The mammalian gastrointestinal tract (GIT) is settled by a wide range of different microorganisms that coexist in a sensitive ecosystem and mutual cooperation, whereas bacteria predominate. Within the GIT, the microorganisms are not only living on the nutrients and energy out of the diet, but they also produce several substances such as vitamins, organic acids, secondary bile acids, and gases. Their products and the simple existence of microorganisms show an influence on host health, whereas changes in diet, environmental stress, and disease can change the microbiome within the GIT [1
The pig (Sus scrofa
) is one of the most important farm animals in today’s agroeconomy, with the swine industry expanding worldwide. Achieving balance between today’s meat production methods and animal welfare has proven to be difficult. To enhance the pig’s health situation—the production efficiency as well as the product quality—it is important to understand the intestinal environment, especially the interactions among microorganisms and between microorganisms and their host [2
]. The predominant aim of intestinal microbiome investigations in farm animal sciences is to determine a balanced microbial composition to optimize animal health, performance, and pathogen resistance [4
], but also to investigate which microbiome composition maximizes the benefits and minimizes the costs of animal husbandry [5
]. Also, the feed industry is interested in establishing or preserving this microbiota by developing feed additives, diets, and other interventions [3
]. In addition, the pig seems to be interesting for human medical purposes as it reveals similarities in size, immunobiology, distribution of lymphocytes [6
], microbial ecosystems [7
], physiology, and disease development to humans [9
]. Thereby, pigs can potentially be used as model animals for human biology [10
The important interface between the microorganism and the host in the GIT is the mucus which functions as a barrier between feed, microorganisms, and the animal [11
]. It is produced by exocrine glands and the goblet cells, which are located in the mucosal epithelium and protects the former against injuries, but also against chemical and physical forces. Mucosa consist of a complex mixture of large glycoproteins (so-called mucins) water, electrolytes, separating epithelial cells, and enzymes, but also secreted immunoglobulins and antimicrobial molecules [12
]. The mucus contains bacteria and cell debris, since the mucus is the first connecting site between host and bacteria. In previous years, the mucus-associated microbiota in pigs became of interest and were studied in regards to dietary effects and age-related changes [13
]. The overall intestinal bacterial phyla in pigs are headed by Firmicutes, Bacteroidetes, Proteobacteria, and Spirochaetes, whereas Fibrobacteres, Actinobacteria, Tenericutes, Synergistetes, and Planctomycetes account for less than 1% of all 16S rRNA gene sequences [13
Since more than a decade, metaproteomics has been used to examine microbial proteins in different sample types to identify and quantify metabolic proteins and the pathways they are involved in [22
]. Microbial protein and host protein co-extraction is an intrinsic bias whose effect can only be minimized, not avoided [23
]. Although it was tried to prioritize microbial protein extraction, co-extracted host proteins have been used to concurrently study the metabolic status of the host [6
]. Thus, a benefit can be found in identifying the bacterial and host proteins in one run which gives new insights to both parts from exactly the same sample.
The present work attempted to detect the active bacterial fraction of the pig’s microbiome along five different sections (stomach, ileum, jejunum, cecum, and colon) by considering both luminal (digesta) and mucosal compartments of each section. A concomitant surplus is the identification and description of the porcine proteome to follow host functions.
2. Material and Methods
2.1. Animal Experiment and Sampling
All experiments and care of animals were approved by the local authorities (Regional Commission of Stuttgart, permit number: V308/13 TH) in accordance with the German Welfare Legislation. This study was generated in addition to the study of Heyer et al. [25
], from which detailed trial operations can be taken. Pigs (German Landrace × Piétrain, initial body weight 54.7 kg ± 4.1 kg) were randomly assigned to four experimental diets. Diets were formulated to meet or exceed the animal’s nutrient requirement and differ among each other in the protein source and the calcium and phosphorous (CaP) levels. Two out of the four diets contain the low digestible (LD) corn-field peas meal as a protein source, whereas the remaining two diets comprise the high digestible (HD) corn-soybean meal as a protein source. Each of these dietary groups was further supplied with high and low CaP levels. Diets with high and low CaP level were formulated to contain respectively 120% and 66% of the requirement for 50–75 kg pigs [26
After a feeding period of 9 weeks, including an adaptation of 19 days to the diets, one female pig per diet was anesthetized and euthanized by intravenous injection via the ear vein with pentobarbital (about 70 mg/kg BW, CP-Pharma, Handelsgesellschaft mbH, Burgdorf, Germany). Mucosae from the stomach (Pars nonglandularis), and both digesta and mucosae from jejunum (80 cm from the Plica ileocecalis), ileum (20 cm from the Plica ileocecalis), cecum, and the mid-colon were aseptically collected by scraping the mucosal layer from the tissue with a sterile glass slide and stored at −80 °C.
2.2. GC Analysis of Short Chain Fatty Acids
Concentration of short chain fatty acids (SCFA) in jejunal and cecal samples was analyzed by gas chromatography according to Wischer et al. (2013) [27
]. Briefly, SCFA were directly measured in a gas chromatographer equipped with a flame ionization detector (HP 6890 Plus; Agilent, Waldbronn, Germany). GC-grade short chain fatty acids (Fluka, Taufkirchen, Germany) were used as internal standards. Measurements were performed in a capillary column (HP 19091F-112, 25 m × 0.32 mm × 0.5 μm) by following the given program: 80 °C, 1 min; 155 °C in 20 °C/min; 230 °C in 50 °C/min., constant for 3.5 min., carrier gas: helium. Short-chain fatty acid concentration is scored as referred to kilogram sample.
