealed that some biological functions, such as lipid metabolism and the formation of capillary networks (proteolysis, apoptosis and plasma proteins), were affected by both bacterial species. E. coli specifically seemed to have a profound effect on cell proliferation and enterocyte migration (actin remodeling), whereas L. fermentum affected proteins related to immune response development. The experimental design we have used allowed us to study the individual animal samples without pooling of samples from the same treatment groups and may explain the good correlation to previous gene expression studies. Rather than showing large differences in protein expression levels, marker proteins are selected when showing consistent regulation in more animals.

Supporting Information Available: Number of identified proteins in ProQUANT in each iTRAQ experiments, and in the concatenated files (Table S1), MS/MS information on the list of differentially expressed proteins (Table S2), and list of all proteins identified in the analysis including quantitation information (Table S3). This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Eckburg, P. B.; Bik, E. M.; Bernstein, C. N.; Purdom, E.; Dethlefsen, L.; Sargent, M.; Gill, S. R.; Nelson, K. E.; Relman, D. A. Science 2005, 308 1635-1638. (2) Hill, J. E.; Hemmingsen, S. M.; Goldade, B. G.; Dumonceaux, T. J.; Klassen, J.; Zijlstra, R. T.; Goh, S. H.; Van Kessel, A. G. Appl. Environ. Microbiol. 2005, 71, 867-875. (3) Shi, H. N.; Walker, A. Can. J. Gastroenterol. 2004, 18, 493-500. (4) Backhed, F.; Ley, R. E.; Sonnenburg, J. L.; Peterson, D. A.; Gordon, J. I. Science 2005, 307(5717), 1915-1920. (5) MacDonald, T. T.; Monteleone, G. Science 2005, 307(5717), 19201925. (6) Relman, D. A.; Falkow, S. Trends Microbiol. 2001, 9, 206-208. (7) Hooper, L. V.; Midtvedt, T.; Gordon, J. I. Annu. Rev. Nutr. 2002, 22, 283-307. (8) Hooper, L. V.; Wong, M. H.; Thelin, A.; Hansson, L.; Falk, P. G.; Gordon, J. I. Science 2001, 291, 881-884. (9) Kelly, D.; Campbell, J. I.; King, T. P.; Grant, G.; Jansson, E. A.; Coutts, A. G.; Pettersson, S.; Conway, S. Nat. Immunol. 2004, 5, 104-112.

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