Article | Open

Bacteriophages as potential new mammalian pathogens

  • Scientific Reports 7, Article number: 7043 (2017)
  • doi:10.1038/s41598-017-07278-6
  • Download Citation
Published online:


Increased intestinal permeability and translocation of gut bacteria trigger various polyaetiological diseases associated with chronic inflammation and underlie a variety of poorly treatable pathologies. Previous studies have established a primary role of the microbiota composition and intestinal permeability in such pathologies. Using a rat model, we examined the effects of exposure to a bacteriophage cocktail on intestinal permeability and relative abundance of taxonomic units in the gut bacterial community. There was an increase in markers of impaired gut permeability, such as the lactulose/mannitol ratio, plasma endotoxin concentrations, and serum levels of inflammation-related cytokines, following the bacteriophage challenge. We observed significant differences in the alpha diversity of faecal bacterial species and found that richness and diversity index values increased following the bacteriophage challenge. There was a reduction in the abundance of Blautia, Catenibacterium, Lactobacillus, and Faecalibacterium species and an increase in Butyrivibrio, Oscillospira and Ruminococcus after bacteriophage administration. These findings provide novel insights into the role of bacteriophages as potentially pathogenic for mammals and their possible implication in the development of diseases associated with increased intestinal permeability.


Our knowledge of the intestinal microbiota has greatly expanded over the last decade, and growing evidence suggests that alterations of the intestinal microbiota are critical pathogenic factors that trigger various polyaetiological diseases associated with increased intestinal permeability and chronic inflammation1,2,3. Previous studies have established a clear correlation between chronic inflammation and a variety of pathologies, including neurodegenerative and cardiovascular diseases, cancer, inflammatory bowel disease (IBD), rheumatoid arthritis, diabetes, aging, among others4,5,6,7. The primary causes of chronic inflammation in the gut are activation of immunoreactive submucosa and endotoxemia, resulting from the impaired gut barrier function. Understanding the factors that modulate microbiota, leading to an increase in intestinal permeability, is essential to determine the direct and indirect causes of these diseases, which have emerged in Western countries as a major health issue during the past several decades, and to find new approaches to their treatment and prevention.

The microbiota of the human intestinal tract is comprised of bacteria, fungi, and viruses, including bacteriophages. This highly diverse and complex ecosystem is characterised by dynamic stability of each of its components in the context of the host organism. The gut microbiota directly or indirectly modulates all components of the gut barrier function, such as the mucus integrity and transcellular and paracellular transport8, 9. The human gut contains approximately 1015 bacteriophages, which >10 times of the number of bacterial cells and 100 times of the number of human cells10. Although recent studies have shown that the number of free lytic phages in the mammalian gastrointestinal tract is relatively low and prophages are dominant, phages are nevertheless considered important regulators of the microbiota stability11. Although it is established that regulators of microbiota can contribute to various pathological conditions in humans, and bacterial viruses are important regulators, there are very few studies of the role of bacteriophages in human health.

Until recently, bacteriophages have been considered not to be harmful to humans since they selectively interact with bacteria and do not affect eukaryotic cells.

Therefore, bacteriophages were used in a number of experimental and clinical therapeutic studies12, 13.

Given the critical role of microbiota alterations in the development of increased intestinal permeability, we hypothesised that bacteriophages as important regulators of the microbiota diversity may be implicated in mucosal impairment and thus can indirectly be pathogenic to mammals. We have first revealed that bacteriophages can cause alterations of the microbiota and increase intestinal permeability in a rat model and thus may be harmful for mammals14. Furthermore, we have proposed that the primary damage to the microbiota caused by phages should be regarded as a ‘microbiota disease’. We have previously reported that the challenge with bacteriophages is associated with altered intestinal permeability; however, some questions remained unanswered, in particular how microbial composition of the gut microbiota changes and whether it correlates with the increased intestinal permeability and endotoxemia. To investigate these issues, we carried out Illumina sequencing of the V3–V4 region of the 16S ribosomal RNA (rRNA) gene to compare the microbiota composition before and after a bacteriophage challenge and to uncover its role in the increased intestinal permeability and endotoxemia in rats15. This information can provide a basis for the crucial next step in the evaluation of bacteriophage implications in poorly treatable human diseases.


Bacteriophages increase gut permeability and mediate endotoxemia

The altered microbiota composition and dysfunctional intestinal barrier have emerged as potential triggers of the growing incidence of chronic diseases1. To investigate whether bacteriophages can cause a shift in microbiota, leading to increased gut permeability, we used a rat model to measure markers of intestinal permeability and endotoxemia. We examined gut permeability function in animals before and 10 days after the daily challenge with a bacteriophage cocktail using each animal as its own control.

All animals survived the entire duration of the experiment, with no clinical changes in the gastrointestinal tract or stool alteration. We did not detect statistically significant differences in the total weight at baseline and after bacteriophage treatment; however, a trend towards weight loss was registered. After bacteriophage administration, the average weight of each rat was 231 ± 45 compared with 269 ± 28 g in the pre-treatment period. At the same time, all animals showed unkempt hair coats and decreased activity starting from the 5th day of bacteriophage administration, which we believe was due to the induction of endotoxemia and impaired intestinal permeability16.

To assess the damage to the gastrointestinal tract, we measured intestinal permeability using a clinical test based on saccharide permeability, which measures the ratio of lactulose to mannitol after oral ingestion of the sugars17, 18. The animals showed higher gut permeability following the bacteriophage challenge, with an average post-treatment lactulose/mannitol ratio of 0.99 ± 0.08 compared with that of 0.31 ± 0.05 pre-bacteriophage treatment (p < 0.05) (Fig. 1A). Given that the altered intestinal permeability is commonly associated with endotoxemia, we next determined whether bacteriophages could cause the latter condition19. As shown in Fig. 1, the rats exhibited dramatically elevated serum levels of endotoxemia markers after the phage challenge, as compared to the pre-treatment baseline data. We found that the animals had higher levels of plasma lipopolysaccharide (LPS) after the bacteriophage challenge, with an average of 0.28 ± 0.07 endotoxin units (EU)/mL as compared with an average of 0.08 ± 0.02 EU/mL (p < 0.05) before the treatment (Fig. 1B). Accordingly, the levels of serum inflammatory cytokines were also elevated after the bacteriophage challenge20, 21. Treatment with phages resulted in a significant increase in the serum concentrations of tumour necrosis factor-alpha (TNF-α), interleukin (IL)-1β, and IL-6 (p < 0.05 compared with the baseline pre-treatment data; Fig. 1C–E).

Figure 1
Figure 1

Effects of bacteriophages on intestinal permeability and endotoxemia. Albino Wistar male rats (n = 5) were challenged with a bacteriophage cocktail for 10 days. Each animal was used as its own baseline control. Blood and urine samples were collected from all rats at the same time and subjected to various analytical tests as described in the Materials and Methods section. (A) Intestinal permeability expressed as the lactulose/mannitol ratio. Treatment with bacteriophages resulted in a significant alteration of intestinal permeability (p < 0.05). (B) Parameters of endotoxemia. Administration of bacteriophages increased the serum LPS levels (p < 0.05). (C–E) Serum levels of inflammation-related cytokines: (C) TNF-α, (D) IL-1β, and (E) IL-6. Data are expressed as the means ± standard error of the mean (SEM). The non-parametric paired Wilcoxon signed-rank test was applied to the analysis of pre- and post-challenge differences.

Bacteriophages alter intestinal microbial communities

To determine the effects of bacteriophages on the intestinal microbiota, we analysed the gut microbiota composition in the rats before and after the bacteriophage challenge using Illumina-based 16s rRNA sequencing. After quality control filtering, we obtained an average of 147,191 reads of the V3–V4 region of 16S rRNA genes per sample. Operational taxonomic units (OTUs) were defined as a set of sequence reads with the similarity cutoff of 97%22.

The sequence reads were then used to investigate whether there was an overall gain or loss of diversity by examining the alpha diversity, which represented a difference in the microbiome composition between groups at baseline and after administration of bacteriophages23. For this purpose, we calculated the species diversity (Shannon, Simpson, and inverse Simpson indices) and richness estimators (Chao 1 and ACE) pre- and post-phage challenge. We found an increase in the alpha diversity on day 10 of phage administration. The bacterial richness also differed, and the Chao 1 and ACE values, as well as the Shannon, Simpson and inverse Simpson indexes, increased following the phage challenge (Fig. 2). These data demonstrated that the faecal microbiota exhibited a rapid and marked increase in the overall microbial diversity after bacteriophage infection.

Figure 2
Figure 2

Alpha diversity before and after bacteriophage challenge as calculated by multiple diversity measures. Comparisons of the alpha diversity indexes revealed alterations after the bacteriophage challenge. (A) Bacterial richness across the samples was calculated using Chao 1 and ACE. (B) Bacterial diversity was evaluated using the Shannon, Simpson, and inverse Simpson parameters. p < 0.05 using t-test analysis.

The taxonomic identity of the reads was analysed using available annotation source databases. With OTUs at a relative abundance of ≥0.5% at the phylum level, Firmicutes was the predominant phylum in the untreated and bacteriophage-treated animals (Fig. 3A). At the phylum level, there were no significant changes in the untreated and bacteriophage-treated animals (Fig. 3A). However, analysis of all phylum level (OTUs > 0.001%) abundance data revealed a decreased abundance of Actinobacteria, Deferribacteres, and Proteobacteria after the bacteriophage challenge. Notably, three phyla, Spirochaetes, Tenericutes, and candidate division TM7, increased following bacteriophage treatment (Fig. 4A).

Figure 3
Figure 3

Comparison of relative abundance of predominant genera. Faecal bacterial communities were analysed by high-throughput sequencing of the 16S rRNA gene. Relative abundances of bacterial (A) phyla, (B) families, and (C) genera before and after the bacteriophage challenge at a level of ≥0.5% relative abundance. The term ‘other’ refers to the genera with the minimum abundance of <0.5%.

