Open Access

Deletion of poly(ADP‑ribose) polymerase-1 changes the composition of the microbiome in the gut

  • Authors:
    • András Vida
    • Gábor Kardos
    • Tünde Kovács
    • Balázs L. Bodrogi
    • Péter Bai
  • View Affiliations

  • Published online on: September 10, 2018     https://doi.org/10.3892/mmr.2018.9474
  • Pages: 4335-4341
  • Copyright : © Vida et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].

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Abstract

Poly(adenosine diphosphate‑ribose) polymerase (PARP)‑1 is the prototypical PARP enzyme well known for its role in DNA repair and as a pro‑inflammatory protein. Since PARP1 is an important co‑factor of several other pro‑inflammatory proteins, in the present study the possible changes in microbial flora of PARP1 knockout mice were investigated. Samples from the duodenum, cecum and feces from wild type and PARP1 knockout C57BL/6J male mice were collected and 16S ribosomal RNA genes were sequenced. Based on the sequencing results, the microbiome and compared samples throughout the lower part of the gastrointestinal system were reconstructed. The present results demonstrated that the lack of PARP1 enzyme only disturbed the microbial flora of the duodenum, where the biodiversity increased in the knockout animals on the species level but decreased on the order level. The most prominent change was the overwhelming abundance of the family Porphyromonadaceae in the duodenum of PARP1‑/‑ animals, which disappeared in the cecum and feces where families were spread out more evenly than in the wild type animals. The findings of the present study may improve current understanding of the role of PARP1 in chronic inflammatory diseases.

Introduction

Poly[adenosine diphosphate (ADP)-ribose] (PAR) polymerase (PARP)-1 is the member of the PARP family that is considered to be the ‘prototypical’ PARP enzyme (1). PARP1 can be activated by DNA strand breaks and a set of posttranslational modifications [previously reviewed in (2)]. Active PARP1 cleaves aldehyde dehydrogenase into nicotinamide and ADP-ribose (ADPR), and forms ADPR polymers, also known as PAR, on different acceptor proteins (3,4). PAR chains can modify the function of the acceptors, enabling PAR-mediated regulation of protein function. PARP1 is responsible for >80% of all cellular PARP activities (5,6).

PARP1 is widely recognized as a pro-inflammatory protein in T helper1-mediated pathologies [previously reviewed in (79)]. The pro-inflammatory properties of PARP1 have numerous molecular roots. Firstly, PARP1 is a vital positive co-factor of several pro-inflammatory transcription factors [previously reviewed in (7)], of which the first to be identified was nuclear factor-κB (NF-κB) (10). In addition, PARP-mediated epigenetic changes also contribute to the pro-inflammatory transcriptional properties of PARP1 (11). The induction of these transcription factors facilitates the production of pro-inflammatory chemokines, cytokines and lipid mediators (7,11). These mediators in turn facilitate the chemotaxis of immune cells and also have a pivotal role in their activation (12,13). Adhesion factors (such as intercellular adhesion molecule 1 and vascular cell adhesion molecule 1) that help immune cells enter the site of inflammation are also expressed in a PARP1-dependent manner (14). Finally, there are other factors, including inducible nitric oxide synthase, cyclooxygenase-2 and certain matrix metalloproteinases that are activated in a PARP1-dependent manner as well (15,16). Taken together, immune cell activation, infiltration, cell migration and oxidative/nitrosative stress are PARP1-dependent. Notably, the administration of PARP inhibitors to humans also has an anti-inflammatory effect (17).

Recent advances in sequencing technology have largely increased current knowledge on the composition of the microflora (the collective microfloral genome often referred to as the microbiome) in various regions of the human body (1822). Sequencing-based determination of the microbiome not only revealed novel bacterial species and enabled the study of the microflora, but also revealed that bodily cavities (such as lower airways), previously thought to be sterile, do contain bacteria in low numbers (1822). Furthermore, these studies have shed light on the interactions between the host and the microbiome. Microbes produce metabolites that enter the systemic circulation, and can affect the cells of the host (2329) and interact directly with the components of the innate immune system (3032). In turn, the host influences the microbial communities through the immune system, feeding behavior and personal hygiene (18). Changes in the composition of the microbiome have been associated with particular diseases, including metabolic diseases, autism and cancer, where the reduction in the diversity of the microbiome frequently coincides with the onset of the disease (33).

The molecular determinants of the interaction between the host's immune system and the microbiome are largely unknown. Previous studies have revealed the role of the innate immune system, more precisely the Toll-like receptor (TLR) family (30,31,34,35). As PARP1 modulates the TLR-mediated signaling (32,3640), the present study was performed to investigate the changes in the microbiome upon the deletion of PARP1.

