- Open Access
Microbial diversity in individuals and their household contacts following typical antibiotic courses
© The Author(s). 2016
Received: 7 April 2016
Accepted: 22 July 2016
Published: 30 July 2016
Antibiotics are a mainstay of treatment for bacterial infections worldwide, yet the effects of typical antibiotic prescriptions on human indigenous microbiota have not been thoroughly evaluated. We examined the effects of the two most commonly prescribed antibiotics (amoxicillin and azithromycin) in the USA to discern whether short-term antibiotic courses may have prolonged effects on human microbiota.
We sampled the feces, saliva, and skin specimens from a cohort of unrelated, cohabitating individuals over 6 months. An individual in each household was given an antibiotic, and the other a placebo to discern antibiotic impacts on microbiota, as well as determine whether antibiotic use might reshape the microbiota of each household. We observed household-specific patterns of microbiota on each body surface, which persevered despite antibiotic perturbations. While the gut microbiota within an individual became more dissimilar over time, there was no evidence that the use of antibiotics accelerated this process when compared to household members. There was a significant change in microbiota diversity in the gut and mouth in response to antibiotics, but analogous patterns were not observed on the skin. Those who received 7 days of amoxicillin generally had greater reductions in diversity compared to those who received 3 days, in contrast to those who received azithromycin.
As few as 3 days of treatment with the most commonly prescribed antibiotics can result in sustained reductions in microbiota diversity, which could have implications for the maintenance of human health and resilience to disease.
The human body has many different surfaces and each is home to its own unique microbes , including cellular microbes (e.g., bacteria, archaea, and fungi) as well as large populations of viruses [2–6]. These microbes are collectively referred to as “the human microbiome,” and a litany of studies exist examining those microbes and their contributions to the maintenance of health and the development of disease [7–10]. Many microbiome studies focus on gut bacterial biota, which have been shown to be altered in conditions such as obesity [11–13], diabetes , and inflammatory bowel diseases [7, 15], and potentially may play roles in disorders of neurological development . Host genetic factors, geography, and environmental variables (e.g., sex and hormonal fluctuations) have also been linked to microbes and their relative abundances in the human microbiome [17–22]. How human microbial communities respond to perturbations such as antibiotics, and whether such perturbations affect host susceptibility to disease, have become cornerstones of human microbiome research.
Microorganisms such as bacteria, fungi, viruses, and parasites cause many of the world’s diseases, yet only bacterial infections are usually susceptible to treatment with commonly prescribed antibiotics. The US Centers for Disease Control estimates that up to 50 % of prescribed antibiotics in the USA are unnecessary, and 30 % of those are prescribed for outpatients . Their use has been associated with the emergence of antibiotic resistance, which resulted in an estimated 23,000 deaths in 2013. The Institute for Healthcare Informatics Review indicates that the two most overused antibiotics in the USA include azithromycin (56.3 million prescriptions annually in 2011) and amoxicillin (53.8 million prescriptions annually in 2011), compared to a population size of only 311.7 million in 2011. Despite the widespread use of azithromycin and amoxicillin, many studies characterizing the effects of antibiotics on the microbiome focus on other antibiotics such as clindamycin and ciprofloxacin [24, 25]. This focus in some cases is related to the commonly known effects of these drugs on human gut microbes, with ciprofloxacin having broad activity against Proteobacteria that inhabit the gut, and clindamycin known to alter gut microbiota in a manner that is closely associated with colonization and subsequent disease caused by Clostridium difficile [26, 27]. Very little is known about the effects of the azithromycin and amoxicillin (typically given in short 3- to 7-day courses) on the microbiota of human body surfaces, and whether their effects may be sustained over long time periods.
It has been established that cohabitating individuals share bacterial biota [28, 29]; however, in many of these studies, the cohabitating individuals had some genetic relationships. For example, monozygotic and dizygotic twins share a substantial proportion of their bacterial biota [21, 30], but do not necessarily need to cohabitate to have similarities in their microbiota. This phenomenon suggests that both proximity and host genetic factors contribute to the composition of the human microbiome. The sharing of microbiota between close contacts gains greater importance when considering that our microbiomes may carry antibiotic resistance [31, 32]. The potential for sharing antibiotic-resistant organisms in our microbiomes that may not be causing disease but could cause disease under certain circumstances is a growing public health concern. How or whether the sharing of our microbiota may be affected by the use of antibiotics has not previously been examined, as there may also be collateral effects of antibiotic use in an individual for their close contacts.
In this study, we recruited a cohort of 56 genetically unrelated individuals, with 48 of them living in pairs in 24 separate households. In each household, 1 individual took 3 to 7 days of an antibiotic and the other took 3 to 7 days of a placebo. Our goals were to (1) discern the effects of the 2 most commonly prescribed antibiotics on the microbiota of the skin, gut, and mouth, (2) characterize the degree of similarity in the microbiota of unrelated household contacts and decipher whether it is significantly affected by antibiotic use, (3) characterize the long-term effects of typical antibiotic prescriptions on microbiota diversity, and (4) discern whether there may be collateral effects to antibiotic use for the diversity of microbiota in household contacts.
Demographics of study participants
Amoxicillin arm (N = 24)
Azithromycin arm (N = 24)
Control arm (N = 8)
24 ± 3.4
29 ± 13
25 ± 11
Female sex (%)
No. subjects with antibiotic consumption within last year
No. subjects with antibiotic consumption within last 3 years (%)
No. households who are couples (%)
Approximate time living together prior to study (months)
16 (range 3 to 54)
16 (range 1 to 42)
Household-specific patterns in the mouth, gut, and skin
We measured weighted UniFrac distances among the households based on whether the individuals in the households were romantic couples or roommates to discern whether personal relationships significantly shape the shared microbiota within the household. We found that in all households, there was a statistically significant pattern of shared microbiota (p < 0.05 in each case; data not shown); however, there was no significant relationship identified in the feces, saliva, or skin based on whether the individuals were couples or roommates. There was a trend toward more shared taxa in couples, but the trend was not statistically significant on any body surface (Additional file 2: Figure S3 and S4).
Effects of time on microbiota compositions
Taxonomic responses to antibiotics
In the saliva, the most abundant taxa identified were Prevotellaceae, Streptococcaceae, Veillonellaceae, and Neisseriaceae (Additional file 2: Figure S7). In response to amoxicillin, Veillonellaceae, Actinomycetaceae, Neisseriaceae, Prevotellaceae, and Porphyromonadaceae were all significantly increased in comparison to their housemates, while Streptococcaceae and Gemellaceae were diminished (Fig. 5). In response to azithromycin, Bifidobacteriales and Veillonellaceae were increased, while Clostridiales, Neisseriaceae, and Erysipelotrichaceae were diminished in comparison to their housemates.
On the skin, the most abundant taxa identified were Corynebacteriaceae, Propionibacteriaceae, Staphylococcaceae, and Streptococcaceae (Additional file 2: Figure S7). In response to both amoxicillin and azithromycin, the relative abundances of Streptococcaceae, Staphylococcaceae, Actinomycetales, Corynebacteriaceae, and Propionibacteriaceae were all altered in comparison to their housemates (Fig. 5).
Reduction in microbial diversity
When compared to housemates, subjects who received amoxicillin saw a reduction in their cutaneous microbiota diversity after 3 days, but not in response to azithromycin (Additional file 2: Figure S9). Independent of their housemates, this reduction in microbiota diversity in response to amoxicillin was sustained over the 6 months of the study (Fig. 7c). We also observed a reduction in diversity of the subjects who received the placebo, which was greater than that observed in response to antibiotics. The reduction in microbiota diversity observed in response to amoxicillin occurred within the first 3 days of therapy (Additional file 2: Figure S8).
We sought to elucidate the effects of the most commonly prescribed antibiotics in the USA on the microbiota of human body surfaces. Because antibiotics often are absorbed across the GI tract and distributed to the tissues via the bloodstream, we expected that each would affect the microflora of each body surface tested. Both amoxicillin and azithromycin often are prescribed unnecessarily, and their effects on the microbiota of each body surface may have implications for resilience to pathogen colonization and susceptibility to disease. We found that each antibiotic had significant impacts upon the microbiota of the mouth and gut that were apparent after only 3 days of therapy (Additional file 2: Figure S8). Other studies have demonstrated that lesser prescribed antibiotics such as ciprofloxacin, clindamycin, and minocycline also can have profound and lasting effects on the microbiota of the gut [24, 25].
Bacterial biota can be shared between individuals in a household, and those household-specific patterns observed on body surfaces also extend to household surfaces such as floors and doorknobs . We did not recruit any subjects with known genetic relationships in an attempt to avoid confounders that may result in similar microbiota between individuals. On all body surfaces studied, we could identify household-specific patterns using weighted UniFrac distances, which take into account both the presence of taxa and their relative abundances. When this same analysis was performed using unweighted distances that do not account for relative abundances, the data were no longer significant (data not shown), which suggests that relative abundances of taxa on various body surfaces are influenced by the household environment. Unfortunately, the individuals in this study had been cohabitating for various time periods prior to the initiation of this study, so the rapidity with which these household-specific patterns developed could not be assessed. We also identified a trend of greater similarity among the microbiota of romantic couples compared to roommates, similar to that which has previously been described in the oral cavities of couples . While we did not record information on physical contact between housemates, we believe the greater similarity in microbiota among romantic couples reflected greater physical contact. Despite the substantial conservation of the gut microbiota within subjects and households over time, genetic distances increased over time within each subject , indicating that there was some turnover of gut taxa and/or sustained alterations in their relative abundances over time. Interestingly, this trend did not appear to be accelerated by the use of antibiotics (Fig. 3), which suggests that this trend would have taken place even without the interventions that took place in this study.
By studying the two most commonly prescribed antibiotics in the USA, we wanted to identify whether there were sustained effects of common antibiotic treatments on the human microbiome. Our use of multiple controls allowed for us to discern whether microbiota diversity was significantly impacted compared to close contacts and to strangers. Because the external controls were not enrolled in the study with housemates and did not receive either antibiotic or placebo, we believe that they provided the most reliable reference group with regard to microbiota variation over time. We found separate trends in our study depending on which controls we used, which highlights that there may be collateral effects to antibiotic use for our close contacts. For example, there were significant diversity reductions in gut microbiota in response to amoxicillin and azithromycin compared to close contacts, but those reductions were not sustained after 8 weeks (Fig. 6). This trend largely reflects the concomitant reduction in gut microbiota diversity that is observed in the housemates of those taking antibiotics that was not identified in the other control subjects (Fig. 7). We originally hypothesized that we would observe an increase in gut microbiota diversity due the sharing of microbiota with housemates and did not predict that we would observe diversity reductions in the housemates. These data suggest that the influence of close contact on an individual’s microbiome may extend beyond simply sharing microbes and may indicate that there is a balance in microbe sharing that contributes to diversity.
We observed different responses of host microbiota based on both the antibiotic used and the length of therapy. While these antibiotics are known to have different spectrums of activity against microbes, they also differ significantly in their half-life, which might result in different effects on our microbiota. Amoxicillin has a half-life of 1 h, while azithromycin has a half-life of 68 h. Thus, we might expect the effects of shorter courses of azithromycin to have similar effects as longer courses because the drug will remain in the host for an extended period regardless of whether the subject receives a 3- or a 7-day course. This is what we observed in both the feces and saliva of subjects taking azithromycin (Figs. 6b and 8b ). We observed the opposite trend in subjects taking amoxicillin. Because the drug is eliminated from subjects much more rapidly, we believe that we observed more profound effects on diversity in those taking a 7-day course than in those taking a 3-day course (Figs. 6a and 8a).
Prior to this study, we believed that the use of these common antibiotics would accelerate microbiota changes significantly over 6 months. We found that the subjects who took antibiotics were no less similar to their original state than were subjects who took placebo or no therapy at all (Fig. 3 and Additional file 2: Figure S5 and S6). We did, however, identify changes in microbiota diversity that did not recover to baseline regardless of the antibiotic taken (Fig. 7). We did not follow the subjects beyond 6 months, so we cannot rule out the possibility that the differences we observed in diversity after just 3 to 7 days of antibiotics could render these subjects more susceptible to pathogen colonization.
Similar to a prior study, there was a near complete recovery of microbial diversity in the saliva  regardless of the antibiotic that was used (Fig. 7b). In that study, minocycline, amoxicillin, clindamycin, and ciprofloxacin were used and each showed a similar trend. We utilized a placebo in this study to simulate the model of a clinical trial and initially intended for the study to be blinded. We unblinded the study because many of the study participants were taking oral contraceptives, which would interact with azithromycin. The placebo (vitamin C) could have affected the microbiota of the skin, as the subjects who received vitamin C had substantial diversity decreases in their skin microbiota (Fig. 7c). Because vitamin C is rapidly excreted when high plasma levels are reached, we did not expect to observe significant microbiota changes after only 3 to 7 days of placebo.
Our data help to illustrate important concepts regarding the responses of our microbiota to common antibiotic perturbations. The shared microbiota in the gut, mouth, and on the skin of close contacts who were not known to be genetically related bolsters the concept that genetic relationships are not required to share our microbiota. We believe that the close contact and shared environmental reservoirs resulted in the sharing of microbiota within the households; however, there are other confounders which could also affect the relative proportions of shared microbiota within a household. For example, 14 of the 24 households studied had pets including dogs, cats, fish, and hamsters (Additional file 1: Table S1), which may also have served as vehicles for the sharing of microbiota. We recorded specific details regarding diet preferences for each subject studied, but did not note any differences between the subject groups that might account for the microbiota differences observed (data not shown). Only two of the subjects studied were vegetarians (Additional file 1: Table S1), but their microbiota was more similar to their housemates than to each other. The impact of short typical antibiotic courses persisted for at least 6 months in the gut, a factor that should be taken into account whenever antibiotics are prescribed. While our cohort was not followed nearly long enough to ascertain whether there may be risks to the sustained diversity reductions, the concomitant reductions in diversity observed in housemates suggests that antibiotic prescriptions may pose collateral risks to our close contacts.
Forty-eight subjects were enrolled in the study in pairs, with 2 individual living in each household. An additional 8 individuals were enrolled without a housemate and received no therapy over the course of the 6-month study. Households were randomized into either the amoxicillin or azithromycin arms of the study. Those subjects also were randomized to receive either antibiotic or placebo; however, because of the large numbers of penicillin allergies reported (Additional file 1: Table S1) and subjects using oral contraceptives (interact with azithromycin), some subjects who were randomized to receive antibiotics were given the placebo, while their housemate received the antibiotic instead. Of the household pairs, 6 pairs were placed into the 3-day amoxicillin arm, 6 pairs were placed into the 7-day amoxicillin arm, 6 pairs were placed into the 3-day azithromycin arm, and 6 pairs were placed into the 7-day azithromycin arm (Fig. 1). In each household, 1 subject received either 3 or 7 days of an antibiotic and the other subject received either 3 or 7 days of the placebo (vitamin C). The dose of amoxicillin was 500 mg twice daily, and the dose of vitamin C was 500 mg twice daily. The dose of azithromycin was 500 mg on the first day and 250 mg daily thereafter (this dosing was used to be consistent with the commonly prescribed Z-Pak). In the azithromycin arm, the placebo was given at 500 mg once daily. Each subject enrolled donated saliva, feces, and a skin swab on day 0 (day prior to antibiotics), day 3 (3 days after initiation of antibiotics), day 7, week 8, and month 6. Of the 24 households enrolled, 5 of those households (Additional file 1: Table S1) were lost to follow-up and did not provide specimens at the month 6 time point. Each subject provided at least 3 ml of unstimulated saliva and a skin swab from the volar surface of the forearm (the same forearm surface was used for each sampling throughout the study) [36–38]. Skin swabs were immediately placed into a solution of 0.15 M NaCl and 0.1 % Tween 20 . All subjects were encouraged to provide specimens in the AM prior to breakfast and freeze them at −20 °C prior to transporting on ice to the study site, where they were frozen at −80 °C until use in this study. Exclusion criteria included prior antibiotic use for 1 year prior to the initiation of the study and preexisting medical conditions such as diabetes, inflammatory bowel disease, and organ transplantation that might result in significant immunosuppression. All subjects self-reported their health status and were genetically unrelated.
Analysis of 16S rRNA
Genomic DNA was prepared from the saliva and skin swabs of each subject and time point using the QIAGEN QIAamp DNA MINI kit (QIAGEN). Each sample was subjected to a bead beating step prior to nucleic acid extraction using Lysing Matrix-B (MP Bio). Genomic DNA was prepared from fecal samples using the QIAGEN QIAamp DNA Stool Mini Kit (QIAGEN). We amplified the bacterial 16S rRNA gene V1–V2 hypervariable region using the forward primer 8F (AGAGTTTGATCCTGGCTCAG) fused with the Ion Torrent Adaptor A sequence and 1 of 70 unique 10 base pair barcodes and reverse primer 357R (CTGCTGCCTYCCGTA) fused with the Ion Torrent Adaptor P1 from each donor and sample type . PCR reactions were performed using Platinum High Fidelity PCR SuperMix (Invitrogen) with the following cycling parameters: 94 °C for 10 min, followed by 30 cycles of 94 °C for 30 s, 53 °C for 30 s, 72 °C for 30 s, and a final elongation step of 72 °C for 10 min. Resulting amplicons were purified on a 2 % agarose gel stained with SYBR Safe (Invitrogen) using the MinElute PCR Purification Kit (QIAGEN). Amplicons were further purified with Ampure XP beads (Beckman-Coulter), and molar equivalents were determined for each sample by quantifying the amplicons using PicoGreen (Invitrogen) using a plate reader. Samples were pooled into equal molar proportions and sequenced on 316 chips using an Ion Torrent PGM according to manufacturer’s instructions (Life Technologies) . Resulting sequence reads were removed from the analysis if they were <180 nucleotides or >500 nucleotides, had any barcode or primer errors, contained any ambiguous characters, or contained any stretch of >8 consecutive homopolymers. Sequences then were trimmed according to any site that had a Phred score of less than 10 . Sequences then were assigned to their respective samples based on a 10-nucleotide barcode sequence and were further processed to remove reads that were greater than 3 standard deviations from the mean read lengths in any specimen. Skin reads were further processed to remove any reads matching >97 % to a read from an Alphaproteobacteria identified from the uninoculated control solution using Ion Assist (www.thepridelaboratory.org).
We sequenced a minimum of 10,000 reads from each sample and analyzed the sequence data using Quantitative Insights Into Microbial Ecology (QIIME 1.5) . We randomly sampled 8000 reads from each sample to create subsamples that were used in each analysis using Ion Assist (www.thepridelaboratory.org). Representative OTUs from each set were chosen at a minimum sequence identity of 97 % using the QIIME script pick_otus_through_otu_table, which uses the Greengenes database . PCOA was performed based on beta diversity using weighted UniFrac distances  using the QIIME script beta_diversity_through_plots. The results of the beta diversity distance matrices were used to determine the weighted UniFrac distances between different samples and sample groups. For each subject group, we determined weighted UniFrac distances between each subject pair within a household at each time point studied and compared those distances with between all pairs of subjects who did not reside in the same household at the same time points. Statistical significance was determined by the Mann Whitney U test using MaxStat Pro (www.maxstat.de). This technique was utilized for determining the distances between household pairs who were couples or roommates, household pairs who received amoxicillin and placebo, and household pairs who received azithromycin and placebo. We also calculated the distances between all household pairs receiving a specific therapy at each time point studied. Statistical significance was performed by comparing the mean distances over time among household pairs using the Kruskal Wallis test using MaxStat Pro. We utilized this technique to determine distances over time among households who received amoxicillin and azithromycin or that contained couples or roommates.
Alpha diversity using the Shannon diversity index  was determined using QIIME. The results were normalized by body site to allow for direct comparisons of changes in diversity between all the study subjects. We used several different techniques for comparing diversity between the study subjects. First, we calculated the change in diversity for all subjects by comparing each time point with their Shannon Index values on day 0. For each household, we next compared the change in diversity between the subjects by subtracting the change in diversity of the subject taking the placebo from the change in diversity observed in the subject taking antibiotics. Negative values indicated that there was a reduction in diversity in the subjects taking antibiotics compared to their housemate, and positive values indicated that there was greater diversity in subjects taking antibiotics compared to their housemate. The change in diversity values were calculated for all household pairs in different subject groups, and the means and standard errors utilized to decipher trends. Statistical differences in the means were determined using the Mann Whitney U test. We utilized this technique to decipher whether subject groups who received amoxicillin and azithromycin had significant differences in diversity between the housemates. The next technique that we utilized was to determine whether there were diversity reductions present independent of housemates. We first normalized the Shannon index values by body site and calculated the change in diversity using day 0 as the index value. We calculated this change in diversity among all subjects who received amoxicillin, azithromycin, placebo, or no therapy and utilized the means and standard errors to help decipher trends in the data. Statistical significance was determined by comparing each time point between groups who received antibiotics with those who received no therapy by the Mann Whitney U test. The last technique utilized did not use day 0 as an index time point and instead compared the change in diversity across each time point studied. Statistical differences in alpha diversity were determined using the Mann Whitney U test for comparisons between each group taking antibiotics and those of the controls.
We identified OTUs with significant changes in their relative abundances by examining the OTU tables produced using QIIME. These OTU tables were normalized and visually represented as heatmaps utilizing MEV 4.9 . Significant differences (p < 0.01) in individual taxa were determined by direct comparisons of each subject taking antibiotics with their housemate. These comparisons were performed using the Rank Products algorithm  typically used to identify differential expression in response to perturbations. We reported only those taxa that were significantly different between household members and represented 1 % or greater of the taxa present in the majority of individuals studied.
We thank Jonathan Gao, Leslie Yip, Philip Herbert, and Ryan McKinzie for their contributions to this work.
Supported by the Burroughs Wellcome Fund and the Doris Duke Charitable Foundation to DTP.
Availability of data and materials
All sequences are available for download in the NCBI Sequence Read Archive under Accession number SRP073335. Ion Assist software is available for download at www.thepridelaboratory.org and runs on Windows XP or higher.
SRA, MBJ, and DTP conceived and designed the experiments. SRA, TSR, ML, and MBJ performed the experiments. SRA, MBJ, SY, NK, and DTP analyzed the data. SRA and DTP contributed reagents, performed examinations, and recruited subjects. SRA, MBJ, KEN, and DTP wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
The consent forms signed by each participant included their consent to allow us to publish our findings.
Ethics approval and consent to participate
Subject recruitment and enrollment were approved by the University of California, San Diego Human Research Protection Program. All subjects signed written informed consent indicating their willingness to participate in this study. Each subject was compensated for their continued participation in this longitudinal study.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. Bacterial community variation in human body habitats across space and time. Science. 2009;326:1694–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Minot S, Sinha R, Chen J, Li H, Keilbaugh SA, Wu GD, Lewis JD, Bushman FD. The human gut virome: inter-individual variation and dynamic response to diet. Genome Res. 2011;21:1616–25.View ArticlePubMedPubMed CentralGoogle Scholar
- Pride DT, Salzman J, Haynes M, Rohwer F, Davis-Long C, White 3rd RA, Loomer P, Armitage GC, Relman DA. Evidence of a robust resident bacteriophage population revealed through analysis of the human salivary virome. ISME J. 2012;6:915–26.View ArticlePubMedGoogle Scholar
- Willner D, Furlan M, Schmieder R, Grasis JA, Pride DT, Relman DA, Angly FE, McDole T, Mariella Jr RP, Rohwer F, Haynes M. Metagenomic detection of phage-encoded platelet-binding factors in the human oral cavity. Proc Natl Acad Sci U S A. 2011;108 Suppl 1:4547–53.View ArticlePubMedGoogle Scholar
- Breitbart M, Hewson I, Felts B, Mahaffy JM, Nulton J, Salamon P, Rohwer F. Metagenomic analyses of an uncultured viral community from human feces. J Bacteriol. 2003;185:6220–3.View ArticlePubMedPubMed CentralGoogle Scholar
- Reyes A, Haynes M, Hanson N, Angly FE, Heath AC, Rohwer F, Gordon JI. Viruses in the faecal microbiota of monozygotic twins and their mothers. Nature. 2010;466:334–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Willing BP, Dicksved J, Halfvarson J, Andersson AF, Lucio M, Zheng Z, Jarnerot G, Tysk C, Jansson JK, Engstrand L. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology. 2010;139:1844–54. e1841.View ArticlePubMedGoogle Scholar
- Arrieta MC, Stiemsma LT, Dimitriu PA, Thorson L, Russell S, Yurist-Doutsch S, Kuzeljevic B, Gold MJ, Britton HM, Lefebvre DL, et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci Transl Med. 2015;7:307ra152.View ArticlePubMedGoogle Scholar
- Relman DA. The human microbiome: ecosystem resilience and health. Nutr Rev. 2012;70 Suppl 1:S2–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Fujimura KE, Slusher NA, Cabana MD, Lynch SV. Role of the gut microbiota in defining human health. Expert Rev Anti Infect Ther. 2010;8:435–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A. 2005;102:11070–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006;444:1022–3.View ArticlePubMedGoogle Scholar
- Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31.View ArticlePubMedGoogle Scholar
- Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60.View ArticlePubMedGoogle Scholar
- Craven M, Egan CE, Dowd SE, McDonough SP, Dogan B, Denkers EY, Bowman D, Scherl EJ, Simpson KW. Inflammation drives dysbiosis and bacterial invasion in murine models of ileal Crohn’s disease. PLoS One. 2012;7:e41594.View ArticlePubMedPubMed CentralGoogle Scholar
- Hsiao EY, McBride SW, Hsien S, Sharon G, Hyde ER, McCue T, Codelli JA, Chow J, Reisman SE, Petrosino JF, et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell. 2013;155:1451–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Lay C, Rigottier-Gois L, Holmstrom K, Rajilic M, Vaughan EE, de Vos WM, Collins MD, Thiel R, Namsolleck P, Blaut M, Dore J. Colonic microbiota signatures across five northern European countries. Appl Environ Microbiol. 2005;71:4153–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Markle JG, Frank DN, Mortin-Toth S, Robertson CE, Feazel LM, Rolle-Kampczyk U, von Bergen M, McCoy KD, Macpherson AJ, Danska JS. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science. 2013;339:1084–8.View ArticlePubMedGoogle Scholar
- Mueller S, Saunier K, Hanisch C, Norin E, Alm L, Midtvedt T, Cresci A, Silvi S, Orpianesi C, Verdenelli MC, et al. Differences in fecal microbiota in different European study populations in relation to age, gender, and country: a cross-sectional study. Appl Environ Microbiol. 2006;72:1027–33.View ArticlePubMedPubMed CentralGoogle Scholar
- Slots J, Feik D, Rams TE. Age and sex relationships of superinfecting microorganisms in periodontitis patients. Oral Microbiol Immunol. 1990;5:305–8.View ArticlePubMedGoogle Scholar
- Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, Magris M, Hidalgo G, Baldassano RN, Anokhin AP, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486:222–7.PubMedPubMed CentralGoogle Scholar
- Zapata HJ, Quagliarello VJ. The microbiota and microbiome in aging: potential implications in health and age-related diseases. J Am Geriatr Soc. 2015;63:776–81.View ArticlePubMedPubMed CentralGoogle Scholar
- Fleming-Dutra KE, Hersh AL, Shapiro DJ, Bartoces M, Enns EA, File Jr TM, Finkelstein JA, Gerber JS, Hyun DY, Linder JA, et al. Prevalence of inappropriate antibiotic prescriptions among US ambulatory care visits, 2010-2011. JAMA. 2016;315:1864–73.View ArticlePubMedGoogle Scholar
- Dethlefsen L, Huse S, Sogin ML, Relman DA. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol. 2008;6:e280.View ArticlePubMedPubMed CentralGoogle Scholar
- Zaura E, Brandt BW, Teixeira de Mattos MJ, Buijs MJ, Caspers MP, Rashid MU, Weintraub A, Nord CE, Savell A, Hu Y, et al. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. MBio 2015;e01693-15.Google Scholar
- Chang JY, Antonopoulos DA, Kalra A, Tonelli A, Khalife WT, Schmidt TM, Young VB. Decreased diversity of the fecal microbiome in recurrent Clostridium difficile-associated diarrhea. J Infect Dis. 2008;197:435–8.View ArticlePubMedGoogle Scholar
- Schubert AM, Sinani H, Schloss PD. Antibiotic-induced alterations of the murine gut microbiota and subsequent effects on colonization resistance against Clostridium difficile. MBio. 2015;6:e00974.View ArticlePubMedPubMed CentralGoogle Scholar
- Song SJ, Lauber C, Costello EK, Lozupone CA, Humphrey G, Berg-Lyons D, Caporaso JG, Knights D, Clemente JC, Nakielny S, et al. Cohabiting family members share microbiota with one another and with their dogs. Elife. 2013;2:e00458.PubMedPubMed CentralGoogle Scholar
- Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM, Scott NM, Gibbons SM, Larsen P, Shogan BD, Weiss S, et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science. 2014;345:1048–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457:480–4.View ArticlePubMedGoogle Scholar
- Moore AM, Ahmadi S, Patel S, Gibson MK, Wang B, Ndao MI, Deych E, Shannon W, Tarr PI, Warner BB, Dantas G. Gut resistome development in healthy twin pairs in the first year of life. Microbiome. 2015;3:27.View ArticlePubMedPubMed CentralGoogle Scholar
- Penders J, Stobberingh EE, Savelkoul PH, Wolffs PF. The human microbiome as a reservoir of antimicrobial resistance. Front Microbiol. 2013;4:87.View ArticlePubMedPubMed CentralGoogle Scholar
- Lozupone C, Hamady M, Knight R. UniFrac—an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinformatics. 2006;7:371.View ArticlePubMedPubMed CentralGoogle Scholar
- Kort R, Caspers M, van de Graaf A, van Egmond W, Keijser B, Roeselers G. Shaping the oral microbiota through intimate kissing. Microbiome. 2014;2:41.View ArticlePubMedPubMed CentralGoogle Scholar
- Faith JJ, Guruge JL, Charbonneau M, Subramanian S, Seedorf H, Goodman AL, Clemente JC, Knight R, Heath AC, Leibel RL, et al. The long-term stability of the human gut microbiota. Science. 2013;341:1237439.View ArticlePubMedPubMed CentralGoogle Scholar
- Grice EA, Kong HH, Renaud G, Young AC, Bouffard GG, Blakesley RW, Wolfsberg TG, Turner ML, Segre JA. A diversity profile of the human skin microbiota. Genome Res. 2008;18:1043–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Gao Z, Tseng CH, Pei Z, Blaser MJ. Molecular analysis of human forearm superficial skin bacterial biota. Proc Natl Acad Sci U S A. 2007;104:2927–32.View ArticlePubMedPubMed CentralGoogle Scholar
- Hanski I, von Hertzen L, Fyhrquist N, Koskinen K, Torppa K, Laatikainen T, Karisola P, Auvinen P, Paulin L, Makela MJ, et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc Natl Acad Sci U S A. 2012;109:8334–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Whiteley AS, Jenkins S, Waite I, Kresoje N, Payne H, Mullan B, Allcock R, O'Donnell A. Microbial 16S rRNA Ion Tag and community metagenome sequencing using the Ion Torrent (PGM) Platform. J Microbiol Methods. 2012;91:80–8.View ArticlePubMedGoogle Scholar
- Rothberg JM, Hinz W, Rearick TM, Schultz J, Mileski W, Davey M, Leamon JH, Johnson K, Milgrew MJ, Edwards M, et al. An integrated semiconductor device enabling non-optical genome sequencing. Nature. 2011;475:348–52.View ArticlePubMedGoogle Scholar
- Ewing B, Green P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 1998;8:186–94.View ArticlePubMedGoogle Scholar
- Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.View ArticlePubMedPubMed CentralGoogle Scholar
- DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.View ArticlePubMedPubMed CentralGoogle Scholar
- Gotelli NJ, Colwell RK. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 2001;4:379–91.View ArticleGoogle Scholar
- Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003;34:374–8.PubMedGoogle Scholar
- Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004;573:83–92.View ArticlePubMedGoogle Scholar