2.3. Sample Preparation
The sample preparation was carried out after the method of Apajalahti et al. [28
]. Each sample containing 5 g of fresh substance was resuspended in 10 mL of washing buffer. The following steps of protein extraction, quantification, digestion, and peptide purification were performed as previously described by Tilocca et al. [29
2.4. LC-MS/MS Analysis
Purified peptides were analyzed using an EASY-nLC 1000 system coupled to a Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Peptides were injected and separated into an EASY-Spray analytical column (2 µm, 100 Å PepMap RSLC C18, 25 cm × 75 µm, Thermo Fisher Scientific) using the following 235 min gradient: 2–10% solvent B in 100 min; 10–22% solvent B in 80 min; 22–45% solvent B in 55 min; 45–90% solvent B in 5 min, 15 min isocratic at 90% solvent B; 90–2% solvent B in 1 min; re-equilibration at 2% solvent B for 40 min. Solvent A contained 0.5% acetic acid and solvent B consisted of 0.5% acetic acid in ACN/H2O (80/20). The flow rate was 250 nL/min and the column temperature was constantly kept at 35 °C.
The MS spectra (m/z
= 300–1600) were ascertained at a resolution of 70,000 (m/z
= 200) using a maximum injection time (MIT) of 50 ms and an automatic gain control (AGC) value of 1 × 106
. The internal calibration of the Orbitrap analyzer was conducted consulting lock-mass ions from ambient air following the method of Olsen et al. [30
]. The 10 highest peptide precursors were used for data dependent MS/MS spectra. Therefore, high energy collision dissociation (HCD) fragmentation was used with the following settings: resolution 17,500; normalized collision energy of 25; intensity threshold of 2 × 105
. For fragmentation, only ions with charge states between +2 and +5 were chosen. Therefore, an isolation width of 1.6 Da was set. AGC was adjusted at 1 × 106
whereas MIT was set at 50 ms for each MS/MS scan. To prevent further fragmentation, it was decided to eliminate fragmented precursor ions for 30 s within a 5 ppm mass window.
2.5. Data Analysis
The raw files from the mass spectrometric measurements were analysed by MaxQuant (v 188.8.131.52, Max Planck Institute of Biochemistry, Munich, Germany) using the database consisting of sequences of the Sus scrofa
genome (61,019 entries, March 2016) and an in-house database of bacterial proteins (14,535 entries, October 2015) identified by a two-step search approach in a previous study analyzing 84 porcine fecal samples [31
]. Protein grouping node was activated with the default software settings. The data analyses failed to include protein entries from dietary intake, leading to non-identified proteins, which are probably dominant in the digesta samples.
Phylogenetic distribution of the bacterial proteins was assessed on the basis of the identified peptides with Unipept [32
]. This tool provides the phylogenetic assessment of the bacterial community up to strain level depending on amino acid sequence homologies. In the present study, protein identification is done at phylum and family level as deeper taxonomic levels are omitted due to a low peptide-to-protein ratio and the risk of false positive identification. Calculation of alpha diversity and statistical evaluation was done with Primer-E (v. 6, primer-e, Auckland, New Zealand), by first standardizing the peptide datasets and afterwards creating a lower triangular resemblance matrix (resemblance measure S17 Bray Curtis similarity). The functional classification of the bacterial proteins was performed by the categorization into COG classes through the WebMGA online tool. [33
]. Proteins descending from the pig were categorized and illustrated using proteomaps [34
2.6. PRIDE Accession
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [35
] partner repository with the dataset identifier PXD011360 (login Username: [email protected]
, Password: a1lkk6Ch)
The current study employs a metaproteomic approach to investigate the porcine microbial community associated with diverse GIT sections, both in its mucosal and luminal compartment. The obtained results highlight a clear alteration between the small and the large intestine, which was evident at the bacterial phylogenetic level and by the distribution of functional protein clusters. This underlines the physiological differences between these two segments. Bacterial proteins in mucosa and digesta differed between sections of the pig in phylogeny and protein functions. A higher diversity of bacterial proteins was found in digesta samples compared to mucosa samples, albeit this might be an effect of a lower protein identification rate on the mucosal site. In general, from proximal to distal sections, more proteins and peptides were found. The metaproteomics approach is assumed to be the “keystone to ecosystematic studies” of environments and their associated microbial communities. Besides the sole consideration of the prokaryotic proteins in an organismal sample, this study showed the benefit to use the so far interference proteins of the host to reveal insight into the host metabolism. However, this method shows multiple challenges within several steps of the analytical workflow. Until now, a lot of information is lost by the data analysis process, since not all proteins or peptides can be identified and annotated to a function or phylogeny. Nevertheless, with this possible output, the present study gives us the first glimpse into the active microbiome of the porcine GIT and the host proteins. This may help us to identify key players or biomarkers as targets to design therapeutic intervention systems for various fields of application. Nevertheless, we retain that further investigations involving a higher number of animals and an integrative approach with other Omics methods (e.g., metagenomics or metatranscriptomics) will further facilitate the understanding and interpretation of the biology of the pigs’ gut and its associated microbial communities.