- Record: found
- Abstract: found
- Article: found
Microbes and cancer geography: can we exploit recent lessons from the gut system to oral cancer context?
editorial
Publication date (Print):
Jul-Aug 2014
Journal:
Journal of Applied Oral Science
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Dear Readers,
In recent JAOS editorials, oral cancer
4
and oral microbes
3
were independently in
the spotlight, and in this issue, two interesting studies
9,10
bring these topics
together and lead us to an interesting discussion.
Before considering the articles published in this JAOS issue, we must consider the
aspects
raised by Bongers, et al.
2
(2014); Garlet
and Santos
3
(2014). In summary, the
authors elegantly describe the interplay of host microbiota, genetic factors and
inflammation in the development of intestinal neoplasms in mice. Using interesting
experimental tools such as transgenic mice, and simple but insightful strategies such
as
broad-spectrum antibiotics treatment, the authors demonstrate a clear relationship
between
bacteria and site-specific cancer development. Interestingly, the results from Lira's
group
provide experimental evidence clearly in line with 'old' suggestions that bacteria
'might
produce carcinogens'
1
. Evidently the
cancer etiology is really complex, and translation of authors' findings to human cancer
reality requires further investigation
3
.
Also, while some microbes have been implicated as potential carcinogenesis trigger,
probiotics microorganisms have been demonstrated to regulate intestinal inflammation
and
pointed as possible protective factors against the cancer development
6,8
.
Back to oral cavity, one may argue that Bongers, et al.
2
(2014) data may be limited to the gut system, and that is premature
to translate such finding to human oral cancer reality. However, recently an interesting
parallel between the gut and oral cavity was drawn, originally focused on the host-microbe
homeostasis (or the lack of) and its role in the health/disease outcome
5
. Interestingly, the gut and the oral cavity
comprise a mucosal firewall in close contact with a highly diverse and abundant microbial
flora. In both environments the disruption of gut host-microbe homeostasis has been
associated with severe inflammatory pathologies. Please not that Bongers, et al.
2
(2014) study also demonstrates the central
involvement of inflammation in intestinal cancer development, in accordance with several
previous evidences. Therefore, gut and the oral cavity share features such as the
mucosal
nature associated with a profuse microbial flora, and the relatively common susceptibility
for microbial-driven chronic inflammation.
As previously mentioned, it is obviously premature to make any strong statement regarding
a
possible role of oral bacteria in oral cancer only based on the gut-mouth parallel,
but two
studies published in this edition of the JAOS may lead us to interesting
considerations.
Pan, et al.
9
(2014) from Shanghai's
Stomatological Disease Centre and Jiao Tong University, demonstrate a high prevalence
of
drug-resistant microbes (such as methicillin resistant S. aureus [MRSA])
in oral cancer patients. While such finding is properly discussed by the authors within
the
study limitation, and primarily interpreted as a potential side effect of the compromised
immune system due cancer treatment, one may argue if such microbes really appeared
after
the treatment. Indeed, the cross-sectional nature of the study does not allow the
determination of the time of such microbes emergence, based on the association of
microbes
with gut cancer it is possible to speculate that specific bacteria could be not a
cause (a
co-factor perhaps) and not a consequence of cancer (and its immunosuppressive
treatment).
On the other hand, Zhang, et al.
10
(2014)
from Jiamusi University and Qiqihaer Medical University in China demonstrate that
Lactobacillus sp. A-2 metabolites have a potential role in the inhibition of growth
and
induction of apoptosis of human tongue squamous cell carcinoma CAL-27 cells in
vitro. As discussed by the authors, probiotics microorganisms metabolites may
have anti-tumor functions acting directly on cancer cells, but also may exert antimicrobial
activities, which in theory also could interfere in cancer development, at least in
the
scenario described by Bongers, et al.
2
(2014).
Again, we reinforce that the translation of experimental data from the gut to oral
cancer
reality might be only speculative at this time. In a very astute comment focused on
the
bacteria-cancer link in the gut, Jobin
7
(2014) discusses the existence of layers of complexity by highlighting the interplay
between host genetics, microbial location, and tumor geography. The microbial contribution
to cancer development in both gut and oral cavity environments evidently requires
further
investigation, but the similarities between both niches may provide additional valuable
clues to direct future investigations.
Related collections
Most cited references27
- Record: found
- Abstract: found
- Article: found
Regulation of intestinal inflammation by microbiota following allogeneic bone marrow transplantation
Robert Jenq, Carles Ubeda, Ying Taur … (2012)
Regulators of the intestinal flora include diet (Kau et al., 2011), antibiotics (Willing et al., 2011), and, importantly, intestinal inflammation (Sekirov et al., 2010). As a result, the cause–effect relationships between intestinal inflammation and changes in microbiota have been difficult to define (Maloy and Powrie, 2011). The success of allogenic BM transplantation (BMT), a standard therapy for conditions such as hematopoietic malignancies and inherited hematopoietic disorders, is limited by graft-versus-host disease (GVHD) morbidity and mortality (Ferrara et al., 2009). With GVHD, vigorous activation of donor immune cells, most importantly T cells (Korngold and Sprent, 1978), leads to damage of skin, liver, hematopoietic system, and gut. The major sources of immune activation are histocompatibility complex differences between donor and recipient. Combinations of chemotherapy and radiation also contribute, as damage to the intestinal epithelium results in systemic exposure to microbial products normally sequestered in the intestinal lumen (Ferrara et al., 2009). The impact of the microbiota on GVHD is known to be significant. Studies in mice have shown reduction of GVHD with gut-decontaminating antibiotics (van Bekkum et al., 1974) and transplantation in germ-free conditions (Jones et al., 1971). This led to efforts to eliminate bacterial colonization in allogenic BMT patients, combining gut decontamination with a near-sterile environment (Storb et al., 1983). Initial reports were promising, but subsequent studies could not confirm a benefit (Petersen et al., 1987; Passweg et al., 1998; Russell et al., 2000). Other approaches include targeting anaerobic bacteria (Beelen et al., 1999) and introducing potentially beneficial bacteria (Gerbitz et al., 2004), with some reduction of GVHD. These initial studies, however, have been few in number, and no consensus exists between BMT centers regarding how to target the flora. Until recently, a reliance on microbiological culture techniques to characterize flora composition limited these studies. Culture-independent techniques such as ribosomal RNA (rRNA) gene sequencing have demonstrated that a large majority of the estimated 500–1,000 bacterial species present in the human intestinal tract are not detected by culture techniques (Manson et al., 2008). In this study, we readdress the relationship between GVHD and the microbiota in murine and human allogenic BMT recipients. RESULTS AND DISCUSSION Studies of allogeneic BMT using mouse models have characterized exaggerated inflammatory mechanisms that lead to acute GVHD in target organs, including the intestine (Reddy and Ferrara, 2008). MHC-disparate donor/host combinations, including those used in this study, typically result in robust GVHD with full penetrance and rapid kinetics (Schroeder and DiPersio, 2011). The B10.BR→B6 model (H2k→H2b) used in most of our experiments is well-established and has been used in the past by us and others (Blazar et al., 1997, 2000; Penack et al., 2009). On day 14, we evaluated histologically for evidence of GVHD and found villous shortening, increased lymphocytic cell infiltration, crypt regeneration, crypt destruction, and epithelial apoptosis. The number of Paneth cells was also decreased, whereas goblet cells appear to be minimally affected (Fig. 1 A). We then quantified copies of 16S rRNA genes to determine bacterial load. After BMT, we noticed an increase in bacterial load in the ileum (Fig. 1 B), but not in the cecum (unpublished data). This occurred both in the absence and presence of GVHD, suggesting that bacterial expansion may result from reduced host-defense mechanisms in the post-BMT setting. Indeed, we found that levels of IgA in the ileal lumen were decreased after BMT, regardless of GVHD (Fig. 1 C). Figure 1. GVHD in mice produces marked changes in the microbiota. (A) B6 mice were lethally irradiated and transplanted with 5 × 106 B10.BR T cell–depleted BM supplemented with or without 1 × 106 splenic T cells. Features of GVHD are indicated on ileal histology sections from day 14, including lymphocytic infiltration (block arrows), crypt regeneration (enlarged crypts and hyperchromasia), and apoptosis (black arrows). Paneth cells are indicated (blue arrows). Bar, 20 µm. Representative images are shown from one of two independent experiments with similar results. Each dot represents an individual mouse, with bars indicating medians. (B) Quantitation of bacterial load of ileal contents on day 14 was performed by quantitative PCR of 16S rRNA gene copies. Results of a single experiment are shown. (C) Quantification of IgA levels in ileal contents on day 14 was performed by ELISA. Results of a single experiment are shown. (D) Comparison of representation by Unclassified Firmicutes, Barnesiella, and unclassified Porphyromonadaceae from ileal samples. Combined results from three experiments are shown. (E) Diversity of ileal floras from mice with GVHD was determined by the Shannon index. Combined results from two experiments are shown. (F) Principal coordinate analysis of unweighted UniFrac, of ileal floras from B6 mice transplanted with syngeneic (syn) or allogenic (allo) BM with or without T cells. Combined results from three experiments, with data points from each experiment indicated by number. Mice from experiment three were housed individually. (G) Dissimilarity of ileal floras of allo BMT recipient mice without and with GVHD compared with untreated mice by Bray-Curtis index. Combined results of two (Day 7) and 3 (Day 14) experiments are shown. We evaluated for effects on the microbiota by performing 16S rRNA gene sequencing and evaluating microbial diversity, as measured by the Shannon index (Magurran, 2004). Loss of diversity has been found to occur with antibiotic use (Dethlefsen et al., 2008; Ubeda et al., 2010) and increasing age (Woodmansey, 2007), and may predispose mice to disease. Mice undergoing BMT without GVHD showed little change in diversity (unpublished data), but phylogenetic classification of 16S rRNA sequences did show some expansion of unclassified Firmicutes and Barnesiella and mild contraction of unclassified Porphyromonadaceae (Fig. 1 D), demonstrating that radiation does produce some changes in the intestinal flora composition. In contrast, mice with GVHD showed a dramatic loss of bacterial diversity during the first 2 wk after BMT (Fig. 1 E). To quantify changes in the composition of the flora, we used unweighted UniFrac (Lozupone et al., 2006) analyzed by the principal coordinate analysis (PCoA). We found that ileal floras of mice with GVHD were distinct from both those of untreated mice and those of mice after BMT without GVHD (Fig. 1 F). Mice after BMT without GVHD clustered apart from untreated mice inconsistently (one of three experiments); however, comparing the floras using the Bray-Curtis index, we found that GVHD increases dissimilarity from baseline more than BMT alone (Fig. 1 G). We then evaluated for changes in bacterial subpopulations in the setting of GVHD and found large shifts within the phylum Firmicutes, with a dramatic increase in Lactobacillales and decreases in Clostridiales and other Firmicutes in the ileum (Fig. 2 A). Previously, we have shown that the flora from the murine ileum is quite distinct from that of the large intestine, whereas samples within different compartments of the large intestine, including cecum and fresh stool pellets, are similar, although there are some minor changes in representation (Ubeda et al., 2010). Thus, we also evaluated for changes in the cecum with GVHD and found changes similar to those in the ileum, but of lesser magnitude (Fig. 2 B). At the genus level, we found marked ileal expansion of Lactobacillus, the dominant member of Lactobacillales (Fig. 2 C). Housing mice individually from the day of transplant to address the possibility of individual mice influencing the flora of cagemates produced identical results (Fig. 2 C). Within the 16S sequences assigned to the genus Lactobacillus, nearly all had identical sequence homology with Lactobacillus johnsonii, a species found as a commensal in humans (Pridmore et al., 2004) and rodents (Buhnik-Rosenblau et al., 2011), and also in probiotic preparations. Figure 2. GVHD in mice produces marked changes in the microbiota. (A) B6 mice were transplanted with B10.BR donor BM and T cells as in Fig. 1. Comparison of representation by Lactobacillales, Clostridiales, and other Firmicutes from ileal samples. Combined results from three experiments are shown. (B) Comparison of representation by Lactobacillales, Clostridiales, and other Firmicutes from cecal samples. Combined results from two experiments are shown. (C) Bacterial composition at the genus level of ileal flora on day 14 after BMT are depicted with individual mice displayed in each bar. Results of three separate experiments, each displayed in a row, are shown. Additional untransplanted mice were treated with osmotic laxative or DSS starting on day 7 and also individually housed. (D) Mice were transplanted using the strain combinations indicated; mouse vendor was The Jackson Laboratory unless otherwise indicated. Bar graphs show bacterial composition of ileal contents at the genus level for individual mice on day 14. We asked if GVHD-associated changes could be secondary to increased gut motility. We evaluated the effects of an osmotic laxative, as well as enteritis caused by dextran sodium sulfate, and found changes with both agents that were distinct from GVHD (Fig. 2 C). Together, these results suggest that GVHD changes the flora in a unique, reproducible pattern. The flora of mice can vary widely from colony to colony. Our data presented thus far used B6 recipient mice from The Jackson Laboratory; we also performed BMT experiments using additional strains and vendors. With BALB/c host mice from The Jackson Laboratory, we found an abundance of Lactobacillus in mice with GVHD, although the floras of BALB/c mice are dominated by Lactobacillus at baseline (unpublished data). In the CD4-driven MHC II–disparate B6→BM12 model, we also found characteristic expansion of Lactobacillus with GVHD (Fig. 2 D). This indicated that alloreactive CD4 T cells are sufficient and do not require CD8 T cells to produce changes in the flora. Interestingly, in two additional models with B10.BR hosts from The Jackson Laboratory and B6 hosts from Charles River Laboratories, we noted expansion of Enterobacteriales with GVHD (Fig. 2 D). Enterobacteriales from both strains appears to be of the same type, an unclassified Enterobacteriaceae that, in our experience, is rarely detectable in B6 mice from The Jackson Laboratory (3 of 71 mice). Expansion of Enterobacteriaceae has been reported before in Japanese (Eriguchi, Y., S. Takashima, N. Miyake, Y. Nagasaki, N. Shimono, K. Akashi, and T. Teshima. 2010. ASH Annual Meeting Abstracts. Abstr. 244) and German (Heimesaat et al., 2010) mouse colonies. Collectively, these data suggest that Lactobacillales and Enterobacteriales (both capable of surviving in aerobic environments) may populate a niche that expands with GVHD at the expense of obligate anaerobes, including Clostridiales and other Firmicutes. Whether Lactobacillales or Enterobacteriales expand appears to depend on the presence of these organisms in the baseline flora. The potential impact of these expanding populations on GVHD has not been well-described. Treatment of B6 mice with ampicillin, followed by a recovery period, results in loss of Lactobacillus from the flora, with expansion of other commensal bacteria (Ubeda et al., 2010) such as Blautia (order Clostridiales; Fig. 3 A). We cultured the predominant L. johnsonii endogenous to B6 mice from The Jackson Laboratory and found that reintroduction after ampicillin treatment restores representation (Fig. 3 A). We then used ampicillin and L. johnsonii reintroduction as tools to test if expansion of Lactobacillales with GVHD could have clinical repercussions. Surprisingly, upon development of GVHD, mice treated with ampicillin before BMT showed loss of Blautia and emergence of Enterococcus (order Lactobacillales; Fig. 3 A). Mice that received L. johnsonii reintroduction after ampicillin showed domination with L. johnsonii and no expansion of Enterococcus (Fig. 3 A). We found similar results in the B6→BM12 model, though BM12 mice after ampicillin treatment demonstrated expansion of both Enterococcus and Enterobacteriaceae with GVHD (Fig. 3 B). BM12 mice that received L. johnsonii reintroduction after ampicillin also showed domination with L. johnsonii and no expansion of Enterococcus or Enterobacteriaceae. This occurred even when using monoclonal BM12-specific donor T cells from TCR transgenic ABM (Sayegh et al., 2003) RAG-1 deficient mice, suggesting that a broad alloreactive T cell repertoire is not required to produce changes in the microbiota with GVHD. Figure 3. Composition of intestinal flora can impact on severity of intestinal GVHD. (A) Schematic of treatment: B6 mice received ampicillin for 1 wk, followed by a 2-wk recovery period with unmodified drinking water; some were gavaged every 2 d with L. johnsonii (Lacto) of B6 flora origin during recovery, followed by harvest or BMT. Ileal contents were evaluated on days 0 (no BMT) and 14 after BMT. Bar graphs show bacterial composition of ileal contents at the genus level for individual mice. (B) Similar to as in A, BM12 mice received ampicillin followed by recovery; some also received L. johnsonii reintroduction. GVHD was induced upon transplantation with BM and either wild-type CD4 T cells (500K) or ABM RAG1 KO TCR transgenic CD4 T cells (100K). (C) B6 mice were treated with ampicillin, and then were or were not gavaged with L. johnsonii and transplanted with B10.BR BM and T cells. (top) Survival data combined from two experiments with similar results. (bottom) Pathological scores of GVHD target organs on day +21. We then evaluated effects of flora manipulation on GVHD severity, focusing on the B10.BR→B6 model. Notably, ampicillin treatment before BMT resulted in worsened GVHD survival. Histologically, these mice had evidence for increased GVHD pathology in the small and large intestines, including epithelial damage and increased inflammation. Remarkably, L. johnsonii reintroduction prevented increased GVHD lethality and pathology (Fig. 3 C). Enterococcus has not been described as a potential contributor to gut GVHD, though enterococcal bacteremia occurs often in patients with GVHD (Dubberke et al., 2006). In mouse models, Enterococcus can contribute to gut inflammation by compromising epithelial barrier integrity (Steck et al., 2011) and stimulating TNF production from macrophages (Kim et al., 2006). Thus, one mechanism by which L. johnsonii may reduce GVHD severity could be prevention of Enterococcus expansion which may exacerbate GVHD-associated intestinal damage and inflammation. We then studied the relationship between the flora and GVHD in humans. We collected weekly stool samples from allogenic BMT patients during transplant hospitalization at our center. Of 9 patients who developed gut GVHD during hospitalization, 8 developed symptoms early, with GVHD onset clustering between days 18 and 21; these 8 were selected for our GVHD cohort. 18 additional patients provided weekly samples through day 21; of these, 10 met our prospective eligibility for inclusion in our non-GVHD cohort, with survival to at least day 30 and absence of GVHD in any target organ through day 100. Clinical parameters for included patients are summarized in Fig. 4 A. Importantly, non-GVHD and GVHD patients had similar exposures to antibiotics during the period of stool collection. Figure 4. GVHD produces marked changes in the microbiota of humans, and the microbiota may affect risk of developing GVHD. (A) Summary of clinical parameters of non-GVHD and GVHD patients. (B) Flora diversity, by Shannon index, of stool samples after BMT. Individual measurements of diversity are displayed, as well as moving averages and P values calculated for 10-d intervals. (C) Contribution of bacterial populations in samples during two time periods, days 0 to 13 and 14 to 21 after BMT. (D) Microbial chaos of stool samples by mean Bray-Curtis time index from pre-BMT to day 13 after BMT. We first examined the effects of GVHD on flora diversity. We found that before GVHD, patients had flora diversity similar to controls but lost diversity over time, particularly after GVHD onset (Fig. 4 B). Thus, GVHD is associated with loss of flora diversity in humans, similar to in mice. We then looked for bacterial populations that changed with the onset of GVHD. Interestingly, we discovered increases in Lactobacillales and decreases in Clostridiales, a pattern identical to our findings in mice. Other populations, as well as classifications at the family or genus level, were otherwise not significantly changed (unpublished data). Importantly, we did not identify these shifts in non-GVHD patients (Fig. 4 C), suggesting that these flora changes were indeed a result of GVHD rather than BMT or antibiotic exposure. Our sample size did not identify specific populations as potential risk factors for subsequent GVHD. Patients who later developed GVHD, however, did have significantly greater microbial chaos early after BMT (before our observed GVHD-associated changes), which we quantified using the Bray-Curtis dissimilarity index (Magurran, 2004) over time (Fig. 4 D). This suggests that large fluctuations in the microbiota early on may lead to an increased risk of GVHD. In conclusion, our findings demonstrate the influence of inflammation on the structure of the intestinal microbiota after allogenic BMT in both mice and humans. The flora, in turn, can modulate severity of intestinal inflammation. Our mouse experiments indicate that antibiotic exposure before BMT, which occurs commonly in patients with hematologic malignancies, may be a risk factor for subsequent intestinal GVHD. This may be remedied with targeted flora reintroduction to potentially reduce the severity of gut GVHD. MATERIALS AND METHODS Mouse BMT experiments. All mouse procedures were performed in accordance with institutional protocol guidelines at Memorial Sloan-Kettering Cancer Center (MSKCC). Mice were maintained according to National Institutes of Health Animal Care guidelines, under protocols approved by the MSKCC Institutional Animal Care Committee describing experiments specific to this study. Mouse BMT experiments were performed as previously described (Penack et al., 2010). Mice received 11 Gy divided in 2 split doses 3–4 h apart. All BMT experiments were performed at Memorial Sloan-Kettering with the exception of the BALB/c into B6/CR experiment in Fig. 2 D, which was performed at University of Minnesota. All mice were obtained from The Jackson Laboratory, with the exception of B6 mice from Charles River in Fig. 2 D, and ABM mice (Sayegh et al., 2003) in Fig. 3 B, which were provided by M. Sayegh (Brigham and Women’s Hospital and Children’s Hospital Boston, Boston, MA) and had been backcrossed onto a B6 background for at least 20 generations, and then crossed on a RAG-1–deficient background derived from The Jackson Laboratory, previously backcrossed 10 times to B6 background. Mice were either co-housed three to five mice/cage in all experiments, or were housed individually as indicated in experiment three of Fig. 1 F and Fig. 2 C. GVHD clinical and histological scoring. Mice were monitored daily for survival and weekly for GVHD clinical scores (Cooke et al., 1996). Small intestine, large intestine, and liver samples were evaluated histologically for evidence of GVHD and scored as previously described (Hill et al., 1997). Laxative and dextran sodium sulfate (DSS) treatments. Mice were treated with drinking water containing osmotic laxative (60 g/l polyethylene glycol 3350, 1.46 g/l NaCl, 0.745 g/l KCl, 1.68 g/l NaHCO3, and 5.698 g/l Na2SO4) or DSS 3.5% for 7 d. A 7-d course of treatment was selected to better compare with GVHD-induced intestinal changes, which first requires alloactivation and expansion of donor T cells. Ampicillin treatment, Lactobacillus isolation, and reintroduction. Mice were given 1 g/l ampicillin in their drinking water during 7 d, followed by a recovery period with normal drinking water for 14 d. The dominant Lactobacillus strain from the small intestine of B6 mice (The Jackson Laboratory) was isolated by plating contents under anaerobic conditions on plates with Lactobacilli MRS agar (BD). 16S rRNA was sequenced and classified using the ribosomal RDP classifier, and confirmed using MOTHUR to be identical to the operational taxonomic unit (OTU) most predominant in B6 mice (The Jackson Laboratory). 1 d after stopping ampicillin treatment, 108 CFUs of the isolated Lactobacillus strain were given to mice by oral gavage every other day during the 14-d recovery period. Patient selection. We collected stool samples on a weekly basis from allogenic BMT patients from 8/29/09 to 5/24/11. Patients were identified that developed upper gut GVHD symptoms (nausea, vomiting, and loss of appetite) or lower gut GVHD symptoms (abdominal discomfort and diarrhea). 8 had onset of symptoms at a similar time point, between days 18–21, and were selected for our GVHD cohort; the ninth patient had GVHD onset on day 27. 7 underwent confirmatory biopsy; 1 could not be biopsied because of thrombocytopenia. Treatment for GVHD began on days ranging from 26 to 48, and thus the majority of samples that were analyzed were collected before initiation of corticosteroids. Six had symptoms of upper intestinal GVHD and were all treated with the oral corticosteroid budesonide; two also had symptoms of lower intestinal GVHD and were treated with intravenous methylprednisolone. Antibiotic exposures were tabulated from day −7 to 21 after BMT. Standard antibiotic guidelines were followed, including prophylaxis with intravenous vancomycin starting at day −2, and empirical treatment of neutropenic fever with piperacillin/tazobactam, or in patients with allergies, cefepime, or aztreonam. The study protocol was approved by the Memorial Sloan-Kettering Cancer Center Institutional Review Board; informed consent was obtained from all subjects before collection procedures. Sample collection and DNA extraction. Stool samples from patients were stored at 4°C for <24 h before freezing at −80°C. Ileal and cecal samples from mice were frozen at −80°C. DNA was extracted using one of the two methods, which give similar results. In Fig. 1, Fig. 2 (A–C), Fig. 3, and Fig. 4, DNA was extracted using a phenol-chloroform extraction technique (Ubeda et al., 2010). In Fig. 2 D, DNA was extracted from samples using Power Soil DNA isolation kit (MO BIO Laboratories). Quantification of gut flora bacterial density. Gut flora bacterial density was quantified as previously described (Ubeda et al., 2010). IgA quantification. Ileum contents were resuspended in 1 ml of a 3:1 mixture of PBS/0.1 M EDTA containing soybean trypsin inhibitor (type II-S; Sigma-Aldrich) at a concentration of 0.1 mg/ml. The mixture was centrifuged at 12.000 rpm for 10 min, and the supernatant was collected for the assay. Plates were coated with 100 µl/well of rat anti–mouse IgA (SouthernBiotech) at a dilution of 1:1,000 in 50 mM carbonate buffer, pH 9.6. After blocking and washing of plates, 100 µl/well serial dilutions of the previously prepared mouse intestinal samples were added and plates were incubated overnight at room temperature. Bounded antibody was detected by incubating plates at 37°C for 1 h with goat anti–mouse IgA-HRP conjugate at a dilution of 1:1,000 in PBS-T-0.1% BSA. Plates were developed with 2, 2′-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid) (Sigma-Aldrich) and 0.03% H2O2 (Sigma-Aldrich), and optical density was determined using a Vmax microplate reader (Molecular Devices) at 405 nm kinetically for 20 min at 14-s intervals. Total ileum content IgA was calculated using a mouse IgA standard (Kappa TEPC 15; Sigma-Aldrich). 16S rRNA gene amplification, 454 pyrosequencing. For each sample, 3 replicate 25-µl PCRs were performed. Each PCR contained 50 ng of purified DNA, 0.2 mM dNTPs, 1.5 mM MgCl2, 1.25 units of Platinum Taq DNA polymerase, 2.5 µl of 10× PCR buffer, and 0.2 µM of each primer designed to amplify the V1-V2 (Ubeda et al., 2010; Fig. 1 and Fig. 2) or V1-V3 (Fig. 3 and Fig. 4) 16S rRNA variable regions, as described in the Human Microbiome Project Provisional 16S 454 Protocol (http://www.hmpdacc.org/tools_protocols/tools_protocols.php). The cycling conditions used were: 94°C for 3 min, followed by 25 cycles (cecum and fecal samples) or 28 cycles (ileum samples) of 94°C for 30 s, 52°C (V1-V2) or 56°C (V1-V3) for 30 s, and 72°C for 1 min. Replicate PCRs were pooled and amplicons were purified using the QIAquick PCR Purification kit (QIAGEN). PCR products were sequenced on a 454 GS FLX or 454 GS FLX Titanium platform following the recommended procedures (Roche). Sequence analysis. Sequence data were compiled and processed using MOTHUR (Schloss et al., 2009). Sequences were aligned to the 16S rRNA gene, using as a template the SILVA reference alignment and the Needleman-Wunsch algorithm with the default scoring options. Potentially chimeric sequences were removed using the ChimeraSlayer program. To minimize the effect of pyrosequencing errors in overestimating microbial diversity, rare abundance sequences that differ in 1 or 2 nt from a high abundant sequence were merged to the high abundant sequence using the pre.cluster option in MOTHUR. Sequences were grouped into OTUs using the average neighbor algorithm. Sequences with distance-based similarity of 97% or greater were assigned to the same OTU. Mouse samples were processed and sequenced as individual experiments, and resulting sequences were analyzed together with all other samples within each figure panel. Human samples were processed and sequenced in batches, and resulting sequences were analyzed together. Sequences from all experiments have been deposited in the Sequence Read Archive of National Center for Biotechnology Information, submission number SRA049925. Determining diversity, phylogenetic classification, dissimilarity, microbial chaos, and UniFrac PCoA. OTU-based microbial diversity was estimated by calculating the Shannon diversity index (Magurran, 2004) using MOTHUR. Phylogenetic classification was performed for each sequence, using the Bayesian classifier algorithm described by Wang et al. (2007) with the bootstrap cutoff at 60%. A phylogenetic tree was inferred using clearcut on the 16S sequence alignment generated by MOTHUR. Microbial chaos was quantified by mean Bray-Curtis time index, calculated as follows: Bray-Curtis dissimilarity index (Magurran, 2004) between temporally adjacent samples was quantified using MOTHUR and divided by the length of the time interval (in days) between samples, starting with the last sample obtained before the transplant and all samples obtained until day 13. Unweighted UniFrac was run using the resulting tree (Lozupone et al., 2006). PCoA was performed on the resulting matrix of distances between each pair of samples. Statistical comparisons. Shannon diversity index in Fig. 4 B for 10-d intervals compared using unpaired two-sided Student’s t tests with a more stringent cut-off of 0.0125 given multiple comparisons, by the Bonferroni correction for 4 time periods of independent comparisons. Comparisons of bacterial populations in Fig. 3 C using paired two-sided Wilcoxon matched pairs test for individual patients. In Fig. 4 C, Change in Clostridiales was compared using a two-sided Student’s t test, with normality confirmed by D’Agostino and Pearson omnibus test with α = 0.05. All other comparisons were done using two-sided Mann-Whitney tests.
- Record: found
- Abstract: not found
- Article: not found
Bacteria and the aetiology of cancer of the large bowel.
B Drasar, M. J. Hill, R. Williams … (1969)
- Record: found
- Abstract: found
- Article: found
Interplay of host microbiota, genetic perturbations, and inflammation promotes local development of intestinal neoplasms in mice
Gerold Bongers, Michelle E. Pacer, Thais Geraldino … (2014)
The preferential localization of some neoplasms in specific areas of the intestine suggests that nongenetic factors may be important for their development. In contrast to adenomas, which occur throughout the large intestine, serrated polyps (SPs) occur in specific areas of the gut in a subtype-specific manner (Huang et al., 2004; Noffsinger, 2009). SPs encompass a heterogeneous set of lesions and are associated with perturbations of the MAPK pathway through, e.g., activating mutations in KRAS or BRAF (Noffsinger, 2009). We have recently shown that EGFR activation is associated with SPs in human biopsies and that expression of the EGFR ligand HB-EGF in transgenic mice (HBUS mice) promotes development of SPs that mostly resemble human hyperplastic polyps (Bongers et al., 2012). Strikingly, despite expression of the HB-EGF transgene throughout the gut, SPs were only observed in the cecum, suggesting that beside genetic alterations, environmental factors played a pivotal role in their development. In the intestine, the microbiota is in close proximity to the intestinal epithelial cells that form a protective barrier separating commensal bacteria from the host. Changes in the microbiota have been associated with inflammatory conditions and cancer (Plottel and Blaser, 2011; Honda and Littman, 2012; Schwabe and Jobin, 2013). For example, activation of TLR4 by the intestinal microbiota is important for the promotion of hepatocellular carcinoma induced by the carcinogen diethylnitrosamine/CCl4 (Dapito et al., 2012). In the gastrointestinal (GI) tract, the presence of Helicobacter pylori is strongly associated with an increased risk for the development of peptic ulcers, gastric mucosa–associated lymphoid tissue tumors, and gastric adenocarcinomas (McColl, 2010). The colonic microbiota has also been suggested to play a role in the pathogenesis of colorectal cancer (Cho and Blaser, 2012), either by provoking an inflammatory response or alterations in metabolic processing (Plottel and Blaser, 2011). Specifically, activation of Th17 cells by the enterotoxigenic Bacteroides fragilis has been shown to promote colonic tumorigenesis in the APCmin mouse model (Wu et al., 2009). More recently, the polyketide synthase genotoxic island expressed by Escherichia coli NC101 was implicated in the development of carcinomas in Il10−/− mice treated with the colonic carcinogen azoxymethane (Arthur et al., 2012). In this study we explored the possibility that the topological distribution of SPs was dependent on the microbiota. We show that alterations of the microbiota induced by antibiotic treatment or by embryo transfer rederivation attenuated the formation of cecal SPs in HBUS mice. SP development was associated with bacterial invasion of the lamina propria, which was associated with production of Il-17 by innate immune cells, neutrophil recruitment, and production of antimicrobials. Together these results indicate that the interplay between genetic changes in the host, an inflammatory response, and a host-specific microbiota accounts for the development of SPs in HBUS mice. RESULTS Antimicrobial defense response genes are induced in SPs Expression of the transgenes in HBUS mice (Bongers et al., 2012) is highest in the small intestine (Fig. 1 A), but HBUS mice only develop SPs at the cecal–colonic junction (Fig. 1 B). To further elucidate potential pathways involved in the formation of SPs, we compared the transcriptome of SPs in HBUS mice with adjacent histologically normal cecal tissue (n = 3). Whole transcriptome sequencing (RNA-Seq) revealed 404 genes with increased and 272 genes with decreased expression in SPs, respectively (Fig. 1 C and Table S1; false discovery rate [FDR] Q 25 genes; kappa 0.5). (F and G) Increased transcript levels (left, n = 3/group) and immunoreactivity (right, n = 8/group) of REG-3β (F) and REG-3γ (G) in SPs compared with surrounding tissue (representative figure of three independent experiments). All histology sections were counterstained with DAPI (F and G) and pan-Keratin (F) or E-cadherin (G). Bars: (F) 250 µm; (G) 100 µm. Interestingly, we found a significant enrichment of the GO overview term associated with “response to molecule of bacterial origin” (Fig. 1 E and Table S2). This encompassed the GO term for “response to bacterium” and can be triggered in response to the presence of a bacterium and includes genes associated with bactericidal activity such as Arg1, Ptgs2 (also known as COX-2), Serpine1, Reg3b, and Reg3g (Fig. 1, D and E). REG-3β and REG-3γ have previously been shown to play an important role in bacterial defense (Dessein et al., 2009; van Ampting et al., 2012) and were among the genes with the largest increase in expression in SPs compared with surrounding tissue (12- and 32-fold, respectively; Fig. 1 C). Immunostaining of cecal sections localized REG-3β and REG-3γ expression to cells in the basal epithelium of the SPs (Fig. 1, F and G), whereas they were mostly absent in unaffected surrounding cecal tissue. This localization pattern suggested that the epithelium of the SPs was actively responding to bacteria present in the local environment. Broad-spectrum antibiotic treatment prevents SP development in HBUS mice The elevated expression of antimicrobial genes in SPs prompted us to investigate whether the intestinal microbiota played a role in SP pathogenesis. To this end, we treated HBUS mice for 9 or 29 wk with a broad-spectrum antibiotic cocktail (metronidazole, ampicillin, neomycin, and vancomycin) in the drinking water and assessed the presence of SPs after 15 or 35 wk (Fig. 2 A). Treatment with antibiotics led to a significant decrease in the relative abundance of bacterial 16S ribosomal DNA (rDNA; Fig. 2 B) and α diversity (species richness, P 0.05; Fig. 3 E). Collectively, our results demonstrate that the presence of a host-specific microbial environment is key to the development of cecal SPs in HBUS mice. Identification of a subset of bacteria enriched in cecal mucosa of SPs Next, we sought to more narrowly define which bacteria contributed to polyp formation. To do so, we compared the cecal mucosa–associated microbiota of affected HBUS mice with those of unaffected rederived HBUS mice, antibiotic-treated HBUS mice, and cohoused WT control littermates. We defined operational taxonomic units (OTUs) based on sequence similarity of the 16S rRNA fragments with Greengenes reference sequences (McDonald et al., 2012). This resulted in the identification of 704 unique OTUs across all samples after filtering as described in Materials and methods (Table S3). Hierarchical clustering (Fig. 4 B) and weighted UniFrac distances (Fig. 3 D) of OTU abundance profiles separated the microbiota of the individual sample groups, whereas WT mice clustered with cohoused HBUS mice that developed SPs (Fig. 4 B). At the phylum level, we found a distinct expansion of Verrucomicrobia and a decrease in Deferribacteres in SPs compared with rederived HBUS mice (Fig. 4 A). Closer inspection revealed these differences to be caused by the increased abundance of Akkermansia muciniphila (Greengenes ID: 4306262) and a decrease of Mucispirillum schaedleri (Greengenes ID: 1136443). Although enriched in SPs, A. muciniphila is one of the few species resistant to long-term treatment of HBUS mice with broad-spectrum antibiotics (Fig. 4 D, arrowhead), which makes it unlikely to be important for SP development. Figure 4. Biome analysis of SPs. (A) Relative abundance of phyla present in HBUS mice with SPs (n = 20), WT littermates (n = 7), rederived HBUS mice (Red; n = 13), and HBUS mice treated with antibiotics (Abx; n = 11). Data shown represent the most abundant phyla, whereas low abundant and unclassified OTUs were grouped in “Other.” (B, top) Pearson hierarchical clustering of the abundance profiles of all 703 OTUs after filtering. Phyla (P) are colored according to the legend in A. Mice were clustered (Spearman) by cage (color coded, top) and sample group (colored according to the legend). (bottom) Number of OTUs above background in each sample (OTU count) and fraction of total sample reads accounted for by the set of 703 filtered OTUs (read fraction). (D) Pearson hierarchical clustering identified four major clusters (C1–C4) in the abundance profiles of 106 OTUs that were significantly different between HBUS mice with SPs and rederived HBUS mice or WT controls (ANOVA Q 1.6). OTUs significantly enriched in SPs compared with rederived (R), WT (W), or antibiotics-treated (A) mice are shaded blue (left). Phyla (P) are colored according to the legend in A. OTU abundance is expressed as the log2-normalized read count in each sample. The OTU corresponding to A. muciniphila is indicated (arrowhead). (E) 15 OTUs from C2 that were present in >75% of SPs. OTUs are ranked by abundance (vertical axis) and according to abundance within each dataset (horizontal axis). OTU are annotated with Greengenes ID, color coded phyla (P) annotations (according to A), taxonomic family, and genus assignments. Each bar (A and C) or column (B, D, and E) represents a different mouse. To determine which OTUs were specially associated with SPs, we compared the relative abundance of OTUs in SPs with cohoused WT, rederived, and antibiotic-treated HBUS mice by ANOVA (Q fold > 1.6; Table S4). Cluster analysis led to the identification of four distinct clusters (C1–C4; Fig. 4 D). OTUs present in clusters C1, C3, and C4 were also present in rederived or antibiotic-treated HBUS mice, i.e., HBUS mice that did not develop SPs. The second cluster (C2) represented 44 OTUs that were specifically enriched in SPs (Fig. 3 B). Of these, only 15 OTUs were found in >75% of all SP samples (Fig. 3 C). Bacterial infiltration SPs and decreased barrier function in SPs Various bacteria have tissue-invasive properties (Pizarro-Cerdá and Cossart, 2006; Sartor, 2008). To examine whether bacteria were present within SPs, we performed in situ hybridization with a eubacterial probe. As expected, probe signal was observed throughout the cecal lumen. In the lamina propria of SPs we observed the presence of bacteria, but not in unaffected surrounding cecal tissue. The invasive bacteria were found in close proximity to infiltrating neutrophils (Fig. 5 A). The majority (7/15) of OTUs enriched in HBUS SPs belonged to the order of Clostridiales. To test whether the invasive bacteria corresponded to these OTUs, we performed in situ hybridization with a Clostridium coccoides–Eubacterium rectale probe (Clostridium cluster XIVa and XIVb, pb-00963) that targets all the Clostridiales that were increased in HBUS SPs compared with the rederived HBUS mice (Fig. 5 B). Analysis of HBUS SPs and surrounding tissue revealed positive signal for this probe within the lamina propria of eight out eight tested HBUS polyps (Fig. 4 B). In antibiotic-treated HBUS mice, as expected, no signal was observed (Fig. 6). To examine whether Clostridiales would be of relevance to the formation of SPs in HBUS mice, we treated HBUS mice for 9 or 29 wk with 0.5 mg/ml vancomycin in the drinking water and assessed the presence of SPs after 15 or 35 wk. Treatment with vancomycin targets gram-positive bacteria and shifts the composition of the microbiota, particularly the family Clostridiales/Lachnospiraceae, without affecting the total number of cecal bacteria (Sekirov et al., 2008; Ubeda et al., 2010; Willing et al., 2011). Vancomycin-treated HBUS mice showed a significant decrease in incidence (11%, P = 0.002, n = 9) and size of SPs (Fig. 5 C). Figure 5. Bacterial infiltration SPs and decreased barrier function in SPs. (A and B) In situ hybridization with a eubacterial probe (A) or Clostridium cluster XIVa and XIVb (pb-00963; B) on frozen sections obtained from HBUS mice. Shown is a representative image obtained from two independent experiments of surrounding cecal tissue (Surr.) and an SP (middle; higher magnification of § on right; n = 6). S100A9-positive cells are indicated by asterisks. Arrowheads indicate bacteria that invaded the lamina propria (A) or bacteria recognized by pb-00963 that invaded the lamina propria (B). (C) At 35 wk of age, the drinking water of HBUS mice was supplemented with 1 mg/ml vancomycin, whereas control mice were maintained on regular water. After 4 wk of treatment, HBUS mice were checked for the presence of SPs by gross and histological analysis. Shown is SP size of HBUS mice treated with vancomycin for 4 wk starting at 35 wk of age (n = 9) compared with age-matched HBUS controls on water (n = 17). ***, P fold > 1.5; Fig. 7 A and Table S5), consistent with a signature of activated leukocytes, as well as “response to bacterium” (Fig. 7 B and Table S6). Analysis of cytokine and chemokine expression by BeadArray and quantitative PCR (qPCR) showed that the proinflammatory cytokines Il1-α, Il1-β, and Tnf and the chemokines Ccl1, Ccl2, Ccl17, Cxcl2, and Cxcl16 were significantly up-regulated in SPs compared with unaffected cecal tissue (n = 5; Fig. 5 C). Figure 7. Marked inflammatory changes in SPs. (A) Leukocytes (CD45+ cells) isolated from HBUS SPs and surrounding (Surr.) tissue were analyzed by Illumina BeadArray/limma. Quantile-normalized expression values were analyzed using a paired design (n = 3/group) and filtered for Q fold change > 1.5. Z score–normalized data were subjected to hierarchical clustering (left): red indicates increased and green indicates decreased expression in SPs compared with surrounding tissue. Plot of logFC (log fold change) versus mean expression (right) of all detected transcripts (gray) and significant genes (153 genes; black). (B) ClueGO analysis of significantly regulated genes in an Illumina BeadArray analysis of SPs of HBUS mice compared with unaffected surrounding cecal tissue. Shown are GO overview terms selected by %Genes/Term in color (Q 15 genes; kappa 0.5). (C) Cytokine and chemokine mRNA expression in tissue obtained from SPs compared with unaffected surrounding proximal cecal tissue (n = 5/group). *, P 50 per 10× field) compared with unaffected cecal tissue where MMP-3–positive cells were only found in the submucosa ( fold change > 1.5, Q fold change > 1.6. Abundance profiles were hierarchically clustered using Spearman correlation as the distance metric, and heat maps were generated using R. RNA extraction from tissues. Approximately 200 mg of tissue sample was placed in 1.5 ml RNAlater buffer (Ambion) and snap-frozen in liquid nitrogen. RNA extraction was performed as described previously (Giannoukos et al., 2012). In brief, at time of extraction, samples were centrifuged for 10 min at 16,000 g at room temperature. Pellets were resuspended in 150 µl lysis buffer (Tris/HCl, pH 8, 1 mM EDTA, 15 mg/ml Lysozyme [Sigma-Aldrich], and 15 µl of 20 mg/ml proteinase K) and incubated at room temperature for 10 min with brief mixing every 2 min. After addition of 1.2 ml QIAGEN RLT buffer containing 1% vol/vol β-mercaptoethanol, 1 ml of 0.1-mm glass beads (BioSpec) was added, and samples were homogenized in a FastPrep at setting 5 (four pulses of 20 s). Samples were kept on ice for 1 min between pulses. Lysates were then homogenized with a QIAshredder spin column, and RNA was isolated using the AllPrep mini kit (QIAGEN), according to the manufacturer’s protocols, which included an on-column digestion with DNase I. 25 µg RNA from each tissue sample was processed with the MICROBEnrich kit (Ambion), and 5 µg of processed RNA was further depleted of rRNAs using the Meta-Bacteria RiboZero rRNA removal kit (Epicentre). The final samples consisted of a mix of host and microbial mRNAs in a 2:1 ratio. cDNA library construction and sequencing. rRNA-depleted RNA was prepared for Illumina paired-end sequencing using the Next mRNA Library Prep Master Mix Set for Illumina (New England Biolabs, Inc.); manufacturers’ protocols were followed with the following modifications. RNA was fragmented for 10 min and then purified with RNeasy MinElute spin columns (QIAGEN). RT was performed with SuperScript III (Invitrogen). Library preparation reactions were cleaned up using Agencourt AMPure XP beads. Size selection was performed before ligation-mediated PCR using Invitrogen E-Gel 2% with SYBR Safe staining. Excised gel fragments were purified with the QIAQuick Gel Extraction kit (QIAGEN). Adapters and primers were synthesized by IDT according to published Illumina sequences. Enrichment PCR was performed with Kapa HiFi HotStart ReadMix. Primers were used at a final concentration of 500 nM; cycling parameters were as follows: 94°C for 5 min, 15 cycles of 94°C for 1 min, 62°C for 30 s, 72°C for 45 s, and then 72°C for 10 min. Libraries were quantified using the BioAnalyzer DNA 1000 chips (Agilent Technologies), diluted to 12 pM, and sequenced for 100 cycles (paired-end) on the HiSeq 2000 (Illumina) using standard methods. Mouse transcriptome analysis. RNA-Seq data from HBUS tissue samples were mapped to the mouse reference genome and transcriptome (GRCm38 and the Jul 2012 ENSEMBL gene build, respectively) using Tophat (Trapnell et al., 2009). Gene-level sequence counts were extracted for all annotated protein-coding genes using htseq-count by taking the strict intersection between reads and the transcript models associated with each gene. Raw count data were filtered to remove low expressed genes with less than five counts in any sample. Remaining data (12,697 genes) were normalized with trimmed mean of M (TMM) normalization and analyzed for differentially expressed genes using the Bioconductor EdgeR package version 2.11 Bioconductor/R (Robinson et al., 2010). To take into account the experimental design where paired unaffected cecal tissue and polyp tissue samples were isolated for each mouse, we fitted an additive generalized linear model that incorporated mouse + tissue effects to adjust for any baseline differences between the mice. Statistically significant differentially expressed genes between polyp and surrounding cecal (normal) tissues (Q < 0.05) were selected in gene-wise log-likelihood ratio tests that were corrected for multiple testing by Benjamini and Hochberg FDR. GO analysis on significantly regulated genes was performed using ClueGO (Bindea et al., 2009) for GO terms containing at least 25 genes, redundancy was reduced using GO term fusion, connections were based on kappa 0.5, the leading overview GO term was selected based on %Genes/Term, and GO terms with Q < 0.05 (FDR) were considered significantly enriched. Accession numbers. Accession numbers for all primary array and sequencing data are available from the NCBI under BioProject accession no. PRJNA207540 and GEO accession no. GSE47736. Statistical analysis. Statistical analysis for BeadArray, Microbiome, and RNA-Seq was performed as described above. For all other experiments, differences among means were evaluated by a 2 × 2 contingency table using Fisher’s exact test (Prism version 5; GraphPad Software), a two-tailed Wilcoxon rank sum test, or pairwise Wilcoxon rank sum test (CRAN/R version 3.0.2); P < 0.05 was considered significant. For multiple comparisons, p-values were adjusted using Benjamini and Hochberg (FDR). No samples were excluded from analysis. All results shown represent mean ± SEM. Online supplemental material. Table S1, included as a separate PDF file, shows differentially regulated genes by RNA-Seq analysis in tissue isolated from HBUS polyps compared with unaffected proximal cecum, as show in the heat map. Table S2, included as a separate PDF file, shows ClueGO analysis of the RNA-Seq analysis in tissue isolated from HBUS polyps compared with unaffected proximal cecum. Table S3, included as a separate PDF file, shows a subsampled and filtered OTU table of Taconic moms, rederived HBUS mice, and HBUS and WT mice obtained through interbreeding with mice obtained from the Jackson Laboratory. Table S4, included as a separate PDF file, shows statistical analysis of OTUs in Table S3. Table S5, included as a separate PDF file, shows differentially regulated genes by BeadArray in CD45+ cells isolated from HBUS polyps compared with unaffected proximal cecum, as show in the heat map. Table S6, included as a separate PDF file, shows ClueGO analysis of the BeadArray analysis in CD45+ cells isolated from HBUS polyps compared with unaffected proximal cecum. Online supplemental material is available at http://www.jem.org/cgi/content/full/jem.20131587/DC1. Supplementary Material Supplemental Material
Author and article information
Journal
Journal ID (nlm-ta): J Appl Oral Sci
Journal ID (iso-abbrev): J Appl Oral Sci
Title:
Journal of Applied Oral Science
Publisher:
Faculdade de Odontologia de Bauru da Universidade de São
Paulo
ISSN
(Print):
1678-7757
ISSN
(Electronic):
1678-7765
Publication date
(Print and electronic):
Jul-Aug 2014
Publication date
(Print):
Jul-Aug 2014
Volume: 22
Issue: 4
Pages: 249-250
Affiliations
Article
DOI: 10.1590/1678-77572014ed004
PMC ID: 4126818
SO-VID: 406fb445-d981-4000-9f11-c6f309f5e166
License:
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
History
Categories
Subject:
Editorial
Data availability: