Bacteriocins are believed to play a key role in defining microbial diversity in different ecological niches. Here, we describe an efficient procedure to assess how bacteriocins affect gut microbiota composition in an animal model.
Very intriguing questions arise with our advancing knowledge on gut microbiota composition and the relationship with health, particularly relating to the factors that contribute to maintaining the population balance. However, there are limited available methodologies to evaluate these factors. Bacteriocins are antimicrobial peptides produced by many bacteria that may confer a competitive advantage for food acquisition and/or niche establishment. Many probiotic lactic acid bacteria (LAB) strains have great potential to promote human and animal health by preventing the growth of pathogens. They can also be used for immuno-modulation, as they produce bacteriocins. However, the antagonistic activity of bacteriocins is normally determined by laboratory bioassays under well-defined but over-simplified conditions compared to the complex gut environment in humans and animals, where bacteria face multifactorial influences from the host and hundreds of microbial species sharing the same niche. This work describes a complete and efficient procedure to assess the effect of a variety of bacteriocins with different target specificities in a murine system. Changes in the microbiota composition during the bacteriocin treatment are monitored using compositional 16S rDNA sequencing. Our approach uses both the bacteriocin producers and their isogenic non-bacteriocin-producing mutants, the latter giving the ability to distinguish bacteriocin-related from non-bacteriocin-related modifications of the microbiota. The fecal DNA extraction and 16S rDNA sequencing methods are consistent and, together with the bioinformatics, constitute a powerful procedure to find faint changes in the bacterial profiles and to establish correlations, in terms of cholesterol and triglyceride concentration, between bacterial populations and health markers. Our protocol is generic and can thus be used to study other compounds or nutrients with the potential to alter the host microbiota composition, either when studying toxicity or beneficial effects.
Bacteriocins are antimicrobial peptides produced by a wide range of bacterial species1,2. These compounds and their producers, especially LAB, have been explored and exploited worldwide for decades for their potential applications in food preservation and medicine3. Several bacteriocins are known to kill important pathogens, including species of Listeria, Enterococcus, Staphylococcus, and Bacillus. Some bacteriocins even have the ability to modulate the immune response4. Many bacteriocins have relatively narrow spectra, a property that is much appreciated in some applications. For example, some narrow-spectrum bacteriocins can be used to direct specific activity against selected groups of problematic bacteria, without much disturbance on the commensal or beneficial flora sharing the same niche; this is especially essential in the gut environment, where numerous beneficial microbes thrive in an interactive and dynamic manner5. Bacteriocins are also very attractive for prophylactic or probiotic use, as they can suppress the (out)growth of pathogens, pathobionts, or opportunistic bacteria that may unbalance the gut homeostasis6,7.
In terms of their nature and physicochemical properties, bacteriocins are very diverse, as they have different structures, target specificities, modes of action, etc. Most bacteriocins have been studied in great detail in in vitro settings, but very few have been tested in food matrices8,9 or in vivo, such as in an animal gut6,10. The in vitro properties can differ to a great extent when assessed in vivo due to the complexity of the gut environment and also to putative unintended effects on beneficial bacteria. Most probiotics are LAB. They produce an array of other metabolites, including short-chain fatty acids, which are known to influence the physiology of the host, as well as to demonstrate antimicrobial properties toward certain bacteria. Therefore, in the case of probiotic strains that produce bacteriocins, it is best to establish realistic assays, such as healthy animals with normal microbiota.
In the present study, we provide a strategy that allows for the assessment of the effect of different bacteriocin-producing strains, whose bacteriocins have different inhibitory spectra, on healthy mice. Our strategy includes feeding mice with isogenic non-bacteriocin mutants, which enables the differentiation of bacteriocin-mediated effects from non-bacteriocin-mediated effects. Sequencing 16S rDNA allows for following the dynamic changes of the bacterial population in the gut. Subsequent statistical analysis deciphers correlations between bacterial species and also between bacterial species and measured physiological parameters (e.g., bodyweight, serum biochemical parameters, etc.). We believe that the protocol presented in this study is also applicable to other probiotic or prebiotic applications beyond the study of bacteriocins in live animals.