Figure 4
Figure 4

Bacteriophage challenge affects the gut bacterial community. Bacterial OTUs occurring at an abundance of >0.001% before and after bacteriophage challenge. Heatmap of the relative abundances of bacterial (A) phyla, (B) families, and (D) genera. Population scores of enriched taxa of bacterial (C) families and (E) genera that differed by at least two-fold (i.e. log2 = 1) between pre- and post-treatment samples. A positive log2-fold change value indicates that OTU is significantly enriched in samples, and a negative log2-fold change indicates that OTU is significantly depleted in post-treatment samples.

We next examined the compositional changes at the family level with taxa detectable at ≥0.5% (Fig. 3B). Consequently, we detected a decrease in the abundance of Bifidobacteriaceae, Clostridiaceae, Erysipelotrichaceae, Lachnospiraceae, Lactobacillaceae, and Prevotellaceae. However, post-bacteriophage samples revealed an increase of Veillonellaceae, Ruminococcaceae, and unclassified organisms after bacteriophage treatment. More detailed data were generated by the analysis of OTUs detectable at relative abundances of >0.001% (Fig. 4B). We evaluated the families differing by at least two-fold (i.e. log2 = 1) between pre- and post-treatment samples (Fig. 4C). The key two-fold alterations following the bacteriophage challenge included a decrease in Staphylococcaceae, Prevotellaceae, Lactobacillaceae, Lachnospiraceae, Erysipelotrichaceae, Coriobacteriaceae, and Bifidobacteriaceae and an increase in certain members of Prevotellaceae and Lachnospiraceae.

At the genus level, the treatment with phages resulted in substantial Blautia, Catenibacterium, Lactobacillus, and Faecalibacterium depletion and a simultaneous increase in Butyrivibrio, Oscillospira, Ruminococcus at a relative abundance of >0.5% (Fig. 3B). Notably, the number of unclassified bacteria significantly increased at the genus level.

In addition, we analysed the 16s rRNA data at the species level with OTUs detected at the level of abundance of >0.001% (Fig. 4D). The highest depletion at the genus level, >two-fold (i.e. log2 = 1), was registered post-bacteriophage treatment for the genera Clostridium, Eubacterium, Staphylococcus, Coprococcus, Lactobacillus, Collinsella, Blautia, Bifidobacterium, Dorea, and Prevotella. Simultaneously, certain members of the genera Ruminococcus and Prevotella were significantly enriched (Fig. 4E).

Overall, we determined that the faecal microbiota of animals exhibited distinct alterations in the bacterial composition following the treatment with bacteriophages.


The aims of the present study were to determine (1) whether bacteriophages could induce alterations of the gut flora, leading to gut leakiness and an impaired intestinal barrier; and (2) whether bacteriophage treatment could increase gut permeability in mammals, sufficient to facilitate endotoxemia and inflammatory responses.

Our knowledge of the particular role of intestinal permeability has greatly increased over the last years24, 25. Indeed, with the impaired gut permeability, bacterial antigens cross into the lamina propria, leading to endotoxemia and dysregulation of inflammatory responses, and trigger chronic inflammation, which in turn is an important factor for the development of various polyaetiological diseases20, 21. There is increased evidence showing that the concomitant gut barrier dysfunction may play a critical role in diseases such as Crohn disease, IBD, chronic fatigue syndrome, diabetes, autism, cancer, neurodegenerative and cardiovascular diseases3,4,5,6,7. At the same time, alterations in the gut microbiota, which is an important regulator of intestinal homeostasis, have been proposed to play a primary role in the impaired gut permeability and deleterious immune activation2. Therefore, understanding all the factors that modulate microbiota is essential for the prevention and therapy of a variety of poorly treatable diseases.

Bacteriophages are known to play an important role in microbiota homeostasis and therefore are involved in human health26. De Paepe et al. described different models including both direct and indirect effects of phages on humans. Indirect models are predominantly realized by the role of phages in dysbiosis, alterations of bacterial properties by transduction, and phage-mediated changes in recognition patterns of bacteria, resulting in an altered immune response. Direct models describe the direct modulation of immunity resulting from phage phagocytosis27.

Our previous results have indicated, for the first time, that bacteriophages might be a previously unrecognised cause of diseases in mammals, including humans. To our knowledge, our previous study was the first to indicate that bacteriophage treatment could lead to intestinal hyperpermeability in rodents14. We have shown that the challenge of animals with bacteriophages resulted in increased intestinal permeability and elevation of circulating immune complexes. This observation led us to define such alterations as ‘microbiota diseases’, which reflects the nature of the primary impairment caused by bacteriophages.

In the present study, we used the same rat model and treatment regimen to study, in more detail, the changes in the gut microbiota and endotoxemia following bacteriophage treatment. To understand the nature of impaired gut permeability in response to bacteriophage exposure, faecal material was collected from the same animals before and after the phage challenge. We used a bacteriophage cocktail comprised of commercially available and characterised phages active against the Enterobacteriaceae, Staphylococcaceae, Streptococcaceae, and Pseudomonadaceae families. Bacteriophages are known to selectively interact with bacteria and not affect eukaryotic cells. Thus, the impaired gut permeability can only be a consequence of altered microbiota12, 28.