Materials and methods

Animals

PARP1 knockout mice with a C57BL/6J background were used (41), and were generated in Het-to-Het breeding. A total of 6 mice were housed in each cage (standard block shape 365×207×140 mm, surface 530 cm2; 1284 L Eurostandard Type II. L from Techniplast) with Lignocel Select Fine (J. Rettenmaierund Söhne, Germany) as bedding. Mice were housed under a 12 h light/dark cycle at 22±1°C. Mice had ad libitum access to food and water (sterilized tap water). The animal facility was overseen by a veterinarian. Male mice (20 animals, 8–12 weeks old, 22–26 g body weight) were randomly selected from a larger pool of mice bred at the Animal Facility of the University of Debrecen (Debrecen, Hungary). Randomization between groups was not possible since group assignment was based on genotypes (n=20, 10 per experimental group). Animals were fasted 16 h prior to sampling to exclude the effect of potentially different eating periods and ingested food quantities. Following this, fresh fecal samples were collected and stored immediately in liquid nitrogen. Subsequently, animals were sacrificed by cervical dislocation. Subsequently the initial 15 mm segment of the duodenum and ~1/3 of the cecum was removed. Both intestinal samples in addition to a freshly collected fecal pellet were rapidly frozen in liquid nitrogen immediately following removal. For long term storage samples were kept at −80°C (41). All animal experiments were approved by the local and national ethical board of the University of Debrecen (reg. 1/2015/DEMÁB).

DNA isolation and sequencing

Total DNA was isolated form each sample using the DNeasy PowerSoil kit according to the manufacturer's protocol (cat. no. 12888-100; Qiagen GmbH, Hilden, Germany). Subsequently identical amounts of DNA from each sample within the groups were pooled; the use of identical quantities of DNA ensures that each sample contributes equally to the abundance. From the pooled samples 16S ribosomal RNA genes were amplified and sequenced. Samples were assessed for quality and potential contaminants on a 1.5% agarose gel. DNA isolation and sequencing were performed by UD-GenoMed as a commercial service (UD-GenoMed, Debrecen, Hungary).

Analysis of the microbiome

Sequence fragments were uploaded to the metagenomics RAST server, MG-RAST (v4.0, metagenomics.anl.gov/) where paired end joining and microbiome reconstruction was performed (42). Subsequent analysis was performed with a specialized standalone software Taxamat (v1.04), which is freely available at www.taxamat.com. Using Taxamat, data representing food contaminants and host DNA (Viridiplantae and Metazoa) was removed. To produce the sequencing depth of each sample to a comparable level, data were downsampled so that samples with higher abundance matched the samples with the lowest abundance values. Source files for the sequencing raw and the curated data can be found at www.ncbi.nlm.nih.gov/bioproject/411773 (NCBI Bioproject PRJNA411773).

Statistical analysis

Diversity profiles were created using Palaeontological Statistics (PAST) (43). Diversity indices were calculated using PAST and Taxamat (www.taxamat.com). When comparing diversity profiles, the curve data points were downsampled to eight evenly distributed values over the whole range and statistical significance was determined using two tailed Student's t-test for paired samples. Family and order distributions across samples were compared using the ‘prop.test’ function (www.rdocumentation.org/packages/mosaic/versions/1.1.1/topics/prop.test) in RStudio (version 0.99.484; www.rstudio.com) (44,45).

Results

As a first step, changes to microbial diversity were assessed by comparing the gut samples of the PARP1+/+ and PARP1−/− mice. When comparing diversity indices, the main pitfall is the arbitrary choice of the used index. To avoid this issue diversity profiles were plotted in addition to comparing individual indices (Fig. 1). These curves use the parameter (α) dependent exponent of the Renyi index (46). This function returns the number of taxa at α=0, a number proportional to the Shannon index at α=1 and a Simpson-like index at α=2. When plotting these profiles, it was demonstrated that the duodenal samples of PARP1+/+ and PARP1−/− animals were slightly different in terms of their diversity profiles (Fig. 1A and D; P<0.001 and P<0.01, respectively). Notably, on the species level the samples from PARP1−/− demonstrated higher diversity values while on order level this trend was the opposite. When comparing diversity profiles representing the lower gastrointestinal tract (cecum) and feces, the only significant differences were in the cecal samples on order level (P<0.05; Fig. 1E). However, even in that case, although statistically significant, profile curves were very similar. The same trend was also demonstrated by the traditional Shannon and Simpson indices (Table I).

Table I.

Simpson and Shannon indices obtained in the present study.

Table I.

Simpson and Shannon indices obtained in the present study.

SpeciesOrder


IndicesDuodenumCecumFecesDuodenumCecumFeces
PARP1+/+−/−+/+−/−+/+−/−+/+−/−+/+−/−+/+−/−
Simpson0.8830.9480.9550.9330.9420.9310.6510.5280.7260.6930.7100.727
Shannon3.3783.7893.8583.7333.6563.6431.3601.0461.5701.4771.5331.617

[i] PARP1, poly(adenosine diphosphate-ribose) polymerase 1.

The most abundant orders in all samples were then investigated. Clostridiales and Bacteroidales represented between ~70 and 90% of all taxa across all samples. The less abundant orders were Lactobacillales, Erysipelotrichales and Verrucomicrobiales accounting for a further 5–15% while the rest (~5–15%) were spread out almost evenly between a further ~30 orders (Fig. 2). On the order level all samples appeared to be similar, the only trend worth noting was the decreased ratio of Clostridiales in cecal and fecal samples when compared with duodenal ones (Fig. 2, middle panel). The statistical significance levels are presented in Table II. This was true for samples from wild type and from PARP1−/− animals, albeit the duodenal samples from the latter demonstrated a higher ratio of Clostridiales (~63% in PARP1−/− animals vs. ~44% in PARP1+/+ animals; Fig. 2A). As these results indicated, the majority of the other orders proportionally advanced to fill in the gap left by Clostridiales throughout the gastrointestinal system.

Table II.

Taxon proportion significance levels between compared samples.

Table II.

Taxon proportion significance levels between compared samples.

Significance levels

Compared samplesOrderBacteroidalesClostridiales
PARP+/+ duodenum vs. PARP+/+ cecumP<0.05P<0.001ns
PARP+/+ duodenum vs. PARP+/+ fecesP<0.001P<0.001ns
PARP+/+ cecum vs. PARP+/+ fecesnsP<0.001ns
PARP−/− duodenum vs. PARP−/− cecumP<0.001P<0.01ns
PARP−/− duodenum vs. PARP−/− fecesP<0.001P<0.01ns
PARP−/− cecum vs. PARP−/− fecesnsP<0.05ns
PARP+/+ duodenum vs. PARP−/− duodenumP<0.001P<0.001ns
PARP+/+ cecum vs. PARP−/− cecumnsP<0.001ns
PARP+/+ feces vs. PARP−/− fecesnsP<0.001P<0.05

[i] ns, non-significant; PARP1, poly(adenosine diphosphate-ribose) polymerase 1.

Further investigation on the family level revealed that there was little to no difference in the main composition in Clostridiales (Fig. 2, right-hand panel). The main families were Lachnospiraceae, Clostridiaceae, Eubacteriaceae and Ruminococcaceae. More than half of the Clostridiales were comprised of members of the Lachnospiraceae family (54–65%), while the next two, Clostridiaceae and Ruminococcaceae, were responsible for a further ~20%. When comparing the family composition of the different samples, there was little to no changes in these ratios throughout the gastrointestinal system, nor were any differences detected between samples from wild type and PARP1−/− animals, except for a slight elevation of the Ruminococcaceae ratio in the fecal samples of PARP1−/− animals (Fig. 2, right-hand panel; Table II; P<0.05).

When investigating the family composition of the order Bacteroidales the most abundant species were Porphyromonadaceae representing ~50–90% of all taxa (Fig. 2, left-hand panel). The other families included Rikenellaceae, Bacteroidaceae and Prevotellaceae. Other taxa not including those already mentioned reached only a combined ratio of a maximal 0.20% across all samples. PARP1−/− originated samples demonstrated little change throughout the gastrointestinal system. The most noteworthy was the ~14% decrease in the family Porphyromonadaceae's ratio, which was almost exclusively made up for by the increase in Prevotellaceae and Bacteroidaceae between the duodenal and fecal samples. This 14% decrease in the ratio of Porphyromonadaceae was much less prominent in PARP−/− animals than in their PARP1+/+ counterpart, where a 46 and 40% decrease was detected in cecal and fecal samples respectively, when compared with duodenal samples. In fact, the little change of Porphyromonadaceae in PARP1−/− animals kept this family the most abundant one in the Bacteroidales order of the knockout strain across all samples, while in wild type animals only the duodenal samples were dominated (92%) by Porphyromonadaceae (Fig. 2A, left-hand panel). Cecal and fecal samples in the PARP1+/+ group, consisted only 46 and 51% of Porphyromonadaceae respectively (Fig. 2B and C, left-hand panel; statistical significance levels are provided in Table II).

Samples isolated from wild type animals demonstrated notable changes too. Duodenal samples harbored ~92% Porphyromonadaceae in the order Bacteroidales, leaving barely any room for the other families in this order, the second most abundant being Prevotellaceae (3.6%) followed by Rikenellaceae (2.1%). Notably, Porphyromonadaceae contributed to only ~50% of the Bacteroidales family, while the other most prominent families indicated a ~10-fold increase when compared with duodenal samples (Bacteroidaceae: 13–17%; Prevotellaceae: 21–29%; Fig. 2A-C, statistical significance levels are provided in Table II).

Discussion

A recent study demonstrated PARP1-mediated changes in the fecal microbiome in regard to mucosal injury (47). Following that thread, the composition of the microbiome on the lower part of the gastrointestinal tract and feces was assessed in the present study.

The most prominent result of the present study was that in the duodenum, in the absence of PARP1, the order of diversity decreased. These results were similar to those of Larmonier et al (47) who also reported a decrease in diversity.

Reference strains of mouse gut bacteria are practically unavailable and very few studies have attempted to provide a broad overview. One of these attempts was made in 2016 by Lagkouvardos et al (48) who aimed to establish the Mouse Intestinal Bacterial Collection. Their results demonstrated that certain species are specific to the mouse intestine. The present results are based on direct sequencing only, while Lagkouvardos et al (48) utilized culturing in parallel. Despite the differences in methodology, the present results on order and family level are very similar with the ones mentioned in Lagkouvardos et al (48), thus validating them.

It is of note that the present experimental system did not challenge the microbiome; in other words, the absence of PARP1 alone led to visible changes in the microbiome in the absence of a disease. Furthermore, direct sequencing of 16S ribosomal DNA was used, which may add a bias to the chemistry prior to the in silico evaluation as compared with shotgun sequencing; however, in the upper parts of the gastrointestinal tract the number of the bacterial DNA is low as compared with the host DNA making shotgun sequencing cumbersome (20).

What could cause these changes in the microbiome? Innate immunity is already implicated in the regulation of gut bacteria through TLRs (2224). PARP1 is a positive co-factor of several key inflammatory transcription factors (such as NF-κB and activator protein-1) (6) and through that PARP1 may modulate TLR function (36,37,40,49). Although, there is no direct evidence, the present study proposed that the interdependence of PARP1 and TLRs is a likely explanation for changes in the microbiome in the PARP1−/− mice. PARP1 is responsible for the majority of the cellular PARP activity (5,50,51), therefore, its absence often resembles to PARP inhibitor treatment. However, there is no evidence for the capability of PARP inhibitors to influence the microbiome.

At present it is difficult to assess the physiological relevance of these findings. A body of evidence has indicated that PARP1 serves a key role in inflammatory pathologies [such as arthritis (52,53) or type I diabetes (54,55)] or metabolic diseases [such as type II diabetes (5658)], where the microbiome has a pivotal pathogenic role (26,57,58). Similarly to these, changes in the duodenal flora serve a dominant role in the pathogenesis of type II diabetes (59). These possibilities require further assessment in order to verify causal association. Damage to the gut flora, similarly to certain antibiotics, may contribute to the diarrhea observed as a side effect of PARP inhibitor treatment in humans (60). This link between diarrhea and changes in the microbiome also suggests that the application of PARP inhibitors may predispose to or aggravate antibiotic-induced diarrhea in PARP inhibitor-treated patients. Taken together, understanding the link between PARPs and the microbiome has importance for the clinical application of these inhibitors.

Acknowledgements

The authors would like to thank Dr. Zsolt Karányi (University of Debrecen, Debrecen, Hungary) for his guidance in statistical analysis.

Funding

The present study was funded by grants from NKFIH (grant nos. K123975 and GINOP-2.3.2-15-2016-00006) and the Momentum Fellowship and PROJEKT2017-44 of the Hungarian Academy of Sciences.

Availability of data and materials

The primary data for microbiome sequencing is available in the NCBI repository www.ncbi.nlm.nih.gov/bioproject/411773 (NCBI BioProject PRJNA411773).

Authors' contributions

AV and TK conducted the collection of fecal samples. AV conducted sample collection and performed data analysis. AV, GK, BLB and PB wrote the manuscript. PB conceptulized the study and drafted the manuscript. BLB and GK provided their medical expertise in understanding and discussing the observed changes. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All animal experiments were approved by the local and national ethical board of the University of Debrecen (reg. 1/2015/DEMÁB; Debrecen, Hungary).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Amé JC, Spenlehauer C and de Murcia G: The PARP superfamily. Bioessays. 26:882–893. 2004. View Article : Google Scholar : PubMed/NCBI

2 

Cantó C, Sauve A and Bai P: Crosstalk between poly(ADP-ribose) polymerase and sirtuin enzymes. Mol Aspects Med. 34:1168–201. 2013. View Article : Google Scholar : PubMed/NCBI

3 

Bartolomei G, Leutert M, Manzo M, Baubec T and Hottiger MO: Analysis of chromatin ADP-ribosylation at the genome-wide level and at specific loci by ADPr-ChAP. Mol Cell. 61:474–485. 2016. View Article : Google Scholar : PubMed/NCBI

4 

Gibson BA and Kraus WL: Identification of protein substrates of specific PARP enzymes using analog-sensitive PARP mutants and a ‘Clickable’ NAD+ analog. Methods Mol Biol. 1608:111–135. 2017. View Article : Google Scholar : PubMed/NCBI

5 

Schreiber V, Amé JC, Dollé P, Schultz I, Rinaldi B, Fraulob V, Ménissier-de Murcia J and de Murcia G: Poly(ADP-ribose) polymerase-2 (PARP-2) is required for efficient base excision DNA repair in association with PARP-1 and XRCC1. J Biol Chem. 277:23028–23036. 2002. View Article : Google Scholar : PubMed/NCBI

6 

Bai P and Virág L: Role of poly(ADP-ribose) polymerases in the regulation of inflammatory processes. FEBS Lett. 586:3771–3777. 2012. View Article : Google Scholar : PubMed/NCBI

7 

Rosado MM, Bennici E, Novelli F and Pioli C: Beyond DNA repair, the immunological role of PARP-1 and its siblings. Immunology. 139:428–437. 2013. View Article : Google Scholar : PubMed/NCBI

8 

Mangerich A and Bürkle A: Pleiotropic cellular functions of PARP1 in longevity and aging: Genome maintenance meets inflammation. Oxid Med Cell Longev. 2012:3216532012. View Article : Google Scholar : PubMed/NCBI

9 

Oliver FJ, Ménissier-de Murcia J, Nacci C, Decker P, Andriantsitohaina R, Muller S, de la Rubia G, Stoclet JC and de Murcia G: Resistance to endotoxic shock as a consequence of defective NF-kappaB activation in poly(ADP-ribose) polymerase-1 deficient mice. EMBO J. 18:4446–4454. 1999. View Article : Google Scholar : PubMed/NCBI

10 

Buzzo CL, Medina T, Branco LM, Lage SL, Ferreira LC, Amarante-Mendes GP, Hottiger MO, De Carvalho DD and Bortoluci KR: Epigenetic regulation of nitric oxide synthase 2, inducible (Nos2) by NLRC4 inflammasomes involves PARP1 cleavage. Sci Rep. 7:416862017. View Article : Google Scholar : PubMed/NCBI

11 

Kiss B, Szántó M, Szklenár M, Brunyánszki A, Marosvölgyi T, Sárosi E, Remenyik É, Gergely P, Virág L, Decsi T, et al: Poly(ADP) ribose polymerase-1 ablation alters eicosanoid and docosanoid signaling and metabolism in a murine model of contact hypersensitivity. Mol Med Rep. 11:2861–2867. 2015. View Article : Google Scholar : PubMed/NCBI

12 

Cecchinato V and Uguccioni M: Insight on the regulation of chemokine activities. J Leukoc Biol. Apr 18–2018.(Epub ahead of print). View Article : Google Scholar : PubMed/NCBI

13 

Hughes CE and Nibbs RJB: A guide to chemokines and their receptors. FEBS J. Apr 10–2018.(Epub ahead of print). View Article : Google Scholar :

14 

Zingarelli B, Szabó C and Salzman AL: Blockade of Poly(ADP-ribose) synthetase inhibits neutrophil recruitment, oxidant generation, and mucosal injury in murine colitis. Gastroenterology. 116:335–345. 1999. View Article : Google Scholar : PubMed/NCBI

15 

Lin Y, Tang X, Zhu Y, Shu T and Han X: Identification of PARP-1 as one of the transcription factors binding to the repressor element in the promoter region of COX-2. Arch Biochem Biophys. 505:123–129. 2011. View Article : Google Scholar : PubMed/NCBI

16 

Brunyánszki A, Hegedus C, Szántó M, Erdélyi K, Kovács K, Schreiber V, Gergely S, Kiss B, Szabó E, Virág L and Bai P: Genetic ablation of PARP-1 protects against oxazolone-induced contact hypersensitivity by modulating oxidative stress. J Invest Dermatol. 130:2629–2637. 2010. View Article : Google Scholar : PubMed/NCBI

17 

Morrow DA, Brickman CM, Murphy SA, Baran K, Krakover R, Dauerman H, Kumar S, Slomowitz N, Grip L, McCabe CH and Salzman AL: A randomized, placebo-controlled trial to evaluate the tolerability, safety, pharmacokinetics, and pharmacodynamics of a potent inhibitor of poly(ADP-ribose) polymerase (INO-1001) in patients with ST-elevation myocardial infarction undergoing primary percutaneous coronary intervention: Results of the TIMI 37 trial. J Thromb Thrombolysis. 27:359–64. 2009. View Article : Google Scholar : PubMed/NCBI

18 

Mikó E, Vida A and Bai P: Translational aspects of the microbiome-to be exploited. Cell Biol Toxicol. 32:153–156. 2016. View Article : Google Scholar : PubMed/NCBI

19 

Del Vecchio F, Mastroiaco V, Di Marco A, Compagnoni C, Capece D, Zazzeroni F, Capalbo C, Alesse E and Tessitore A: Next-generation sequencing: Recent applications to the analysis of colorectal cancer. J Transl Med. 15:2462017. View Article : Google Scholar : PubMed/NCBI

20 

Quince C, Walker AW, Simpson JT, Loman NJ and Segata N: Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 35:833–844. 2017. View Article : Google Scholar : PubMed/NCBI

21 

Shaffer M, Armstrong AJS, Phelan VV, Reisdorph N and Lozupone CA: Microbiome and metabolome data integration provides insight into health and disease. Transl Res. 189:51–64. 2017. View Article : Google Scholar : PubMed/NCBI

22 

Reuter JA, Spacek DV and Snyder MP: High-throughput sequencing technologies. Mol Cell. 58:586–597. 2015. View Article : Google Scholar : PubMed/NCBI

23 

Tlaskalová-Hogenová H, Stěpánková R, Kozáková H, Hudcovic T, Vannucci L, Tučková L, Rossmann P, Hrnčíř T, Kverka M, Zákostelská Z, et al: The role of gut microbiota (commensal bacteria) and the mucosal barrier in the pathogenesis of inflammatory and autoimmune diseases and cancer: Contribution of germ-free and gnotobiotic animal models of human diseases. Cell Mol Immunol. 8:110–120. 2011. View Article : Google Scholar : PubMed/NCBI

24 

Kahn SE, Cooper ME and Del Prato S: Pathophysiology and treatment of type 2 diabetes: Perspectives on the past, present, and future. Lancet. 383:1068–1083. 2014. View Article : Google Scholar : PubMed/NCBI

25 

Naseer MI, Bibi F, Alqahtani MH, Chaudhary AG, Azhar EI, Kamal MA and Yasir M: Role of gut microbiota in obesity, type 2 diabetes and Alzheimer's disease. CNS Neurol Disord Drug Targets. 13:305–311. 2014. View Article : Google Scholar : PubMed/NCBI

26 

Puertollano E, Kolida S and Yaqoob P: Biological significance of short-chain fatty acid metabolism by the intestinal microbiome. Curr Opin Clin Nutr Metab Care. 17:139–144. 2014. View Article : Google Scholar : PubMed/NCBI

27 

Duca FA, Sakar Y, Lepage P, Devime F, Langelier B, Doré J and Covasa M: Replication of obesity and associated signaling pathways through transfer of microbiota from obese prone rat. Diabetes. 63:1624–1636. 2014. View Article : Google Scholar : PubMed/NCBI

28 

Xie G, Wang X, Huang F, Zhao A, Chen W, Yan J, Zhang Y, Lei S, Ge K, Zheng X, et al: Dysregulated hepatic bile acids collaboratively promote liver carcinogenesis. Int J Cancer. 139:1764–1775. 2016. View Article : Google Scholar : PubMed/NCBI

29 

Yoshimoto S, Loo TM, Atarashi K, Kanda H, Sato S, Oyadomari S, Iwakura Y, Oshima K, Morita H, Hattori M, et al: Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature. 499:97–101. 2013. View Article : Google Scholar : PubMed/NCBI

30 

Leifer CA, McConkey C, Li S, Chassaing B, Gewirtz AT and Ley RE: Linking genetic variation in human Toll-like receptor 5 genes to the gut microbiome's potential to cause inflammation. Immunol Lett. 162:3–9. 2014. View Article : Google Scholar : PubMed/NCBI

31 

Velloso LA, Folli F and Saad MJ: TLR4 at the crossroads of nutrients, gut microbiota, and metabolic inflammation. Endocr Rev. 36:245–271. 2015. View Article : Google Scholar : PubMed/NCBI

32 

Caballero S and Pamer EG: Microbiota-mediated inflammation and antimicrobial defense in the intestine. Annu Rev Immunol. 33:227–256. 2015. View Article : Google Scholar : PubMed/NCBI

33 

Goedert JJ, Jones G, Hua X, Xu X, Yu G, Flores R, Falk RT, Gail MH, Shi J, Ravel J and Feigelson HS: Investigation of the association between the fecal microbiota and breast cancer in postmenopausal women: A population-based case-control pilot study. J Natl Cancer Inst. 107:djv1472015. View Article : Google Scholar : PubMed/NCBI

34 

Mu C, Yang Y and Zhu W: Crosstalk between the immune receptors and gut microbiota. Curr Protein Pept Sci. 16:622–631. 2015. View Article : Google Scholar : PubMed/NCBI

35 

Frosali S, Pagliari D, Gambassi G, Landolfi R, Pandolfi F and Cianci R: How the intricate interaction among toll-like receptors, microbiota, and intestinal immunity can influence gastrointestinal pathology. J Immunol Res. 2015:4898212015. View Article : Google Scholar : PubMed/NCBI

36 

Farez MF, Quintana FJ, Gandhi R, Izquierdo G, Lucas M and Weiner HL: Toll-like receptor 2 and poly(ADP-ribose) polymerase 1 promote central nervous system neuroinflammation in progressive EAE. Nat Immunol. 10:958–964. 2009. View Article : Google Scholar : PubMed/NCBI

37 

Zerfaoui M, Errami Y, Naura AS, Suzuki Y, Kim H, Ju J, Liu T, Hans CP, Kim JG, Abd Elmageed ZY, et al: Poly(ADP-ribose) polymerase-1 is a determining factor in Crm1-mediated nuclear export and retention of p65 NF-kappa B upon TLR4 stimulation. J Immunol. 185:1894–1902. 2010. View Article : Google Scholar : PubMed/NCBI

38 

Kauppinen A, Suuronen T, Ojala J, Kaarniranta K and Salminen A: Antagonistic crosstalk between NF-κB and SIRT1 in the regulation of inflammation and metabolic disorders. Cell Signal. 25:1939–1948. 2013. View Article : Google Scholar : PubMed/NCBI

39 

Krukenberg KA, Kim S, Tan ES, Maliga Z and Mitchison TJ: Extracellular poly(ADP-ribose) is a pro-inflammatory signal for macrophages. Chem Biol. 22:446–452. 2015. View Article : Google Scholar : PubMed/NCBI

40 

Qin WD, Mi SH, Li C, Wang GX, Zhang JN, Wang H, Zhang F, Ma Y, Wu DW and Zhang M: Low shear stress induced HMGB1 translocation and release via PECAM-1/PARP-1 pathway to induce inflammation response. PLoS One. 10:e01205862015. View Article : Google Scholar : PubMed/NCBI

41 

Menissier-de Murcia J, Niedergang C, Trucco C, Ricoul M, Dutrillaux B, Mark M, Oliver FJ, Masson M, Dierich A, LeMeur M, et al: Requirement of poly(ADP-ribose) polymerase in recovery from DNA damage in mice and in cells. ProcNatl Acad Sci USA. 94:7303–7307. 1997. View Article : Google Scholar

42 

Meyer F, Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A, et al: The metagenomics RAST server-a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics. 9:3862008. View Article : Google Scholar : PubMed/NCBI

43 

Hammer O, Harper DAT and Ryan PD: PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica. 4:92001.

44 

Newcombe RG: Two-sided confidence intervals for the single proportion: Comparison of seven methods. Stat Med. 17:857–872. 1998. View Article : Google Scholar : PubMed/NCBI

45 

Newcombe RG: Interval estimation for the difference between independent proportions: Comparison of eleven methods. Stat Med. 17:873–890. 1998. View Article : Google Scholar : PubMed/NCBI

46 

Tothmeresz B: Comparison of different methods for diversity ordering. J Veget Sci. 6:283–290. 1995. View Article : Google Scholar

47 

Larmonier CB, Shehab KW, Laubitz D, Jamwal DR, Ghishan FK and Kiela PR: Transcriptional reprogramming and resistance to colonic mucosal injury in Poly(ADP-ribose) Polymerase 1 (PARP1)-deficient mice. J Biol Chem. 291:8918–8930. 2016. View Article : Google Scholar : PubMed/NCBI

48 

Lagkouvardos I, Pukall R, Abt B, Foesel BU, Meier-Kolthoff JP, Kumar N, Bresciani A, Martínez I, Just S, Ziegler C, et al: The mouse intestinal bacterial collection (miBC) provides host-specific insight into cultured diversity and functional potential of the gut microbiota. Nat Microbiol. 1:161312016. View Article : Google Scholar : PubMed/NCBI

49 

Davis K, Banerjee S, Friggeri A, Bell C, Abraham E and Zerfaoui M: Poly(ADP-ribosyl)ation of high mobility group box 1 (HMGB1) protein enhances inhibition of efferocytosis. Mol Med. 18:359–369. 2012. View Article : Google Scholar : PubMed/NCBI

50 

Szanto M, Rutkai I, Hegedus C, Czikora Á, Rózsahegyi M, Kiss B, Virág L, Gergely P, Tóth A and Bai P: Poly(ADP-ribose) polymerase-2 depletion reduces doxorubicin-induced damage through SIRT1 induction. Cardiovasc Res. 92:430–438. 2011. View Article : Google Scholar : PubMed/NCBI

51 

Bai P, Canto C, Brunyánszki A, Huber A, Szántó M, Cen Y, Yamamoto H, Houten SM, Kiss B, Oudart H, et al: PARP-2 regulates sirt1 expression and whole-body energy expenditure. Cell Metab. 13:450–460. 2011. View Article : Google Scholar : PubMed/NCBI

52 

Pascual M, López-Nevot MA, Cáliz R, Ferrer MA, Balsa A, Pascual-Salcedo D and Martín J: A poly(ADP-ribose) polymerase haplotype spanning the promoter region confers susceptibility to rheumatoid arthritis. Arthritis Rheum. 48:638–641. 2003. View Article : Google Scholar : PubMed/NCBI

53 

Masutani M, Nakagama H and Sugimura T: Poly(ADP-ribosyl)ation in relation to cancer and autoimmune disease. Cell Mol Life Sci. 62:769–783. 2005. View Article : Google Scholar : PubMed/NCBI

54 

Burkart V, Wang ZQ, Radons J, Heller B, Herceg Z, Stingl L, Wagner EF and Kolb H: Mice lacking the poly(ADP-ribose) polymerase gene are resistant to pancreatic beta-cell destruction and diabetes development induced by streptozocin. Nat Med. 5:314–319. 1999. View Article : Google Scholar : PubMed/NCBI

55 

Pacher P, Beckman JS and Liaudet L: Nitric oxide and peroxynitrite in health and disease. Physiol Rev. 87:315–424. 2007. View Article : Google Scholar : PubMed/NCBI

56 

Bai P, Cantó C, Oudart H, Brunyánszki A, Cen Y, Thomas C, Yamamoto H, Huber A, Kiss B, Houtkooper RH, et al: PARP-1 Inhibition Increases Mitochondrial Metabolism through SIRT1 Activation. Cell Metab. 13:461–468. 2011. View Article : Google Scholar : PubMed/NCBI

57 

Koren O, Goodrich JK, Cullender TC, Spor A, Laitinen K, Bäckhed HK, Gonzalez A, Werner JJ, Angenent LT, Knight R, et al: Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell. 150:470–480. 2012. View Article : Google Scholar : PubMed/NCBI

58 

Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, Almeida M, Arumugam M, Batto JM, Kennedy S, et al: Richness of human gut microbiome correlates with metabolic markers. Nature. 500:541–546. 2013. View Article : Google Scholar : PubMed/NCBI

59 

Kamvissi-Lorenz V, Raffaelli M, Bornstein S and Mingrone G: Role of the Gut on glucose homeostasis: Lesson learned from metabolic surgery. Curr Atheroscler Rep. 19:92017. View Article : Google Scholar : PubMed/NCBI

60 

Fong PC, Boss DS, Yap TA, Tutt A, Wu P, Mergui-Roelvink M, Mortimer P, Swaisland H, Lau A, O'Connor MJ, et al: Inhibition of Poly(ADP-Ribose) polymerase in tumors from BRCA mutation carriers. N Engl J Med. 361:123–134. 2009. View Article : Google Scholar : PubMed/NCBI

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November 2018
Volume 18 Issue 5

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Copy and paste a formatted citation
APA
Vida, A., Kardos, G., Kovács, T., Bodrogi, B.L., & Bai, P. (2018). Deletion of poly(ADP‑ribose) polymerase-1 changes the composition of the microbiome in the gut. Molecular Medicine Reports, 18, 4335-4341. https://doi.org/10.3892/mmr.2018.9474
MLA
Vida, A., Kardos, G., Kovács, T., Bodrogi, B. L., Bai, P."Deletion of poly(ADP‑ribose) polymerase-1 changes the composition of the microbiome in the gut". Molecular Medicine Reports 18.5 (2018): 4335-4341.
Chicago
Vida, A., Kardos, G., Kovács, T., Bodrogi, B. L., Bai, P."Deletion of poly(ADP‑ribose) polymerase-1 changes the composition of the microbiome in the gut". Molecular Medicine Reports 18, no. 5 (2018): 4335-4341. https://doi.org/10.3892/mmr.2018.9474