Care and handling must be carried out at a specialized animal care unit. Procedures described here were approved by the corresponding Ethics Committee of the University of Valencia and local authorities, following the principles of laboratory animal care mandatory by European Union Law and 2010/63/EU and the Spanish Government RD 53/2013 on the protection of animals used for scientific purposes, in order to respect the 3R principle in animal experimentation (Replacement, Reduction, Refinement).
1. Frozen Bacterial Cultures Used to Inoculate Mice
2. Mice Assay and Experimental Design
Note: Specific pathogen-free (SPF) BALB/c young female mice (6 – 8 weeks) are needed for this experiment; here, a total of 100 animals were purchased.
3. Collecting Samples
4. LAB Counting and Bacteriocin Activity
5. DNA Extraction, 16S rDNA Amplification, and Sequencing
NOTE: Steps for DNA extraction are described for the use of a commercial kit (e.g., Realpure "SSS" Kit).
6. 16S rRNA Gene Amplification and Sequencing
7. Data Analysis and Statistics
The production of bacteriocins has been considered a positive probiotic feature in LAB, as it was assumed to prevent the growth of opportunistic bacteria and pathogens. The aim of this work was to show the capacity of bacteriocins to modulate gut microbiota populations in a mouse model. For this purpose, a procedure was developed to compare the effect of the intake of bacteriocin-producing strains and their isogenic non-producing strains. The procedure for the inoculation and the time extension of the assay are shown in Figure 1. Mice were grouped into eleven cages: a control without any treatment, five cages treated with bacteriocin-producer strains, and five cages treated with the corresponding isogenic mutants that have reduced or no bacteriocin production. The tested bacteriocin-producing LAB, as well as the respective counts (cfu/mL) in drinking water and counts in feces, are summarized in Table 1. The determination of bacteriocin production by LAB recovered from feces is illustrated in Figure 2. There were no significant differences between the bacteriocin treatments and the isogenic strains when all samples and all bacterial groups were considered together (Figure 3). However, there were likely differences when particular bacterial genera were studied independently, as shown in Figure 4. Furthermore, Pearson's correlation analysis revealed significant linkage between different bacterial genera and a correlation between bacterial genera and triglycerides and LDL in serum, as shown by the network built in CoNet in Figure 5.
Figure 1: Scheme Showing the Time Course Design of the Experiment. Please click here to view a larger version of this figure.
Figure 2: Detection of Bacteriocin Production in LAB Recovered from Feces by a Three-layer Protocol (as Described in the Procedure). Please click here to view a larger version of this figure.
Figure 3: Comparison of the Composition of Bacteria Treatments. A PCoA plot was generated based on the calculated distances in an unweighted UniFrac matrix. Different time points from the same sample (Figure 1) have the same color, and each color identifies a treatment group. For clarity of the illustration, only four strains are shown: garvicin and sakacin producer and non-producer strains (see the legend in the figure to relate color and treatment). The time of the samples is annotated as T0 (before inoculation), 7d (7 days), 14d (14 days), 21d (21 days), and 28d (28 days). For statistics, see the Procedure. Adapted from Umu et al., 201612. Please click here to view a larger version of this figure.
Figure 4: Changes in the Relative Abundance of LAB and Bacteriocin-targeted Bacterial Groups at the Genus Level during the Treatment and Post-treatment Periods. Changes in the relative abundances of genera in treatments, obtained with respect to time 0, were compared to those in the control group (CON). For clarity of the illustration, only four strains are shown: garvicin and sakacin producer and non-producer strains (see the legend in the figure to relate color and treatment). Error bars represent the standard deviation. Significance degree is represented as follows: P <0.1 with dot (.); P <0.05 with one star (*); P <0.01 with two stars (**). Adapted from Umu et al., 201612. Please click here to view a larger version of this figure.
Figure 5: Correlation Network of Relative Abundances of OTUs on Day 14 and Serum Levels. The correlations were calculated using Pearson's correlation in CoNet. Only the significant ones (P <0.05) are shown on the network. Parameters determined in serum – total cholesterol (Chol), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides (Trig – are shown by one color (gray), while OTUs belonging to different families are represented by different colors (see the legend). Positive correlations are displayed with green edges, and negative correlations with red edges. OTUs on the nodes are represented with OTU numbers or the genus to which they belong. Adapted from Umu et al., 201612. Please click here to view a larger version of this figure.
Strain | Inoculated in drinking water | Lactic acid bacteria counts in feces | Indicator strains | |
CFU/ml (log10) | CFU/g (mean log10) | |||
CONTROL | —- | 10 mice | ND | |
Lactobacillus sakei B1500 (sakA +) | 1.6 | 9 mice | 8.53 ± 0.18 | Enterococcus faecium P21 (LMG 2783) |
Lactobacillus sakei B1501 (sakA -) | 2.0 | 9 mice | 8.22 ± 0.05 | |
Pediocccus acidilactici B1502 (ped +) | 26 | 9 mice | 8.73 ± 0.12 | Enterococcus faecium P21 (LMG 2783) |
Pediocccus acidilactici B1503 (ped -) | 25 | 9 mice | 8.75 ± 0.09 | |
Enterococcus faecium B1504 (L50 wt +) | 6 | 9 mice | 8.83 ± 0.14 | Pediococcus damnosus (LMG 3397) |
Enterococcus faecium B1505 (L50 cured -) | 10 | 9 mice | 8.72 ± 0.14 | |
Lactobacillus plantarum B1507 (planta +) | 26 | 9 mice | 9.14 ± 0.14 | Lactobacillus spp. 965 (LMG 2003) |
Lactobacillus plantarum B1508 (planta -) | 23 | 9 mice | 8.60 ± 0.18 | |
Lactococcus garviae B1515 (GarML +) | 17 | 9 mice | 8.41 ± 0.08 | Lactococcus lactis IL1403 (LMG2705) |
Lactococcus garviae B1516 (GarML -) | 17 | 9 mice | 8.05 ± 0.18 | |
Enterococcus faecium P21 (LMG 2783) for SakA and PedPA-1, Pediococcus damnosus (LMG 3397) for enterocins, Lactobacillus spp. 965 (LMG 2003) for plantaricins, and Lactococcus lactis IL1403 (LMG2705) for GarML. |
Table 1: Inoculated Strains and the Counts in Fecal Samples.
The procedure described here has been used to determine whether changes in the microbiota are bound to health or age. Different parts of the protocol are important, but among them, sampling the feces, choosing the DNA fragment to be sequenced and analyzed, and performing the DNA extraction and bioinformatic analysis could certainly be the most critical points. Sampling is crucial because, for ethical reasons, mice should not be stressed and because it is known to change the proportion of bacteria in the gut. Samples must be processed as soon as possible or should be frozen until use, as some bacteria are very sensitive to dehydration and/or to oxygen. The selection of the DNA fragment to be sequenced from the amplified 16S rRNA genes is a crucial point. In this work, the variable regions V3 – V4 were selected25. The importance of the DNA extraction method must not be underestimated, as bacterial cells present in the samples are not all equally sensitive to different lysis protocols, and biases in the estimation of the bacterial populations can result from incomplete lysis. Therefore, the introduction of a bead beater step is required. Isogenic strains can be constructed by genetic knockdown or by plasmid curation, although in the case of strains, refractory to transformation chromosome-encoded bacteriocins may represent a problem. This procedure can be adapted using different specific culturing techniques, such as anaerobic methods26.
The procedure can be modified in most steps, depending upon the type of factor changes to be studied. The inoculation procedure could be shortened, but changes in serum parameters may need some time to develop. A possible limitation of the method could be the study of bacterial strains (nutrients or compounds) that cannot survive several hours in water or that may clump (or precipitate for compounds). Hence, this is a factor that must also be considered.
Until now, bacteriocin activity was generally determined in vitro27,28,29 or in mixed cultures30. This method has allowed for the in vivo monitoring of the activities of five bacteriocins from the whole-gut microbiota of standard laboratory mice. Small variations in populations could be determined, as well as their influence on other bacterial groups or physiological factors, like serum parameters12. Bacteriocins have a relatively narrow activity spectrum, inhibiting the growth of taxonomically related bacteria. However, those used in this assay are among the most effective, as some of them are active against Staphylococcus aureus, Listeria monocytogenes31, and even some strains of Escherichia coli, as in the case of sakacin A32. When inoculated in mice, LAB producing bacteriocins did not give rise to remarkable changes in the global microbiota composition, but rather to a reduced number of bacterial taxa. Nevertheless, global correlation network analysis revealed interesting ecological compatibilities and antagonism between different bacteria. This method could be adapted to analyze the effect of food constituents, drugs, or other compounds, although the expected outcome may be deliberately restricted to preserve healthy conditions.
The authors have nothing to disclose.
The authors wish to thank the EEA Grant NILS Science and Sustainability Coordinated Mobility of Researchers (reference 017-ABEL-CM-2013). C.B. and G.P.-M. were supported by the AGL2015-70487-P grant from the Spanish Ministry of Economy and Competitiveness. O.C.O.U. and D.B.D. were supported by a strategic scholarship program for food science research from the Norwegian University of Life Sciences (NMBU) (project 1205051025). We would also like to thank Inmaculada Noguera for her assistance with animal care and sampling and Jesus Dehesa for his help with ensuring the availability of laboratory materials in the animal facility. We also appreciate Professor Lars-Gustav Snipen for his advice on statistics.
Balb/c mice (female) | Harlan | Mice should be 6 – 8 weeks of age | |
Plastic Petri dish | Thermo Scientific | 101VR20 | |
Brain-Heart-Infusion broth | Conda | 1400.00 | |
European Bacteriological Agar | Pronadisa | 1800.00 | |
Agarose D1 Low EEO | Pronadisa | 8010.00 | |
1XTAE buffer | Thermo Scientific | 15558042 | |
MRS broth | Difco | 288130 | |
PBS tablets | Sigma | P4417-100TAB | |
scale | Mettler Toledo | PB602-S | |
sterile forceps | Levantina de Laboratorios S.L. | 260-3710014 | |
Microcentrifuge | Eppendorf | 5424 | |
Centrifuge | Hermle | Z383K | |
sodium chloride | AppliChem Panreac | 121659.1211 | |
Realpure SSS Kit | Real Life Science Solutions, Durviz, Spain | RBME04 (300 ml) | |
Isopropanol | AppliChem Panreac | 131090.1611 | |
Ethanol | AppliChem Panreac | 131086.1214 | |
Qubit fluorometer | Invitrogen | ||
Qubit dsDNA HS Assay Kit | Invitrogen | Q32851 | |
AMPure XP beads | Beckman Coulter Genomics, USA | A63881 (60 ml) | |
PerfeCta NGS library quantification kit | Quanta BioSciences, Maryland, USA | 733-2300 | |
MiSeq v3 reagent kit | Illumina, San Diego, California, USA | MS-102-3003 | |
Primers for 16S rRNA gene amplification | Primers contain V3-V4 region of bacterial 16S rRNA gene and Illumina overhang adaptors:5’-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG-3’ and 5'-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C-3’ | ||
Nextera XT Index kit | FC-131-1002 | Indices and Illumina sequencing adaptors | |
Micropestle for 1.5 ml tubes, Eppendorf / Sigma , Ref. | Sigma | Z317314-1PAK | |
Glass beads, 0.1 mm diameter | Biospec Products | 11079-101 | |
NucleoSpin Gel and PCR Clean-up Kit | Macherey-Nagel | 740609.25 | |
Omni Bead Ruptor 24 | Omni International Inc. | 19-040 | |
mutanolysin | Sigma | M9901-10KU | |
lysozyme | Roche | 10837059001 | |
proteinase K | Roche | 3115887001 | |
Rnase A | Sigma | R4875 |