We found an increased lactulose/mannitol ratio compared with that at baseline, which was determined prior to administration of the bacteriophages, indicating increased intestinal permeability following oral administration of bacteriophages. Our results also showed significantly elevated levels of blood serum endotoxin, TNF-α, IL-1β, and IL-6 compared to those in the pre-treatment period.

Consistent with other studies, which indicated that the alteration of gut microbiota by antibiotics might lead to impaired gut permeability, we showed for the first time that bacteriophages could induce inflammation, most likely through an increase in the circulating endotoxin level, which is likely to be a result of an altered intestinal microbiota and increased intestinal permeability29. There are different ways by which a disrupted gut barrier can lead to immune modulation; the most well described is stimulation of inflammatory responses by the intestinal-derived endotoxin (increased serum LPS levels), which subsequently results in elevated levels of inflammatory mediators18, 30.

In this study, we found that the bacteriophage challenge affected the microbial alpha diversity. Overall, we detected an increase in the richness and diversity of faecal microbiota, indicating that the total number of bacterial species increased after phage treatment compared to the baseline pre-treatment data. The increased richness of intestinal microbiota is considered one of the signatures of a leaky gut and a feature of intestinal inflammation. Similar patterns have been described in other diseases associated with increased intestinal permeability31, 32. Our study identified bacteria whose abundance changed following the bacteriophage challenge, and this alteration resulted in increased intestinal permeability, since phages selectively target bacteria with no effect on mammalian cells33, 34.

At the genus level, samples collected after the treatment with bacteriophages showed increases in Oscillospira and Butyrivibrio and decreases in Lactobacillus and Faecalibacterium; however, only Lactobacillus showed a two-fold change. The decreases in Lactobacillus and Faecalibacterium represent an important pattern, regarded as a signature of impaired gut permeability and inflammation35, 36, 37. Both bacterial genera are known to be beneficial for mammals, and studies have shown that their depletion is associated with barrier abnormalities. In a number of studies, both bacterial genera, which are considered effective anti-inflammatory microorganisms, were shown to restore the function of the intestinal barrier. Thus, Lactobacillus rhamnosus CNCM I-3690 and the commensal bacterium Faecalibacterium prausnitzii A2-165 exhibited similar protective effects against induced barrier hyperpermeability in mice.

It is interesting to note that the increase in relative abundance of the butyrate-producing bacteria Butyrivibrio, known to lead to decreased expression of proinflammatory cytokines and suppression of proinflammatory responses38, 39. Butyrivibrio are known to reduce bacterial translocation by potentiation of mucin synthesis, are beneficial for tight junctions, and are suggested to suppress intestinal hyperpermeability40.

Of note, the bacteriophages used in this study had no direct impact on the genera Lactobacillus, Faecalibacterium, and some others whose abundance was significantly altered. This is consistent with previous studies, which have shown that microbiota is characterized by temporal stability and dynamic equilibrium and its alterations result in complex and poorly predicted responses and consequences41.

It is likely that the decrease in the abundance of certain genera, which were directly affected by the phages, triggered a cascade of microbiota alterations, eventually leading to the depletion of Lactobacillus and Faecalibacterium species. This may be the result of alteration of the abundance of species having symbiotic associations with Lactobacillus and Faecalibacterium or of outgrowth of species having inhibitory interactions with these two species42.

An example of possible alterations of symbiotic properties is the decreased relative abundance of Streptococcus spp that are known to have a mutualistic relationship with certain Lactobacillus spp as a result of the direct impact of phages used in this study43.

In contrast, one of the possible inhibitory pathways may involve increased abundance of Helicobacter spp., whose antagonistic effects towards Lactobacillus were previously described44, 45. In turn, the abundance of Helicobacter is likely to be increased by the decrease of Enterobacteriacea, which is directly affected by the bacteriophage cocktail46.

On the other hand, participation of these species in the gut balance is well known, while relative contributions of other bacteria, whose abundance also changed in this study and which could regulate the intestinal barrier function, may be underestimated. Therefore, at this point the exact pathway of microbiota alteration that leads to gut leakiness remains unknown. However, this was not the goal of our study since bacteriophages are known to specifically target specific bacterial species, and therefore, other types of phages may lead to different microbiota alterations and consequences for the macroorganism.

The experimental model used allows to minimize possible direct effect of used phages on mammalian intestinal epithelium as there are numerous of available receptors of specific bacteriophage receptors on the surface of the bacteria of rats’ microbiota.

