Potential role of gastrointestinal microbiota composition in prostate cancer risk
© Amirian et al.; licensee BioMed Central Ltd. 2013
Received: 11 June 2013
Accepted: 12 October 2013
Published: 4 November 2013
Among men in the U.S., prostate cancer is the most common cancer and the second leading cause of cancer death. Despite its prevalence, there are few established risk factors for prostate cancer. Some studies have found that intake of certain foods/nutrients may be associated with prostate cancer risk, but few have accounted for how intake and metabolic factors may interact to influence bioavailable nutrient levels and subsequent disease risk.
Presentation of the hypothesis
The composition of the gastrointestinal (GI) microbiome may influence metabolism of dietary compounds and nutrients (e.g., plant phenols, calcium, choline) that may be relevant to prostate cancer risk. We, therefore, propose the hypothesis that GI microbiota may have a markedly different composition among individuals with higher prostate cancer risk. These individuals could have microbial profiles that are conducive to intestinal inflammation and/or are less favorable for the metabolism and uptake of chemopreventive agents.
Testing the hypothesis
Because very little preliminary data exist on this potential association, a case–control study may provide valuable information on this topic. Such a study could evaluate whether the GI microbial profile is markedly different between three groups of individuals: healthy men, those with latent prostate cancer, and those with invasive prostate cancer. Any findings could then be validated in a larger study, designed to collect a series of specimens over time.
Implications of the hypothesis
Given the plethora of information emerging from the Human Microbiome Project, this is an opportune time to explore associations between the microbiome and complex human diseases. Identification of profiles that alter the host’s risk for disease may clarify inconsistencies in the literature on dietary factors and cancer risk, and could provide valuable targets for novel cancer prevention strategies.
KeywordsHuman microbiome Metagenome Prostate cancer Metabolic process
Prostate cancer is the most common cancer among men in the U.S. . In 2012, approximately 241,740 new diagnoses and 28,170 prostate cancer-related deaths were expected in the U.S. alone (global incidence of 27.9 cases per 100,000) [1, 2]. Lifetime risk for prostate cancer is estimated to be 16%, and the median age at diagnosis is 67 years .
Despite its prevalence, there are few established risk factors for prostate cancer . According to twin studies, a proportion of cases (10-40%) may be explained by genetic factors [3–5]. However, dietary and lifestyle factors may also influence prostate cancer susceptibility [3, 6]. Intake of red meat [7–10], dairy products [11, 12], eggs [9, 13, 14], green tea [15, 16], calcium [17–20], lycopene [21–23], selenium [6, 24], and fish oil [4, 25] have all been examined in relation to prostate cancer risk with relatively inconsistent results. The inconsistency of these findings may partly be due to the use of food intake measures as surrogates for bioavailable micronutrient levels, resulting in some misclassification of nutrient/metabolite exposures [26, 27]. Differing levels of nutrient metabolism and absorption from foods between study participants could bias the results of intake studies. Bioavailable micronutrient levels are often determined by complex interactions between intake and metabolism, which are generally not accounted for in such studies.
While many studies have examined self-reported dietary intake of certain foods or biomarkers of specific nutrient levels, few studies have focused on the interactions between how intake and metabolic factors may be working together to influence bioavailable nutrient levels and, potentially, disease risk. The studies that have examined interactions have mostly investigated how genetic variation may affect metabolism or intake (i.e., [28–30]). For example, one recent study investigated the effect of a single-nucleotide polymorphism (rs4988235) in the lactase (LCT) gene on dairy intake, blood analytes, and prostate cancer risk . Although they did not find a significant association with prostate cancer susceptibility, they did report that this variant was correlated with milk intake, with the genotype that confers lower tolerance of lactose-containing foods being associated with lower dairy intake. Another study found that obesity (and its inflammatory sequelae) may modify the impact of arachidonic acid metabolism gene polymorphisms on prostate cancer risk . Nevertheless, genetic factors are only one component of what determines the ability to absorb, convert, and retain dietary nutrients [31–33].
Bioavailable micronutrient levels are not only dependent upon metabolism-related genetic profiles, but are also partly determined by the composition of one’s gastrointestinal (GI) microbiota and the metabolic profiles of these GI microorganisms [31, 32, 34–37]. In fact, the relationship between the GI microbiome and dietary factors is bidirectional-- diet influences the composition of the GI microbiome and the GI microbiome affects the digestion and metabolism of dietary factors . Interactions between intake and many species of microbes in the host GI tract have already been well documented [31, 32, 38, 39], and there are several excellent comprehensive review articles currently available on this topic [36, 37, 39]. Here, we will highlight a few examples of such interactions to show that the GI microbiome and its related metabolic properties can potentially be highly relevant to prostate cancer risk.
Examples of known associations between components of the gastrointestinal microbiome and xenobiotic compounds*
Partial sequestration, limiting availability to host
2-Amino-3-methylimidazo [4,5-f]quinoline (IQ)
Carcinogenic heterocyclic amine, food-borne
Bacteroides, Clostridium, Escherichia
Degraded into 7-hydroxy-IQ (direct mutagen) by β-glucuronidase
Metabolized into equol or non-estrogenic metabolites
Reduction of mercuric tissue content
A recent review article provided further rationale behind why the GI bacterial community should be viewed as a biodynamic system that interacts with its living environment  and may, thus, affect disease risk. This recent article focused on the hypothesis that GI microbes could influence prostate cancer risk based on the presence of isoflavone-metabolizing, equol-producing bacteria. Equol, which has anti-androgenic properties, is being tested as a potential chemopreventive agent. The idea of Slackia sp. NATTS strain bacteria metabolizing daidzein into equol, which may, subsequently, influence prostate cancer susceptibility, provides yet another example of how the GI microbiome could impact digestion and potentially have downstream systemic effects. With a ratio of about 10 microbes for each eukaryotic cell in the human body [59, 60], it is likely that the human microbiome has major physiological and metabolic impacts that we have yet to uncover.
Plotter and Blaser have suggested that human cancers should be considered in the milieu of host-microbiome interactions. They previously described three paradigms relating how the microbiome may be involved in cancer development and pathogenesis. The first involves constituents of the microbiome having inflammatory effects in a lumenal organ. The second paradigm revolves around the metabolic effects of the host’s GI microbiome indirectly contributing to distal malignancies via the human estrobolome. The estrobolome is defined as the set of enteric bacterial genes which code for proteins involved in estrogen metabolism. This paradigm may be particularly relevant to our proposed hypothesis due to the reported associations between estrogens and prostate cancer risk [53, 61]. The third paradigm is related to the alteration of clinical latency preceding malignancies.
Additionally, there are several specific mechanisms (some of which could fall under one or more of the aforementioned paradigms) through which the GI microbiome could have downstream effects on cancer risk, including competitive inhibition of pathogenic bacteria, production of antibacterial compounds (i.e., bacteriocins) and acids, gene transference between food-borne microbes and members of the GI microbiota, and modulation of the host’s immune system [37, 62]. The exact mechanisms by which the GI microbiome may distally affect cancer risk is likely to be different depending on the cancer site. Nevertheless, the first step to determining how influential the GI microbiome may be in prostate cancer development is to assess whether there are key distinctions in the microbial profiles of men who do and do not develop aggressive disease.
Presentation of the hypothesis
Our hypothesis is that gastrointestinal microbiota may have a markedly different composition among individuals with higher prostate cancer risk. It is expected that men who are more susceptible to the development of aggressive disease will have similarities in their microbial/metabolic profiles that diverge from the profiles of healthy men. With regard to the microbial profiles of interest, our hypothesis is not, per se, contingent upon the taxonomic composition of the GI microbiome of low- versus high-risk individuals, but rather the different metabolic and functional profiles represented by the GI microbial community. Given that different taxa of bacteria can have similar metabolic effects, both taxonomic and metabolic profiles should be identified and compared between high- and low-risk men. Furthermore, because of the varied effects that intestinal bacteria can have on the host, many of which are still being uncovered, functional studies and research into the specific mechanisms of action will be necessary to explain any microbial or metabolic differences found. Individuals with higher prostate cancer susceptibility may potentially have microbial profiles that are more conducive to intestinal inflammation and/or less favorable for the metabolism and uptake of chemopreventive agents, certain micronutrients, etc.
Testing the hypothesis
Strengths and limitations of epidemiologic study designs for examining the associations between the gastrointestinal microbiome and prostate cancer risk
● Cannot truly establish temporality or differentiate between cancer-induced and pre-cancerous changes in the GI microbiome
Diagnostically-confirmed latent and invasive prostate cancer cases compared to each other and to matched controls
● Relatively inexpensive
● No follow-up required
● Difficult to obtain appropriate control group
● Ability to assess changes from multiple samples as individuals transition from healthy to cancerous state
● Need very large sample size to be able to obtain enough incident prostate cancer cases
Large cohort of older men followed over time with regular assessments of GI microbiome and prostate cancer status
● Need long follow up time
● Can obtain data on incident cases
● Extremely expensive
● Potential biases due to loss to follow
● Can continue to obtain data throughout course of treatment and progression to assess post-diagnostic longitudinal and treatment-related changes
● Can evaluate mortality as an end point
● Likely to have clearer temporality between assessment of microbial/metabolic profile and prostate cancer development than in a case–control study
● Requires availability of previously collected and appropriately preserved samples (or data)
Previously collected samples on a large cohort of men, who were healthy at baseline, assessed for current prostate cancer status
● Participants must have been cancer-free at time of sample collection
● Less expensive than prospective cohort study
● Multiple longitudinal samples are unlikely to be available
Another concern regarding the temporality of potential associations between the microbiome and cancer development relates to the “driver-passenger model” that has been proposed for colorectal cancer . Tjalsma et al. posit that colorectal carcinogenesis may be spurred by “driver” bacteria, which can initially induce DNA damage, and are later replaced by “passenger” bacteria that could either delay or enhance tumorogeneis. They suggest that the changing microenvironment surrounding the growth of the tumor may alter selective pressures and, thus, result in the driver bacteria being outcompeted by passenger bacteria (which are defined as commensal organisms that may have tumor promoting or suppressing properties). This driver-passenger model of the involvement of the GI microbiome in colorectal cancer development is probably less applicable to cancers in tissues that have little direct exposure to the microbiome, such as the prostate. Nonetheless, it is possible that physiological changes that occur after the development of prostate cancer may, in turn, have an indirect impact on the composition of the GI microbiome. As a result, it is important that the issue of temporality be considered in any study of the possible associations between the GI microbiome and prostate cancer risk.
While knowledge of microbial changes that occur due to cancer development may be useful as potential diagnostic or screening tools, identification of changes in gastrointestinal microbiota that increase one’s risk for invasive cancer would provide a key opportunity for cancer prevention. A prospective study design, through expensive and time-consuming, may afford opportunities to study both these topics, if a large enough cohort of men could be recruited and followed. Exploring the composition of the GI microbiome in relation to prostate cancer risk over time may clarify the findings of previous studies that have inconsistently reported associations between intake of various foods/nutrients and prostate cancer susceptibility by better encompassing the complex set of interactions involved in digestion/metabolism. However, a longitudinal study may present several obstacles related to feasibility, given the incidence of prostate cancer among the general population, the cost of repeated evaluations of microbial profiles, and the need to successfully follow the participants over time while minimizing loss to follow up.
Sample collection could pose another challenge for a longitudinal study on this topic. Many protocols require that stool samples be kept on ice and returned to the lab within 24 hours of collection. A prospective study that requires participants to collect stool, pack it, and return it to the lab within one day may have high loss to follow up, which could be differential between those who go on to develop prostate cancer versus those who do not. This type of selection bias would impact the study findings. Thus, procedures should be streamlined, detailed instructions must be given to participants, and appropriate study incentives should be provided. Ideally, sample collection and processing should follow the protocols set forth and established by The Human Microbiome Project [65, 66].
Given the feasibility-related issues that may be associated with a longitudinal study on the GI microbiome and prostate cancer susceptibility, initial studies may realistically need to be retrospective to determine whether this topic is a fruitful area of research. Because little preliminary data exist on this potential association, a case–control study (recruiting incident cases) may provide valuable information, despite its limitations. The GI microbial profiles of healthy men can be compared to those with latent prostate cancer and those with invasive prostate cancer. Alternatively, in a prospective cohort including only diagnostically-confirmed cases, the GI microbiome can feasibly be examined in relation to prostate cancer survival over time among men with aggressive disease to assess its prognostic value.
Implications of the hypothesis
Investigating the role of the human microbiome in the etiology of complex multifactorial conditions, like cancer, is still a relatively new field. Much research to date has focused on profiling the composition of the microbiome across the human body, rather than exploring associations with disease. The recent literature contains several profiles of the bacterial diversity across the human digestive tract [67–70]. However, inter-individual differences in GI microbiome composition are a source of genetic/metabolic variation that warrant thorough analysis as potential predictors of disease susceptibility.
Even if the hypothesis proposed here is consistently supported by our and other similar studies, establishing causation for this association will likely be relatively difficult. Any differences in the GI microbiome detected between low- and high-risk (or healthy and diseased) men are likely to be neither sufficient nor necessary for the development of malignancy. Furthermore, the association of interest is essentially a network of interactions between the GI microbiome, dietary factors, and the host’s other environmental and genetic susceptibility factors. Studying such interactive relationships between interconnected exogenous and endogenous factors has always been challenging, but as the revolutionary new field of molecular pathologic epidemiology continues to evolve, the pathogenic processes behind complex diseases can slowly be uncovered [71, 72].
Molecular pathologic epidemiology can be described as the study of the interactive relationships between lifestyle/dietary factors and molecular tumoral characteristics on the development or progression of a specific molecular subtype of cancer . Because this field will attempt to examine more homogeneous and specific outcomes, while also accounting for the network of biological interactions inevitably at play in the disease process, etiologic relationships that have long eluded us because of the heterogeneity of the case definition (or because of the reductionist approach of simply examining main effects) may begin to be revealed. It follows that simple standards, such as Koch’s postulates or Hill’s criteria, which have previously been used for establishing causality, are unlikely to be entirely applicable to studies such as the one proposed here (which will be focused on the multiple networks of interactions constituted by the potential relationships between the GI microbiome and prostate cancer) . Therefore, as the field of molecular pathologic epidemiology grows, the criteria by which we assess causality must also evolve to incorporate a more systemic approach.
Nevertheless, given the available information from the Human Microbiome Project [74, 75], this is an opportune time to clarify associations between the microbiome and complex diseases . Identification of profiles that alter the host’s disease risk may clarify inconsistencies in the literature on dietary factors and cancer risk, and will likely provide novel targets for cancer prevention strategies and personalized medicine . Such strategies may involve personalized probiotic and vitamin/mineral supplementation, fecal transplant [76–78], or the use of antibiotics to achieve a more favorable microbial profile among men whose GI microbiota may support a predisposition to invasive prostate cancer.
- SEER stat fact sheets: prostate. [http://www.seer.cancer.gov/statfacts/html/prost.html]
- Brenner AV, Linet MS, Fine HA, Shapiro WR, Selker RG, Black PM, Inskip PD: History of allergies and autoimmune diseases and risk of brain tumors in adults. Int J Cancer. 2002, 99: 252-259. 10.1002/ijc.10320.PubMedView ArticleGoogle Scholar
- Wilson KM, Giovannucci EL, Mucci LA: Lifestyle and dietary factors in the prevention of lethal prostate cancer. Asian J Androl. 2012, 14: 365-374. 10.1038/aja.2011.142.PubMedPubMed CentralView ArticleGoogle Scholar
- Astorg P: Dietary N-6 and N-3 polyunsaturated fatty acids and prostate cancer risk: a review of epidemiological and experimental evidence. Cancer Causes Control. 2004, 15: 367-386.PubMedView ArticleGoogle Scholar
- Page WF, Braun MM, Partin AW, Caporaso N, Walsh P: Heredity and prostate cancer: a study of World War II veteran twins. Prostate. 1997, 33: 240-245. 10.1002/(SICI)1097-0045(19971201)33:4<240::AID-PROS3>3.0.CO;2-L.PubMedView ArticleGoogle Scholar
- Sonn GA, Aronson W, Litwin MS: Impact of diet on prostate cancer: a review. Prostate Cancer Prostatic Dis. 2005, 8: 304-310. 10.1038/sj.pcan.4500825.PubMedView ArticleGoogle Scholar
- Punnen S, Hardin J, Cheng I, Klein EA, Witte JS: Impact of meat consumption, preparation, and mutagens on aggressive prostate cancer. PLoS One. 2011, 6: e27711-10.1371/journal.pone.0027711.PubMedPubMed CentralView ArticleGoogle Scholar
- Major JM, Cross AJ, Watters JL, Hollenbeck AR, Graubard BI, Sinha R: Patterns of meat intake and risk of prostate cancer among African-Americans in a large prospective study. Cancer Causes Control. 2011, 22: 1691-1698. 10.1007/s10552-011-9845-1.PubMedPubMed CentralView ArticleGoogle Scholar
- Richman EL, Kenfield SA, Stampfer MJ, Giovannucci EL, Chan JM: Egg, red meat, and poultry intake and risk of lethal prostate cancer in the prostate-specific antigen-era: incidence and survival. Cancer Prev Res (Phila). 2011, 4: 2110-2121. 10.1158/1940-6207.CAPR-11-0354.View ArticleGoogle Scholar
- Alexander DD, Mink PJ, Cushing CA, Sceurman B: A review and meta-analysis of prospective studies of red and processed meat intake and prostate cancer. Nutr J. 2010, 9: 50-10.1186/1475-2891-9-50.PubMedPubMed CentralView ArticleGoogle Scholar
- Newmark HL, Heaney RP: Dairy products and prostate cancer risk. Nutr Cancer. 2010, 62: 297-299. 10.1080/01635580903407221.PubMedView ArticleGoogle Scholar
- Lampe JW: Dairy products and cancer. J Am Coll Nutr. 2011, 30: 464S-470S. 10.1080/07315724.2011.10719991.PubMedView ArticleGoogle Scholar
- Richman EL, Kenfield SA, Stampfer MJ, Giovannucci EL, Zeisel SH, Willett WC, Chan JM: Choline intake and risk of lethal prostate cancer: incidence and survival. Am J Clin Nutr. 2012, 96: 855-863. 10.3945/ajcn.112.039784.PubMedPubMed CentralView ArticleGoogle Scholar
- Xie B, He H: No association between egg intake and prostate cancer risk: a meta-analysis. Asian Pac J Cancer Prev. 2012, 13: 4677-4681. 10.7314/APJCP.2012.13.9.4677.PubMedView ArticleGoogle Scholar
- Montague JA, Butler LM, Wu AH, Genkinger JM, Koh WP, Wong AS, Wang R, Yuan JM, Yu MC: Green and black tea intake in relation to prostate cancer risk among Singapore Chinese. Cancer Causes Control. 2012, 23: 1635-1641. 10.1007/s10552-012-0041-8.PubMedPubMed CentralView ArticleGoogle Scholar
- Ozten-Kandas N, Bosland MC: Chemoprevention of prostate cancer: natural compounds, antiandrogens, and antioxidants - in vivo evidence. J Carcinog. 2011, 10: 27-10.4103/1477-3163.90438.PubMedPubMed CentralView ArticleGoogle Scholar
- Chan JM, Giovannucci EL: Dairy products, calcium, and vitamin D and risk of prostate cancer. Epidemiol Rev. 2001, 23: 87-92. 10.1093/oxfordjournals.epirev.a000800.PubMedView ArticleGoogle Scholar
- Gao X, LaValley MP, Tucker KL: Prospective studies of dairy product and calcium intakes and prostate cancer risk: a meta-analysis. J Natl Cancer Inst. 2005, 97: 1768-1777. 10.1093/jnci/dji402.PubMedView ArticleGoogle Scholar
- Schwartz GG, Skinner HG: A prospective study of total and ionized serum calcium and time to fatal prostate cancer. Cancer Epidemiol Biomarkers Prev. 2012, 21: 1768-1773. 10.1158/1055-9965.EPI-12-0585.PubMedView ArticleGoogle Scholar
- Chan JM, Stampfer MJ, Ma J, Gann PH, Gaziano JM, Giovannucci EL: Dairy products, calcium, and prostate cancer risk in the Physicians’ health study. Am J Clin Nutr. 2001, 74: 549-554.PubMedGoogle Scholar
- Key TJ, Appleby PN, Allen NE, Travis RC, Roddam AW, Jenab M, Egevad L, Tjonneland A, Johnsen NF, Overvad K, et al.: Plasma carotenoids, retinol, and tocopherols and the risk of prostate cancer in the European prospective investigation into cancer and nutrition study. Am J Clin Nutr. 2007, 86: 672-681.PubMedGoogle Scholar
- Wu K, Erdman JW, Schwartz SJ, Platz EA, Leitzmann M, Clinton SK, DeGroff V, Willett WC, Giovannucci E: Plasma and dietary carotenoids, and the risk of prostate cancer: a nested case–control study. Cancer Epidemiol Biomarkers Prev. 2004, 13: 260-269. 10.1158/1055-9965.EPI-03-0012.PubMedView ArticleGoogle Scholar
- Teodoro AJ, Oliveira FL, Martins NB, Maia GA, Martucci RB, Borojevic R: Effect of lycopene on cell viability and cell cycle progression in human cancer cell lines. Cancer Cell Int. 2012, 12: 36-10.1186/1475-2867-12-36.PubMedPubMed CentralView ArticleGoogle Scholar
- Hurst R, Hooper L, Norat T, Lau R, Aune D, Greenwood DC, Vieira R, Collings R, Harvey LJ, Sterne JA, et al.: Selenium and prostate cancer: systematic review and meta-analysis. Am J Clin Nutr. 2012, 96: 111-122. 10.3945/ajcn.111.033373.PubMedView ArticleGoogle Scholar
- Sala-Vila A, Calder PC: Update on the relationship of fish intake with prostate, breast, and colorectal cancers. Crit Rev Food Sci Nutr. 2011, 51: 855-871. 10.1080/10408398.2010.483527.PubMedView ArticleGoogle Scholar
- Dennis LK, Snetselaar LG, Smith BJ, Stewart RE, Robbins ME: Problems with the assessment of dietary fat in prostate cancer studies. Am J Epidemiol. 2004, 160: 436-444. 10.1093/aje/kwh243.PubMedView ArticleGoogle Scholar
- Wei MY, Giovannucci EL: Lycopene, tomato products, and prostate cancer incidence: a review and reassessment in the PSA screening Era. J Oncol. 2012, 2012: 271063-PubMedPubMed CentralView ArticleGoogle Scholar
- Joshi AD, Corral R, Catsburg C, Lewinger JP, Koo J, John EM, Ingles SA, Stern MC: Red meat and poultry, cooking practices, genetic susceptibility and risk of prostate cancer: results from a multiethnic case–control study. Carcinogenesis. 2012, 33: 2108-2118. 10.1093/carcin/bgs242.PubMedPubMed CentralView ArticleGoogle Scholar
- Travis RC, Appleby PN, Siddiq A, Allen NE, Kaaks R, Canzian F, Feller S, Tjonneland A, Fons Johnsen N, Overvad K, et al.: Genetic variation in the lactase gene, dairy product intake and risk for prostate cancer in the European prospective investigation into cancer and nutrition. Int J Cancer. 2012, 132: 1901-1910.PubMedPubMed CentralView ArticleGoogle Scholar
- Amirian ES, Ittmann MM, Scheurer ME: Associations between arachidonic acid metabolism gene polymorphisms and prostate cancer risk. Prostate. 2011, 71: 1382-1389. 10.1002/pros.21354.PubMedView ArticleGoogle Scholar
- Tilg H: Obesity, metabolic syndrome, and microbiota: multiple interactions. J Clin Gastroenterol. 2010, 44 (Suppl 1): S16-S18.PubMedView ArticleGoogle Scholar
- Tremaroli V, Backhed F: Functional interactions between the gut microbiota and host metabolism. Nature. 2012, 489: 242-249. 10.1038/nature11552.PubMedView ArticleGoogle Scholar
- Macdonald RS, Wagner K: Influence of dietary phytochemicals and microbiota on colon cancer risk. J Agric Food Chem. 2012, [Epub ahead of print] PMID: 22632581Google Scholar
- Musso G, Gambino R, Cassader M: Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annu Rev Med. 2011, 62: 361-380. 10.1146/annurev-med-012510-175505.PubMedView ArticleGoogle Scholar
- Musso G, Gambino R, Cassader M: Obesity, diabetes, and gut microbiota: the hygiene hypothesis expanded?. Diabetes Care. 2010, 33: 2277-2284. 10.2337/dc10-0556.PubMedPubMed CentralView ArticleGoogle Scholar
- Haiser HJ, Turnbaugh PJ: Developing a metagenomic view of xenobiotic metabolism. Pharmacol Res: J Italian Pharmacol Soc. 2013, 69: 21-31.View ArticleGoogle Scholar
- Dutton RJ, Turnbaugh PJ: Taking a metagenomic view of human nutrition. Curr Opin Clin Nutr. 2012, 15: 448-454. 10.1097/MCO.0b013e3283561133.View ArticleGoogle Scholar
- Akaza H: Prostate cancer chemoprevention by soy isoflavones: role of intestinal bacteria as the "second human genome". Cancer Sci. 2012, 103: 969-975. 10.1111/j.1349-7006.2012.02257.x.PubMedView ArticleGoogle Scholar
- Saad R, Rizkallah MR, Aziz RK: Gut pharmacomicrobiomics: the tip of an iceberg of complex interactions between drugs and gut-associated microbes. Gut Pathogens. 2012, 4: 16-10.1186/1757-4749-4-16.PubMedPubMed CentralView ArticleGoogle Scholar
- Masood MI, Qadir MI, Shirazi JH, Khan IU: Beneficial effects of lactic acid bacteria on human beings. Crit Rev Microbiol. 2011, 37: 91-98. 10.3109/1040841X.2010.536522.PubMedView ArticleGoogle Scholar
- Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF, Gordon JI: The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A. 2004, 101: 15718-15723. 10.1073/pnas.0407076101.PubMedPubMed CentralView ArticleGoogle Scholar
- Russell SL, Finlay BB: The impact of gut microbes in allergic diseases. Curr Opin Gastroenterol. 2012, 28: 563-569. 10.1097/MOG.0b013e3283573017.PubMedView ArticleGoogle Scholar
- Arthur JC, Perez-Chanona E, Muhlbauer M, Tomkovich S, Uronis JM, Fan TJ, Campbell BJ, Abujamel T, Dogan B, Rogers AB, et al.: Intestinal inflammation targets cancer-inducing activity of the microbiota. Science. 2012, 338: 120-123. 10.1126/science.1224820.PubMedPubMed CentralView ArticleGoogle Scholar
- Graessler J, Qin Y, Zhong H, Zhang J, Licinio J, Wong ML, Xu A, Chavakis T, Bornstein AB, Ehrhart-Bornstein M, et al.: Metagenomic sequencing of the human gut microbiome before and after bariatric surgery in obese patients with type 2 diabetes: correlation with inflammatory and metabolic parameters. Pharmacogenomics J. 2012, doi: 10.1038/tpj.2012.43. [Epub ahead of print] PMID: 23032991Google Scholar
- Iebba V, Nicoletti M, Schippa S: Gut microbiota and the immune system: an intimate partnership in health and disease. Int J Immunopathol Pharmacol. 2012, 25: 823-833.PubMedGoogle Scholar
- Sanders ME, Klaenhammer TR: Invited review: the scientific basis of lactobacillus acidophilus NCFM functionality as a probiotic. J Dairy Sci. 2001, 84: 319-331. 10.3168/jds.S0022-0302(01)74481-5.PubMedView ArticleGoogle Scholar
- Montes RG, Bayless TM, Saavedra JM, Perman JA: Effect of milks inoculated with lactobacillus acidophilus or a yogurt starter culture in lactose-maldigesting children. J Dairy Sci. 1995, 78: 1657-1664. 10.3168/jds.S0022-0302(95)76790-X.PubMedView ArticleGoogle Scholar
- Sekirov I, Russell SL, Antunes LC, Finlay BB: Gut microbiota in health and disease. Physiol Rev. 2010, 90: 859-904. 10.1152/physrev.00045.2009.PubMedView ArticleGoogle Scholar
- Russell W, Duthie G: Plant secondary metabolites and gut health: the case for Phenolic acids. Proc Nutr Soc. 2011, 70: 389-396. 10.1017/S0029665111000152.PubMedView ArticleGoogle Scholar
- Namasivayam N: Chemoprevention in experimental animals. Ann N Y Acad Sci. 2011, 1215: 60-71. 10.1111/j.1749-6632.2010.05873.x.PubMedView ArticleGoogle Scholar
- Plottel CS, Blaser MJ: Microbiome and malignancy. Cell host & microbe. 2011, 10: 324-335. 10.1016/j.chom.2011.10.003.View ArticleGoogle Scholar
- Flores R, Shi J, Fuhrman B, Xu X, Veenstra TD, Gail MH, Gajer P, Ravel J, Goedert JJ: Fecal microbial determinants of fecal and systemic estrogens and estrogen metabolites: a cross-sectional study. J Transl Med. 2012, 10: 253-10.1186/1479-5876-10-253.PubMedPubMed CentralView ArticleGoogle Scholar
- Cavalieri E, Rogan E: The molecular etiology and prevention of estrogen-initiated cancers. Mol Aspects Med. 2013, doi: 10.1016/j.mam.2013.08.002. [Epub ahead of print] PMID: 23994691Google Scholar
- Kasaikina MV, Kravtsova MA, Lee BC, Seravalli J, Peterson DA, Walter J, Legge R, Benson AK, Hatfield DL, Gladyshev VN: Dietary selenium affects host selenoproteome expression by influencing the gut microbiota. FASEB J: Publ Fed Am Soc Exp Biol. 2011, 25: 2492-2499.View ArticleGoogle Scholar
- Humblot C, Combourieu B, Vaisanen ML, Furet JP, Delort AM, Rabot S: 1H Nuclear magnetic resonance spectroscopy-based studies of the metabolism of food-borne carcinogen 2-amino-3-methylimidazo[4,5-f]quinoline by human intestinal microbiota. Appl Environ Microb. 2005, 71: 5116-5123. 10.1128/AEM.71.9.5116-5123.2005.View ArticleGoogle Scholar
- Humblot C, Murkovic M, Rigottier-Gois L, Bensaada M, Bouclet A, Andrieux C, Anba J, Rabot S: Beta-glucuronidase in human intestinal microbiota is necessary for the colonic genotoxicity of the food-borne carcinogen 2-amino-3-methylimidazo[4,5-f]quinoline in rats. Carcinogenesis. 2007, 28: 2419-2425. 10.1093/carcin/bgm170.PubMedView ArticleGoogle Scholar
- Rafii F, Jackson LD, Ross I, Heinze TM, Lewis SM, Aidoo A, Lyn-Cook L, Manjanatha M: Metabolism of daidzein by fecal bacteria in rats. Comparat Med. 2007, 57: 282-286.Google Scholar
- Rowland IR, Davies MJ, Evans JG: Tissue content of mercury in rats given methylmercuric chloride orally: influence of intestinal flora. Arch Environ health. 1980, 35: 155-160. 10.1080/00039896.1980.10667485.PubMedView ArticleGoogle Scholar
- Li K, Bihan M, Yooseph S, Methe BA: Analyses of the microbial diversity across the human microbiome. PLoS One. 2012, 7: e32118-10.1371/journal.pone.0032118.PubMedPubMed CentralView ArticleGoogle Scholar
- Goodman AL, Gordon JI: Our unindicted coconspirators: human metabolism from a microbial perspective. Cell Metab. 2010, 12: 111-116. 10.1016/j.cmet.2010.07.001.PubMedPubMed CentralView ArticleGoogle Scholar
- Carruba G: Estrogen and prostate cancer: an eclipsed truth in an androgen-dominated scenario. J Cell Biochem. 2007, 102: 899-911. 10.1002/jcb.21529.PubMedView ArticleGoogle Scholar
- Commane D, Hughes R, Shortt C, Rowland I: The potential mechanisms involved in the anti-carcinogenic action of probiotics. Mutat Res. 2005, 591: 276-289. 10.1016/j.mrfmmm.2005.02.027.PubMedView ArticleGoogle Scholar
- Khan AA, Shrivastava A, Khurshid M: Normal to cancer microbiome transformation and its implication in cancer diagnosis. Biochim Biophys Acta. 1826, 2012: 331-337.Google Scholar
- Tjalsma H, Boleij A, Marchesi JR, Dutilh BE: A bacterial driver-passenger model for colorectal cancer: beyond the usual suspects. Nat Rev Microbiol. 2012, 10: 575-582. 10.1038/nrmicro2819.PubMedView ArticleGoogle Scholar
- NIH human microbiome project sampling, sequencing, and analysis protocols. [http://www.hmpdacc.org/tools_protocols/tools_protocols.php]
- Dave M, Higgins PD, Middha S, Rioux KP: The human gut microbiome: current knowledge, challenges, and future directions. Transl Res: J Lab Clin Med. 2012, 160: 246-257. 10.1016/j.trsl.2012.05.003.View ArticleGoogle Scholar
- Segata N, Haake SK, Mannon P, Lemon KP, Waldron L, Gevers D, Huttenhower C, Izard J: Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 2012, 13: R42-10.1186/gb-2012-13-6-r42.PubMedPubMed CentralView ArticleGoogle Scholar
- Stearns JC, Lynch MD, Senadheera DB, Tenenbaum HC, Goldberg MB, Cvitkovitch DG, Croitoru K, Moreno-Hagelsieb G, Neufeld JD: Bacterial biogeography of the human digestive tract. Sci Rep. 2011, 1: 170-PubMedPubMed CentralView ArticleGoogle Scholar
- Huse SM, Ye Y, Zhou Y, Fodor AA: A core human microbiome as viewed through 16S rRNA sequence clusters. PLoS One. 2012, 7: e34242-10.1371/journal.pone.0034242.PubMedPubMed CentralView ArticleGoogle Scholar
- Antonopoulos DA, Huse SM, Morrison HG, Schmidt TM, Sogin ML, Young VB: Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation. Infect Immun. 2009, 77: 2367-2375. 10.1128/IAI.01520-08.PubMedPubMed CentralView ArticleGoogle Scholar
- Ogino S, Stampfer M: Lifestyle factors and microsatellite instability in colorectal cancer: the evolving field of molecular pathological epidemiology. J Natl Cancer Inst. 2010, 102: 365-367. 10.1093/jnci/djq031.PubMedPubMed CentralView ArticleGoogle Scholar
- Ogino S, Chan AT, Fuchs CS, Giovannucci E: Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field. Gut. 2011, 60: 397-411. 10.1136/gut.2010.217182.PubMedPubMed CentralView ArticleGoogle Scholar
- Plowright RK, Sokolow SH, Gorman ME, Daszak P, Foley JE: Causal inference in disease ecology: investigating ecological drivers of disease emergence. Front Ecol Environ. 2008, 6: 420-429. 10.1890/070086.View ArticleGoogle Scholar
- Morgan XC, Segata N, Huttenhower C: Biodiversity and functional genomics in the human microbiome. Trends Genet. 2012, 29: 51-58.PubMedPubMed CentralView ArticleGoogle Scholar
- Conlan S, Kong HH, Segre JA: Species-level analysis of DNA sequence data from the NIH human microbiome project. PLoS One. 2012, 7: e47075-10.1371/journal.pone.0047075.PubMedPubMed CentralView ArticleGoogle Scholar
- Vindigni SM, Broussard EK, Surawicz CM: Alteration of the intestinal microbiome: fecal microbiota transplant and probiotics for clostridium difficile and beyond. Expert Rev Gastroenterol Hepatol. 2013, 7: 615-628. 10.1586/17474124.2013.832501.PubMedView ArticleGoogle Scholar
- Aroniadis OC, Brandt LJ: Fecal microbiota transplantation: past, present and future. Curr Opin Gastroenterol. 2013, 29: 79-84. 10.1097/MOG.0b013e32835a4b3e.PubMedView ArticleGoogle Scholar
- Paasche S: Fecal microbiota transplantation: an innovative approach to treating clostridium difficile disease. JAAPA: J Am Acad Physic Assistants. 2013, 26: 46-49.Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.