Collectively, our results warrant further research on microbiota diseases and previously underestimated consequences related to bacteriophage exposure in mammals, including humans. We suggest that the global distribution of bacteriophages, their high prevalence in the outer environment and microbiota, and their potential to alter gut permeability, which was revealed in this study, should drive future research regarding phage implications in a variety of emerging diseases. Although it is not possible to determine, based on this study, if the observed alterations occur in the natural environment, one may assume that under certain conditions, bacteriophage infections could be contagious and spread among mammalian hosts, triggering polyaetiological conditions. Follow-up studies on the role of bacteriophages should be considered for better understanding of the implication of phages in mammalian diseases and the spread of poorly treated diseases, associated with alterations of microbiota, in humans.

Materials and Methods


Healthy adult male Wistar rats (n = 5; 12-week-old, 240–280 g) were maintained in individual cages in a P3 room under a 12-h light/dark cycle, at a temperature of 22 to 25 °C and 60 ±  5% atmospheric humidity. All animals had free access to food and water according to the Guide for the Care and Use of Laboratory Animals47. An ethical approval was granted by the Human Microbiology Institute Ethics Committee (B12/2016).

The animals were monitored daily for any changes of their activity, behaviour, and general health status, including the weight loss, ruffled coat, wheezing or abnormal respiration, and the presence of unformed faeces. Body weights of all animals were measured using a digital balance (Mettler-Toledo, Inc., Columbus, OH, USA).


Lactulose (L7877, Sigma–Aldrich) and mannitol (M8429, Sigma–Aldrich) were utilised for all arms of the study.

Phages and experimental model

A commercial bacteriophage cocktail against Enterobacteriaceae, Staphylococcaceae, and Streptococcaceae families was used to induce microbiota diseases. It included a Salmonella bacteriophage cocktail from Microgen (Moscow, Russia; product batch number H20), containing bacteriophages against S. Paratyphi, S. Typhimurium, S. Heidelberg, S. Newport, S. Choleraesuis, S. Oranienburg, S. Infans, S. Dublin, S. Enteritidis, S. Anatum, and S. Newlands. Pyobacteriophage, another polyvalent commercial phage cocktail from Microgen (product batch number 4), contained phages against seven bacterial species, Staphylococcus aureus, Streptococcus pyogenes, Proteus mirabilis, Proteus vulgaris, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli48. Phages (1.5 mL, 1 × 106 plaque-forming units/mL of each phage cocktail) were added to drinking water according to the manufacturer’s instruction and administered orally for 10 days. Each animal was used as its own control before the bacteriophage challenge.

Measurement of gut permeability in vivo

Intestinal permeability in rats was assessed after 8-h fasting as previously described49. In brief, animals received 2.0 mL of a solution containing 120 mg of lactulose and 80 mg of mannitol by oral gavage. Two tests of intestinal permeability were performed in each animal, at baseline and 10 days after daily phage administration. Rats were placed individually in metabolic cages, and urine was collected for 24 h. Then, we measured the lactulose/mannitol ratio using gas chromatography as described previously50.

Serum markers of inflammation

Levels of TNF-α, IL-6, and IL-1β were measured in the serum using respective commercial rat-specific enzyme-linked immunosorbent assay (ELISA) kits (BioLegend, San Diego, CA, USA) according to the manufacturer’s instructions. LPS was measured using a Pyrochrome Limulus Amebocyte lysate kit (Associates of Cape Cod, Inc., East Falmouth, MA, USA) according to the manufacturer’s instructions.

Great care was taken to ensure aseptic collection and to avoid contamination with environmental LPS.

Venous blood samples were collected atraumatically and aseptically, with the skin shaved and sterilized with alcohol swabs. In brief, blood specimens were collected under pyrogen-free conditions and stored in LPS-free vials (Eppendorf, Germany) on ice51.

Plasma was centrifuged, transferred to a glass tube, and stored at −20 °C until analysis. Optical densities were measured by ELISA and read at 405 nm.

Comparative faecal microbiome analysis

Faecal samples were collected at baseline and after 10 days of the bacteriophage challenge into a sterile container and immediately stored at −80 °C until further processing. Bacterial DNA extraction was performed using a QIAamp stool DNA mini kit according to the manufacturer’s instructions (Qiagen, Germany). Sequencing libraries of the V3–V4 region were prepared according to the Illumina MiSeq system instructions52. In brief, the V3 and V4 regions of the 16S bacterial rRNA gene were amplified using a two-step polymerase chain reaction (PCR) protocol with V3 and V4 region primers (forward: 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′; reverse: 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′) for the first PCR and Nextera XT index primers for the second PCR. Amplicons were cleaned using AMPure XP magnetic beads, and then, Illumina sequencing adapters and dual-index barcodes were added to each amplicon. Libraries were assessed with the Qubit dsDNA HS assay kit (Thermo Fisher Scientific) and TapeStation high sensitivity D1000 ScreenTape (Agilent) and normalised and sequenced on an Illumina MiSeq instrument using a MiSeq reagent kit v2 (500 cycles). Data was analysed with the MiSeq Reporter software Metagenomics workflow v2.5.1.3 (Illumina). The average number of reads was approximately 147,191 per sample.

Analysis of sequencing data

FASTQ files generated by Illumina sequencing were qualitatively evaluated using FASTQC. Adaptor contamination and low-quality reads were detected with Trimmomatic53. Raw data were quality-filtered to remove sequences with <200 nucleotides or containing ambiguous bases, uncorrectable barcodes, and homopolymer runs of more than six bases. Sequences were grouped into OTUs with a 97% threshold of pairwise identity54. For clarity and visualisation purposes, the most abundant sequences with a relative abundance of ≥0.5% within each taxa were designated as ‘representative sequences’. OTUs that had a mean relative abundance of >0.001% were designated as ‘total sequences’ and were analysed separately. Representative and total sequences were assigned at different taxonomic levels (from phylum to genus) to bacterial SILVA datasets54,55,56,57.

The QIIME pipeline was used for quality filtering of DNA sequences, chimera removal (using the USEARCH software), taxonomic assignment, and calculation of the alpha diversity as previously described58, 59. Downstream data analysis and calculation of diversity metrics was completed in R3.3.2 using ggplot2 and phyloseq libraries and DESeq. 2 for calculating the log fold change60. The abundance-based coverage estimator (ACE), Chao 1 richness estimator, Shannon, Simpson, and inverse Simpson diversity indices were calculated using the phyloseq R library61.

Statistical analysis

All statistical analyses were performed using the statistics package Statistica for Windows (version 5.0). Results are reported as the mean ± SEM for each group. The non-parametric paired Wilcoxon signed-rank test was applied to the analysis of pre- and post-challenge differences. A value of p < 0.05 was considered significant.

Additional Information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Natividad, J. & Verdu, E. Modulation of intestinal barrier by intestinal microbiota: Pathological and therapeutic implications. Pharmacol. Res. 69, 42–51 (2013).

  2. 2.

    Sommer, F. & Bäckhed, F. The gut microbiota — masters of host development and physiology. Nat. Rev. Microbiol. 11, 227–238 (2013).

  3. 3.

    Ashida, H., Ogawa, M., Kim, M., Mimuro, H. & Sasakawa, C. Bacteria and host interactions in the gut epithelial barrier. Nature Chemical Biology 8, 36–45 (2011).

  4. 4.

    König, J. et al. Human Intestinal Barrier Function in Health and Disease. Clin. Transl. Gastroenterol. 7, e196 (2016).

  5. 5.

    Rhee, S., Pothoulakis, C. & Mayer, E. Principles and clinical implications of the brain–gut–enteric microbiota axis. Nat. Rev. Gastroenterol. Hepatol. 6, 306–314 (2009).

  6. 6.

    Vaarala, O., Atkinson, M. & Neu, J. The “Perfect Storm” for Type 1 Diabetes: The Complex Interplay Between Intestinal Microbiota, Gut Permeability, and Mucosal Immunity. Diabetes 57, 2555–2562 (2008).

  7. 7.

    Bischoff, S. et al. Intestinal permeability – a new target for disease prevention and therapy. BMC Gastroenterol. 14 (2014).

  8. 8.

    Kaliannan, K., Wang, B., Li, X., Kim, K. & Kang, J. A host-microbiome interaction mediates the opposing effects of omega-6 and omega-3 fatty acids on metabolic endotoxemia. Sci. Rep. 5, 11276 (2015).

  9. 9.

    van Vliet, M., Harmsen, H., de Bont, E. & Tissing, W. The Role of Intestinal Microbiota in the Development and Severity of Chemotherapy-Induced Mucositis. PLoS Pathog. 6, e1000879 (2010).

  10. 10.

    Dalmasso, M., Hill, C. & Ross, R. Exploiting gut bacteriophages for human health. Trends Microbiol. 22, 399–405 (2014).

  11. 11.

    Mills, S. et al. Movers and shakers. Gut Microbes 4, 4–16 (2013).

  12. 12.

    Sulakvelidze, A., Alavidze, Z. & Morris, J. Bacteriophage Therapy. Antimicrob. Agents Chemother. 45, 649–659 (2001).

  13. 13.

    Wittebole, X., De Roock, S. & Opal, S. A historical overview of bacteriophage therapy as an alternative to antibiotics for the treatment of bacterial pathogens. Virulence 5, 226–235 (2013).

  14. 14.

    Tetz, G. & Tetz, V. Bacteriophage infections of microbiota can lead to leaky gut in an experimental rodent model. Gut Pathog. 8 (2016).

  15. 15.

    Claesson, M. et al. Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucleic Acids Res. 38, e200–e200 (2010).

  16. 16.

    Nemzek, J. A., Hugunin, K. & Opp, M. R. Modeling sepsis in the laboratory: merging sound science with animal well-being. Comp. med. 58, 120–128 (2008).

  17. 17.

    Wang, L. et al. Methods to determine intestinal permeability and bacterial translocation during liver disease. J. Immunol. Methods 421, 44–53 (2015).

  18. 18.

    Ding, L. Effects of glutamine on intestinal permeability and bacterial translocation in TPN-rats with endotoxemia. World J. Gastroenterol. 9, 1327 (2003).

  19. 19.

    Fukui, H. Gut-liver axis in liver cirrhosis: How to manage leaky gut and endotoxemia. World J. Hepatol. 7, 425–442 (2015).

  20. 20.

    Yu, C. et al. Chronic Kidney Disease Induced Intestinal Mucosal Barrier Damage Associated with Intestinal Oxidative Stress Injury. Gastroenterol Res. Pract. 2016, 1–9 (2016).

  21. 21.

    D’Argenio, G. et al. Symbiotic formulation in experimentally induced liver fibrosis in rats: intestinal microbiota as a key point to treat liver damage? Liver Int. 33, 687–697 (2013).

  22. 22.

    Schloss, P. et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

  23. 23.

    Dethlefsen, L., Huse, S., Sogin, M. & Relman, D. The Pervasive Effects of an Antibiotic on the Human Gut Microbiota, as Revealed by Deep 16S rRNA Sequencing. PLoS Biol. 6, e280 (2008).

  24. 24.

    Odenwald, M. & Turner, J. Intestinal Permeability Defects: Is It Time to Treat? Clin. Gastroenterol. Hepatol. 11, 1075–1083 (2013).

  25. 25.

    Tremaroli, V. & Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature 489, 242–249 (2012).

  26. 26.

    De Paepe, M., Leclerc, M., Tinsley, C. R. & Petit, M. A. Bacteriophages: an underestimated role in human and animal health? Front Cell Infect Microbiol. 4, 39 (2014).

  27. 27.

    Duerkop, B. A. & Hooper, L. V. Resident viruses and their interactions with the immune system. Nat. immunol. 14, 654–659 (2013).

  28. 28.

    Fischetti, V. Bacteriophage lytic enzymes: novel anti-infectives. Trends Microbiol. 13, 491–496 (2005).

  29. 29.

    Collins, S., Denou, E., Verdu, E. & Bercik, P. The putative role of the intestinal microbiota in the irritable bowel syndrome. Dig. Liver Dis. 41, 850–853 (2009).

  30. 30.

    Maes, M., Kubera, M., Leunis, J. & Berk, M. Increased IgA and IgM responses against gut commensals in chronic depression: Further evidence for increased bacterial translocation or leaky gut. J. Affect. Dis. 141, 55–62 (2012).

  31. 31.

    Costello, M. et al. Brief Report: Intestinal Dysbiosis in Ankylosing Spondylitis. Arthritis Rheum 67, 686–691 (2015).

  32. 32.

    McLean, M., Dieguez, D., Miller, L. & Young, H. Does the microbiota play a role in the pathogenesis of autoimmune diseases? Gut 64, 332–341 (2014).

  33. 33.

    Dabrowska, K., Switala-Jelen, K., Opolski, A., Weber-Dabrowska, B. & Gorski, A. Bacteriophage penetration in vertebrates. J. Appl. Microbiol. 98, 7–13 (2005).

  34. 34.

    Kutter, E. et al. Phage Therapy in Clinical Practice: Treatment of Human Infections. Curr. Pharm. Biotechnol. 11, 69–86 (2010).

  35. 35.

    Laval, L. et al. Lactobacillus rhamnosus CNCM I-3690 and the commensal bacterium Faecalibacterium prausnitzii A2-165 exhibit similar protective effects to induced barrier hyper-permeability in mice. Gut Microbes 6, 1–9 (2015).

  36. 36.

    Mennigen, R. & Bruewer, M. Effect of Probiotics on Intestinal Barrier Function. Ann. N.Y.Acad. Sci. 1165, 183–189 (2009).

  37. 37.

    Martín, R. et al. Faecalibacterium prausnitzii prevents physiological damages in a chronic low-grade inflammation murine model. BMC Microbiol. 15 (2015).

  38. 38.

    Singh, N. et al. Activation of Gpr109a, Receptor for Niacin and the Commensal Metabolite Butyrate, Suppresses Colonic Inflammation and Carcinogenesis. Immunity 40, 128–139 (2014).

  39. 39.

    Lewis, K. et al. Enhanced translocation of bacteria across metabolically stressed epithelia is reduced by butyrate. Inflamm. Bowel Dis. 16, 1138–1148 (2010).

  40. 40.

    Burger-van Paassen, N. et al. The regulation of intestinal mucin MUC2 expression by short-chain fatty acids: implications for epithelial protection. Biochem. J. 420, 211–219 (2009).

  41. 41.

    Blaser, M. & Kirschner, D. The equilibria that allow bacterial persistence in human hosts. Nature 449, 843–849 (2007).

  42. 42.

    Kuramitsu, H. K., He, X., Lux, R., Anderson, M. H. & Shi, W. Interspecies interactions within oral microbial communities. Microbiol. Mol. Biol. Rev. 71, 653–670 (2007).

  43. 43.

    Filoche, S. K., Anderson, S. A. & Sissons, C. H. Biofilm growth of Lactobacillus species is promoted by Actinomyces species and Streptococcus mutans. Oral microbiology and immunology 19(5), 322–326 (2004).

  44. 44.

    Cao, L. & Yu, J. Effect of Helicobacter pylori infection on the composition of gastric microbiota in the development of gastric cancer. Gastrointest. tumors 2, 14–25 (2015).

  45. 45.

    Servin, A. L. Antagonistic activities of lactobacilli and bifidobacteria against microbial pathogens. FEMS microbiol rev 28, 405–440 (2004).

  46. 46.

    López-Brea, M., Alarcón, T., Domingo, D. & Díaz-Regañón, J. Inhibitory effect of Gram-negative and Gram-positive microorganisms against Helicobacter pylori clinical isolates. J. antimicrob. chemother. 61, 139–142 (2008).

  47. 47.

    Albus, U. Guide for the Care and Use of Laboratory Animals (8th edn) by the National Research Council of the National Academies Washington, DC: National Academies Press, 2011. Lab. Anim. 46, 267–268 (2012).

  48. 48.

    Bourdin, G. et al. Coverage of diarrhoea-associated Escherichia coli isolates from different origins with two types of phage cocktails. Microb. Biotechnol. 7, 165–176 (2014).

  49. 49.

    Meddings, J. & Swain, M. Environmental stress–induced gastrointestinal permeability is mediated by endogenous glucocorticoids in the rat. Gastroenterology 119, 1019–1028 (2000).

  50. 50.

    Parlesak, A., Schäfer, C., Schütz, T., Bode, J. & Bode, C. Increased intestinal permeability to macromolecules and endotoxemia in patients with chronic alcohol abuse in different stages of alcohol-induced liver disease. J. Hepatol. 32, 742–747 (2000).

  51. 51.

    Tsuji, E. et al. Simultaneous onset of acute inflammatory response, sepsis‐like symptoms and intestinal mucosal injury after cancer chemotherapy. Int. J. Cancer. 107, 303–308 (2003).

  52. 52.

    Amplicon, P. C. R., Clean-Up, P. C. R. & Index, P. C. R. 16S Metagenomic Sequencing Library Preparation. (2013).

  53. 53.

    Bolger, A., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

  54. 54.

    Edgar, R. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

  55. 55.

    Zhang, Y., Wang, X., Hu, M. & Li, P. Effect of hydraulic retention time (HRT) on the biodegradation of trichloroethylene wastewater and anaerobic bacterial community in the UASB reactor. Appl. Microbiol. Biotechnol. 99, 1977–1987 (2014).

  56. 56.

    Wang, Q., Garrity, G., Tiedje, J. & Cole, J. Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Appl Environ. Microbiol. 73, 5261–5267 (2007).

  57. 57.

    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).

  58. 58.

    Caporaso, J. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

  59. 59.

    Cox, L. et al. Altering the Intestinal Microbiota during a Critical Developmental Window Has Lasting Metabolic Consequences. Cell 158, 705–721 (2014).

  60. 60.

    Love, M., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq. 2. Genome Biology 15 (2014).

  61. 61.

    McMurdie, P. J. & Holmes, S. phyloseq: An R Package For Reproducible Interactive Analysis And Graphics Of Microbiome Census Data. PloS one 8, e61217 (2013).

Download references


We would like to thank the Applied Bioinformatics Laboratories at NYU School of Medicine for providing bioinformatics support and helping with the analysis and interpretation of the data. This work used computing resources at the High Performance Computing Facility at the NYU Langone Medical Center. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sector.

Author information


  1. Human Microbiology Institute, New York, NY, 10027, USA

    • George V. Tetz
    •  & Victor Tetz
  2. Department of Medicine, New York University School of Medicine, New York, NY, 10016, USA

    • Kelly V. Ruggles
  3. Applied Bioinformatics Laboratories, New York University Medical Center, New York, NY, 10016, USA

    • Kelly V. Ruggles
    • , Hua Zhou
    •  & Aristotelis Tsirigos
  4. Department of Pathology, New York University School of Medicine, New York, NY, 10016, USA

    • Adriana Heguy
    •  & Aristotelis Tsirigos
  5. Laura and Isaac Perlmutter Cancer Center, New York University School of Medicine, New York, NY, 10016, USA

    • Adriana Heguy
    •  & Aristotelis Tsirigos
  6. Genome Technology Center, Division of Advanced Research Technologies, NYU School of Medicine, New York, NY, 10016, USA

    • Adriana Heguy


  1. Search for George V. Tetz in:

  2. Search for Kelly V. Ruggles in:

  3. Search for Hua Zhou in:

  4. Search for Adriana Heguy in:

  5. Search for Aristotelis Tsirigos in:

  6. Search for Victor Tetz in:


G.T. designed the experiments and study. G.T. and V.T. supervised data analysis and wrote the manuscript. K.R. and H.Z. conducted metagenomics analyses. A.H. and A.T. gave technical support and conceptual advice.

Competing Interests

The authors declare that they have no competing interests.

Corresponding author

Correspondence to George V. Tetz.


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Creative Commons BY

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit