Environmental Science and Pollution Research

, Volume 25, Issue 15, pp 14743–14751 | Cite as

Temporal dynamics of gut microbiota in triclocarban-exposed weaned rats

  • Rebekah C. Kennedy
  • Russell R. Fling
  • Michael S. Robeson
  • Arnold M. Saxton
  • Liesel G. Schneider
  • John L. Darcy
  • David A. Bemis
  • Ling Zhao
  • Jiangang Chen
Research Article


Widely used as an antimicrobial in antibacterial bar soaps, triclocarban (3,4,4′-trichlorocarbanilide; TCC) is effective against Gram-positive bacteria but shows little efficacy against Gram-negative strains, potentially altering the composition of indigenous microflora within and on the human body. To date, the consequence of continuous or previous nonprescription antimicrobial exposure from compounds in personal care products on commensal microflora is still elusive. Previous research has shown that TCC exposure during gestation and lactation induced dysbiosis of gut microbial communities among exposed dams and neonates. However, the impact of antimicrobial exposure specifically after discontinuation of the use of TCC on the gut microbiota has not been investigated. In this study, weaned Sprague Dawley rats (postnatal day, PND 22) were provided ad lib access to TCC-supplemented diet (0.2% w/w or 0.5% w/w) for 4 weeks (phase I) followed by a 4-week washout period (phase II) to determine gut microflora changes both during continuous exposure to TCC and to determine the potential rebound following TCC withdrawal. Fecal samples were collected at baseline (PND 22) prior to TCC exposure and throughout phase I and phase II. The V4 region of 16S rDNA was sequenced from extracted total fecal DNA with the MiSeq platform. Exposure to both 0.2% w/w and 0.5% w/w TCC was sufficient to alter diversity of microbiota during phase I of treatment. This effect was further prolonged into phase II, even when TCC exposure was discontinued. Collectively, these data highlight the impact of both continuous and prior TCC exposure on gut microbial ecology and shed light onto the potential long-term health risk of daily nonprescription antimicrobial personal care product use.


Microbiota Triclocarban Antimicrobial Gut microbiota 16S rDNA Weaned rats Antiseptic 


The human intestinal tract is home to a microcosm of microbes that act in symbiosis with the host to influence health status ranging from immunomodulation to development of the nervous system (Sekirov et al. 2010). Major changes in gut microbial colonization patterns occur during two primary phases, from birth to weaning and from weaning to adulthood (Wopereis et al. 2014). Early in life, the initiation and maturation of the neonatal microbiota is largely determined by exchanges between maternal-offspring microbiota (Mueller et al. 2015). The introduction of solid food diversifies the infant gut microbiota to a more adult-like, stable profile (Wopereis et al. 2014). However, environmental insults such as prescription antibiotic exposure can disrupt microbiota composition stability and can alter colonization patterns (Yassour et al. 2016), influencing susceptibility to both infectious and chronic disease (Vangay et al. 2015).

Though parents may not control whether their children are prescribed antibiotics, they are in control of consumer purchases. Marketing campaigns have successfully convinced the public to purchase nonprescription antibacterial personal care products, though the efficacy over regular soap to reduce infectious disease has not been firmly established (Giuliano and Rybak 2015; Levy 2001). Triclocarban (3,4,4′-trichlorocarbanilide; TCC) is an antimicrobial agent commonly added to antibacterial consumer bar soaps (Perencevich et al. 2001). As an antiseptic normally applied to the skin, TCC is primarily effective against Gram-positive bacteria with reduced efficacy for Gram-negative strains and fungi (McDonnell and Russell 1999), suggesting the potential to differentially disrupt mixed population microbial communities.

Following external application, TCC can be absorbed through the skin (Scharpf Jr et al. 1975) and has been detected in biological matrices collected from pregnancy (Pycke et al. 2014) to the adult stage (Ye et al. 2011), indicating lifelong exposure could begin during sensitive windows of physiological development. In addition, human exposure to TCC could occur through the diet when the compound is incompletely removed through the wastewater treatment process, potentially translocating to food crops irrigated with TCC-contaminated water (Wu et al. 2013) or fertilized with TCC-contaminated biosolids (Prosser et al. 2014). Currently, the level of exposure expected from the diet is not estimated to be acutely toxic to humans (Aryal and Reinhold 2011); however, the effect of the assumed exposure on more sensitive endpoints, such as the alteration of the gut microbiota composition during early life, is still largely elusive.

The widespread use of TCC has raised concerns about its toxicity beyond acting as an endocrine disruptor (Chen et al. 2008; Duleba et al. 2011). Currently, there is limited data available on the potential interplay of TCC with the human gut microbiome (Poole et al. 2016; Kennedy et al. 2016b). However, the research community is beginning to understand that environmental exposures, other than just prescription antibiotics, have the potential to disrupt the composition of the microbiome (Hu et al. 2016; Pasch et al. 2009; Syed et al. 2014) and that this alteration may be accompanied by physiologic consequences (Claus et al. 2016; Velmurugan et al. 2017). For instance, it has been shown that the interaction of certain environmental endocrine chemicals including nonprescription antimicrobials disruptors with gut microbiota could alter host glucose homeostasis (Velmurugan et al. 2017). It is therefore important that the evaluation of the interaction of chemicals and gut microbiota should be integrated within toxicity assessment tests (Claus et al. 2016; Velmurugan et al. 2017).

Previously, we reported that TCC provided orally to adult rats during gestation and lactation resulted in dysbiosis of the adult and neonatal gut microbiome compared to non-exposed controls (Kennedy et al. 2016a). However, it is still unknown how gut microbial communities will respond when exposure is removed and whether the composition could return to a pre-exposure-like state if the exposure occurred during early life. The objectives of this report were to investigate and characterize the temporal changes of the biodiversity and composition of intestinal microbiota in post-weaned rats after oral TCC exposure and to investigate the resilience of the gut microbiome after TCC exposure was discontinued.


Animals and husbandry

A total of 12 female Sprague Dawley (SD) rats were purchased from Harlan Laboratory (Dublin, VA) and arrived on postnatal day (PND) 21. Animals were weighed and randomly assigned to control or treatment groups (n = 4/group). In each cage, two rats from the same treatment group were housed with a 12:12-h light cycle, temperature of 20 to 22 °C, and relative humidity of 40 to 50%. Animals were provided ad libitum access to water and commercial Harlan ground 2020X chow or 2020X supplemented with 0.2 or 0.5% w/w TCC (purity 99%, Sigma Aldrich, St Louis, Missouri) from PND 22 until PND 50 (28 days, phase I). At PND 50, the diet of TCC-exposed animals was switched to 2020X without TCC supplement and animals were maintained on the same chow diet until PND 78 as a washout period (28 days, phase II). Animals in the control group were provided 2020X chow without TCC throughout the entire study period. The Animal Care and Use Committee at the University of Tennessee, Knoxville, approved all study protocols. All methods were conducted in accordance with the Institutional Animal Care and Use Committee (IACUC) guidelines. This investigation was conducted in an animal facility fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care.

Feces sample collection

Feces were collected at designated dates over the 28 days of exposure (phase I) and the 28 days of washout (phase II). Fecal collection was conducted as previously reported (Kennedy et al. 2016a). Fecal samples were collected at baseline (PND 22, prior to exposure to TCC-supplemented diet), 2 days post-treatment (PND 24), 5 days post-treatment (PND 27), 12 days post-treatment (PND 34), and 28 days post-treatment (PND 50). All fecal samples were collected in the morning. Starting from PND 50, after last pellets were collected, 2020X without TCC supplementation was provided for each exposure group and fecal samples were collected from the same animals at 2 days post-washout (PND 52), 8 days post-washout (PND 58), 11 days post-washout (PND 61), and 28 days post-washout (PND 78). The collection of fecal samples was more frequent at the beginning of the treatment as well as after the removal of treatment to catch the early dynamics of microbiota shortly after and following removal of TCC exposure.

DNA isolation, amplification, and 16S rDNA sequencing

DNA extraction, amplification, and clean-up

DNA was extracted from frozen fecal samples with the QIAamp DNA Stool Mini Kit (QIAGEN, Inc. Valencia, CA) following manufacturer’s instructions. DNA was amplified as described by Caporaso et al. (2012). DNA was purified and normalized as previously reported (Kennedy et al. 2016a).

Bacterial barcoded amplicon library preparation, sequencing, and sequence analysis

Beads clean-up

Pooled amplicons were purified, and the products were analyzed for quality assurance as previously reported (Kennedy et al. 2016a).

Library quantification and Illumina sequencing

The pooled amplicon library was quantified and sequenced on the Illumina MiSeq sequencer (Illumina, Inc., San Diego, CA) as previously reported (Kennedy et al. 2016a).

Sequence data analysis

The resulting raw sequencing data was analyzed using the QIIME (v1.9.1) pipeline as previously reported (Caporaso et al. 2010; Kennedy et al. 2016a). All samples were rarefied at a minimum sequencing depth of 4200 operational taxonomic units (OTUs). 16S datasets were deposited in the Sequence Read Archive under the BioProject Accession: PRJNA351840.


Statistical analysis on beta diversity was conducted using Phyloseq in R (version 3.3.1) (McMurdie and Holmes 2013). A repeated measures permanova was conducted on fecal samples stratified by rat ID using the BiodiversityR package, with separate whole and sub-plot analyses during phase I and phase II (Kindt and Coe 2005). Post hoc analysis of these results with the Vegan package was utilized to dissect significant time-treatment interactions during each phase (Oksanen et al. 2016). Alpha diversity estimates as well as relative abundance of OTUs were analyzed using mixed model ANOVA with repeated measures over time during each phase of TCC exposure with the SigmaPlot (version 14) followed by Fisher LSD post hoc test. Data were presented as group mean ± SEM. Relative abundance of OTUs at the phyla level was visualized with Phyloseq (McMurdie and Holmes 2013). Statistical significance was set at alpha = 0.05.


Body weights

Although we did not monitor the daily body weight of weaned rats throughout the whole experimental period, the initial body weight prior to treatment (PND 21: 51.35 ± 2.03 g, 48.49 ± 1.22 g, and 49.23 ± 1.31 g for control, 0.2% w/w and 0.5% w/w groups, respectively) as well as body weight gain from PND31 to PND 45 during the treatment among the three groups were not different (control: 58.0 ± 5.45 g; 0.2% w/w exposed: 55.83 ± 1.33 g; and 0.5% w/w exposed: 52.45 ± 1.32 g).

Alpha diversity

After quality filtering and removing of any OTU present at less than 0.005% of the total read count (Bokulich et al. 2013; Navas-Molina et al. 2013), 102 samples comprising 1,067,997 sequences remained with an average of 10,471 sequences per sample. Overall average phylogenetic diversity is shown during phase I (Fig. 1a) and phase II (Fig. 1b). Repeated measures ANOVA of phylogenetic diversity revealed a significant effect of time throughout phase I (ANOVA, p < 0.05), primarily due to a decrease in diversity across the entire treatment period of phase I [baseline (PND 22): control: 17.11 ± 1.83; 0.2% w/w: 16.50 ± 0.40; 0.5% w/w: 17.79 ± 1.25; 2 days (PND 24) exposure: control: 17.50 ± 0.90; 0.2% w/w: 16.00 ± 0.52; 10.5% w/w: 5.77 ± 0.79; 5 days (PND 27) exposure: control: 18.50 ± 0.81; 0.2% w/w: 15.30 ± 0.47; 0.5% w/w: 14.35 ± 0.13; 12 days (PND 34) exposure: control: 18.12 ± 1.99; 0.2% w/w: 15.23 ± 0.28; 0.5% w/w: 13.13 ± 0.40; 28 days (PND 50) exposure: control: 17.23 ± 3.93, 0.2% w/w: 13.17 ± 0.61; 0.5% w/w: 11.34 ± 0.26, Fisher LSD test, p < 0.05), Fig. 1a]. We also observed an overall effect of time that occurred throughout the course of phase II (ANOVA, p < 0.05) which was related to increases in alpha diversity [2 days (PND 52) post-washout: control: 17.80 ± 2.55; 0.2% w/w: 13.41 ± 1.00; 0.5% w/w: 11.99 ± 0.33; 8 days (PND 58) post-washout: control: 18.90 ± 0.67, 0.2% w/w: 15.25 ± 0.58, 0.5% w/w: 13.97 ± 0.47; 11 days (PND 61) post-washout: control: 18.32 ± 1.65, 0.2% w/w: 15.79 ± 0.34, 0.5% w/w 14.90 ± 0.45; 28 days (PND 78) post-washout: control: 19.17 ± 1.18, 0.2% w/w: 15.60 ± 0.14, 0.5% w/w 15.57 ± 0.43; Fisher LSD test, p < 0.05, Fig. 1b].
Fig. 1

Alpha diversity (phylogenetic diversity) during phase I (a) at 0 (baseline; PND 22), 2, 5, 12, and 28 days of exposure and during phase II (b) when TCC exposure was removed for 2, 8, 11, or 28 days (control: hatched bar, 0.2% w/w: gray bar, 0.5% w/w: black bar; n = 4/group). Data represent mean ± SEM of each group. ANOVA with repeated measures followed by Fisher LSD test; significant level alpha = 0.05. *Significant different from day 0 (a) or day 2 (b) of respective group

Beta diversity

The pairwise variance of weighted UniFrac distances between control samples compared to 0.2% w/w- and 0.5% w/w-exposed samples across both phase I and phase II are shown in Fig. 2a and Fig. 2b respectively, as a representation of beta diversity dissimilarity over time. During phase I, an initial microbial community shift occurred after 2 days of exposure where both beta diversity of 0.2% w/w- and 0.5% w/w-exposed samples moved away from control samples and remained separated from controls throughout the treatment period of phase I (Fig. 2a, b). After exposure was removed during phase II, 0.2 and 0.5% w/w microbial communities became more similar to the control microbiota over time until the end of the phase II washout. Statistically, an effect of time was demonstrated among weighted UniFrac distances using repeated measures ADONIS in both phases (ADONIS p < 0.05). Further, nested permanova analysis revealed a treatment effect on weighted UniFrac distances during both phase I (p < 0.05) and phase II (p < 0.05). A permanova of each subject, nested within treatment group demonstrated significant differences between the distance of control microbiota communities and the microbiota of both exposure groups during phase I (p < 0.05). Though a significant difference was only noted between control communities and 0.5% w/w communities during phase II (p < 0.05). No differences in beta diversity were noted between 0.2% w/w-exposed samples and 0.5% w/w-exposed samples during either phase I or phase II. Additionally, a time-treatment interaction was identified during phase I (ADONIS, p < 0.05). Post hoc analysis indicated that the interaction occurred after exposure was initiated at PND 22.
Fig. 2

Beta diversity during phase I and phase II of the study period. Relative abundance of operational taxonomic units (OTUs) at the phyla level was visualized with Phyloseq. Weighted UniFrac distances between Control and TCC exposure is shown during phase I (A) at 0 (baseline; PND 22), 2, 5, 12, and 28 days of exposure and during phase II (B) when TCC exposure was removed for 2, 8, 11, or 28 days. *Treatment difference differs from day 0 treatment difference (ADONIS, post hoc interaction contrast, P < 0.05)

Fecal microbiota community composition

The relative abundance of the gut microbial community composition of animals over time at the phylum level is shown in Fig. 3. During phase I, Firmicutes dominated in both 0.2% w/w- and 0.5% w/w-exposed samples, while Bacteroidetes was the prominent phylum in control samples (Fig. 3a). Significant differences of microbial community composition are shown during phase I in Table 1. When treatment was removed during phase II, the proportion of both Bacteroidetes and Firmicutes in both exposed groups appeared to normalize to that of controls (Fig. 3b). However, the relative abundance of certain bacteria at the phylum level, i.e., Tenericutes and Verrucomicrobia, were significantly altered into phase II (Table 2), even though exposure to TCC did not significantly modify the relative abundance of the Tenericutes phylum during phase I. A heat map of all relative abundance of microbiota at the phylum level is shown during phases I and II in Fig. 4.
Fig. 3

Relative abundance of bacteria at the phylum level during phase I (a) at 0 (baseline; PND 22), 2, 5, 12, and 28 days of exposure and during phase II (b) when TCC exposure was removed for 2, 8, 11, or 28 days

Table 1

Relative abundance of phase I microbial phyla


Least squares means

Collection day
















































< 1%

< 1%


< 1%



< 1%

< 1%





< 1%
























Two-way ANOVA with repeated measures followed by Fisher LSD test. Data displayed comprise > 1% of the relative abundance; least squares means displayed as percentage at each collection date.

*Significant difference compared to control at a designated collection day (p < 0.05)

+Significant difference compared to 0.2% w/w and control at a designated collection day (p < 0.05)

Table 2

Relative abundance of phase II microbial phyla


Least squares means

Collection day










< 1%

< 1%



< 1%

< 1%

< 1%

< 1%*


< 1%

< 1%


< 1%*



< 1%

< 1%

< 1%

< 1%





< 1%





< 1%

Two-way ANOVA with repeated measures followed by Fisher LSD test. Data displayed comprise > 1% of the relative abundance; least squares means displayed as percentage at each collection date

*Significant difference compared to control at a designated collection day (p < 0.05)

+Significant difference compared to 0.2% w/w at a designated collection day (p < 0.05)

Fig. 4

Heat map of the relative abundance of bacteria at the phylum level phase I (a) at 0 (baseline; PND 22), 2, 5, 12, and 28 days of exposure and during phase II (b) when TCC exposure was removed for 2, 8, 11, or 28 days. Color scale begins at the minimum relative abundance shown as blue (min) and continues to the maximum relative abundance shown as red


Due to insufficient data to demonstrate that ingredients in over-the-counter (OTC) consumer antiseptic wash products are both safe for long-term daily use and more effective than plain soap to prevent the spread of certain infections, a final ruling was issued from FDA to prohibit the marketing of certain antiseptic ingredients, including TCC (Food and Drug Administration 2016). Prescription antibiotic use during sensitive exposure windows is frequent, with the proportion of broad spectrum prescription antibiotics given to children and adolescents dramatically increasing over the previous decade (Lee et al. 2014). This is concerning given that inappropriate antibiotic use can potentially lead to dysbiosis of the gut resident community and produce a predisposition disease (Arrieta et al. 2014). Like prescription antibiotics, exposure to nonprescription antimicrobials during development is widespread (Frederiksen et al. 2013; Pycke et al. 2014), though biocides generally have a broader spectrum of activity than antibiotics (McDonnell and Russell 1999). We have previously demonstrated that TCC exposure to adult rats during gestation and lactation and their neonates alters the diversity and composition of the gut microbiome (Kennedy et al. 2016a). However, to the best of our knowledge, this study is the first to follow the dynamics of gut microbial community structure in rats exposed to TCC after weaning.

Weaned rats were exposed to TCC for 28 days, and fecal material was collected at specific time points. A washout period was then initiated for an additional 28 days to monitor the potential rebound of the gut microbial community structure. As shown in Fig. 1a, b, repeated measures ANOVA revealed that 0.5% w/w TCC exposure significantly altered phylogenetic diversity during the exposure period (phase I). In addition to the short-term perturbations that accompany prescription antibiotic exposure, long-term damage to the fecal microbiome has been noted with even single exposures to certain prescription antibiotics (Zaura et al. 2015). We demonstrated that even when TCC exposure was removed, fecal microbial communities did not completely rebound to a pre-exposure state in all exposure groups. When TCC exposure was removed during phase II, continued suppression of average taxa number within each sample was observed compared to that of controls (Fig. 1b). This information is not altogether surprising as TCC is an effective antimicrobial (McDonnell and Russell 1999), and board-spectrum prescription antibiotics are known to suppress alpha diversity (Lankelma et al. 2016). However, it is unclear, in our study, whether these alternations observed would lead to functional changes. Nonetheless, our data collectively imply that continuous exposure to TCC containing soap or potentially through the diet, during early life could suppress the richness of microbial taxa both during and after TCC exposure compared to those never exposed to TCC containing products/food.

Weighted UniFrac distances revealed a similar pattern to phylogenetic diversity across the treatment and washout periods. Both 0.2 and 0.5% w/w TCC exposure restricted diversity of microbial samples beginning as early as 2 days post exposure in phase I (Fig. 2a). During phase II, the variance of weighted UniFrac distances between control samples and previously exposed samples was reduced over the study period indicating that microbial communities became more similar when TCC exposure was removed (Fig. 2b). This general pattern of continued recovery reflects the effect of TCC exposure on fecal phylogenetic diversity. Narrowe et al. demonstrated that triclosan exposure alters the microbiome of juvenile fathead minnows (Narrowe et al. 2015). In their study, a week of triclosan exposure induced alterations in both alpha and beta diversity in fat head minnows that vanished after exposure was removed. This information collectively indicates that the length of exposure as well TCC exposure level may be particularly relevant to determine whether and how quickly the microbiota community could rebound after the exposure to nonprescription antimicrobials are discontinued. However, it is important to consider how the increasing age of the animals during phase II might additionally influence the effect of TCC exposure on the gut microbiota composition. In our study, TCC exposure was initiated post-weaning for 3 weeks and discontinued from PND 50 until PND 78. Recently, Hu and colleagues demonstrated that overall microbiota diversity of feces was disrupted in adolescent (PND 62) SD rats continuously exposed from birth to diethyl phthalate, methylparaben, triclosan, or mixtures of these compounds compared to controls (Hu et al. 2016). However, when fecal samples were collected during the adult stage (PND 181), diversity did not significantly differ. The authors concluded that the impact of exposure was more profound during adolescence and microbiota may develop resistance to insult over time, even though exposure was ongoing. Additionally, although as a whole, the results of the Narrowe et al. suggest that most of the gut communities returned to the same developmental path as those of the unexposed fish, a few samples from the triclosan-exposed groups remained distant from the main group of samples and may take longer or may never completely recover (Narrowe et al. 2015). Thus, further investigation is warranted to understand what the effect would be if animals were followed into even later life stages.

Given that TCC exposure appeared to affect the gut microbiota structure both during active treatment and when exposure was discontinued, we sought to understand how TCC exposure influenced individual bacterium phylum distribution throughout phase I and phase II (Fig. 3a, b). Bacteroidetes and Firmicutes dominated in all samples collected during both the phase I (exposure) and phase II (washout). When the microbial community structure was analyzed over time, TCC exposure significantly decreased the relative abundance of Bacteroidetes, while the Firmicutes phylum was significantly increased compared to controls during phase I (Table 1). Fluctuations in Bacteroidetes and Firmicutes have been associated with weight changes and obesity (Duncan et al. 2008; Sweeney and Morton 2013). When exposure was removed, no differences were noted in the most dominant phyla (Bacteroidetes and Firmicutes) in samples collected from either exposure group compared to controls. However, the relative abundance of the Verrucomicrobia phylum was significantly increased with prior exposure to 0.5% w/w TCC compared to control samples shortly after exposure was removed (Table 2), while the relative abundance of Tenericutes in phase II suppressed 28 days after exposure was discontinued compared to controls. Interestingly, deviations in both these phyla (Verrucomicrobia and Tenericutes) are associated with exposure to a high-fat diet in a mouse model (Everard et al. 2014). We have previously shown that Tenericutes was suppressed in dam fecal microbiota after exposure to TCC during gestation and lactation compared to control animals (Kennedy et al. 2016a), suggesting that this phylum may be particularly sensitive to TCC exposure. Further, Tenericutes was identified as a significantly heritable taxon among healthy individuals (Lindheim et al. 2017), and higher diversity and an enrichment of bacteria from this phylum has been shown in healthy patients, compared to metabolic syndrome patients (Lim et al. 2017; Lindheim et al. 2017). Future studies are warranted to investigate whether the impact on Tenericutes by TCC exposure may indicate long-term collateral physiological consequences even when exposure is removed.

The selection of dosages was based on previous studies (Chen et al. 2008; Duleba et al. 2011; Kennedy et al. 2015, 2016a). When dams were treated during gestation, there was no statistical difference in body weight gain between the control and the 0.2% w/w TCC-supplemented group (Kennedy et al. 2015) as well as body weight, although the body weight of 0.5% w/w-treated dams at GD 19 was 6.7% less than that of control dams. We have demonstrated that the serum TCC concentration of rats after oral exposure to 0.2% w/w TCC was similar to or within an order of magnitude of the concentrations reported in the blood of human volunteers, specifically in those who were regular users of TCC-containing soap (Kennedy et al. 2015; Schebb et al. 2011). For the current study, although we did not monitor the daily body weight of weaned rats throughout the whole experimental period, the initial body weight prior to treatment (PND 21: 51.35 ± 2.03 g, 48.49 ± 1.22 g, and 49.23 ± 1.31 g for control, 0.2 and 0.5% w/w groups, respectively) as well as body weight gain from PND31 to PND 45 during the treatment among the three groups were not different (control: 58.0 ± 5.45 g; 0.2% w/w exposed: 55.83 ± 1.33 g; and 0.5% w/w exposed: 52.45 ± 1.32 g). It is currently unknown how this serum/oral concentration would translate to gut microbial exposure to TCC in humans, although similar concentrations have been used in animal toxicological investigations (Duleba et al. 2011; Nolen and Dierckman 1979). Thus, our results are interesting given the potential application to human exposure, as well as providing insight into additional effects of oral TCC exposure in animal models.

Several limitations of our study were identified. Previously, we showed that TCC exposure during gestation and lactation led to enlarged abdomens with mustard-colored diarrhea in suckling neonates. However, no apparent histological differences were noted between the unexposed and exposed neonates (Kennedy et al. 2016a). In the present study, diarrhea was not observed in exposed weaned rats throughout the study period; thus, pathological evaluation was not conducted. We did not monitor physiological parameters, such as body weight during the whole study period, and no additional biological samples besides feces were collected or analyzed. Research has shown that gut microbiota dysbiosis could be associated with clinical manifestations of metabolic disease and contribute to metabolic impairment or health (Lippert et al. 2017; Velmurugan et al. 2017). Future investigation is warranted to assess if physiological and biological perturbations accompany changes in gut microbial composition after TCC exposure in weaned rats.


Even after TCC is no longer marketed in the USA (Food and Drug Administration 2016), continuous TCC exposure may still occur through alternative sources (Prosser et al. 2014; Wu et al. 2013). We demonstrated that the impact of TCC exposure on gut microbiota community composition could be long lasting if exposure were to begin early in life (i.e., prior to adolescence) even after exposure is discontinued. The potential impact on the individual’s predisposition to certain disease states warrants further investigation.



The study was conducted at the University of Tennessee Knoxville and supported by the National Institutes of Environmental Health Sciences to Dr. Jiangang Chen (1R21ES017475-01A1). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIEHS.

Compliance with ethical standards

The authors and the conduction of experiments adhered to the research ethics guidelines of the journal.

Competing interests

The authors declare that they have no conflicts of interest.


  1. Arrieta MC, Stiemsma LT, Amenyogbe N, Brown EM, Finlay B (2014) The intestinal microbiome in early life: health and disease. Front Immunol 5:427CrossRefGoogle Scholar
  2. Aryal N, Reinhold DM (2011) Phytoaccumulation of antimicrobials from biosolids: impacts on environmental fate and relevance to human exposure. Water Res 45:5545–5552CrossRefGoogle Scholar
  3. Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA, Caporaso JG (2013) Quality-filtering vastly improves diversity estimates from illumina amplicon sequencing. Nat Methods 10:57–59CrossRefGoogle Scholar
  4. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) Qiime allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336CrossRefGoogle Scholar
  5. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R (2012) Ultra-high-throughput microbial community analysis on the illumina hiseq and miseq platforms. ISME J 6:1621–1624CrossRefGoogle Scholar
  6. Chen J, Ahn KC, Gee NA, Ahmed MI, Duleba AJ, Zhao L, Gee SJ, Hammock BD, Lasley BL (2008) Triclocarban enhances testosterone action: a new type of endocrine disruptor? Endocrinology 149:1173–1179CrossRefGoogle Scholar
  7. Claus SP, Guillou H, Ellero-Simatos S (2016) The gut microbiota: a major player in the toxicity of environmental pollutants? NPJ Biofilms Microbiomes 2:16003CrossRefGoogle Scholar
  8. Duleba AJ, Ahmed MI, Sun M, Gao AC, Villanueva J, Conley AJ, Turgeon JL, Benirschke K, Gee NA, Chen J, Green PG, Lasley BL (2011) Effects of triclocarban on intact immature male rat: augmentation of androgen action. Reprod Sci 18:119–127CrossRefGoogle Scholar
  9. Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P, Flint HJ (2008) Human colonic microbiota associated with diet, obesity and weight loss. Int J Obes 32:1720–1724CrossRefGoogle Scholar
  10. Everard A, Lazarevic V, Gaia N, Johansson M, Stahlman M, Backhed F et al (2014) Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity. ISME J 8:2116–2130CrossRefGoogle Scholar
  11. Food and Drug Administration (2016) Safety and effectiveness of consumer antiseptics; topical antimicrobial drug products for over-the-counter human use. Final rule 0097-6326 (Print)Google Scholar
  12. Frederiksen H, Aksglaede L, Sorensen K, Nielsen O, Main KM, Skakkebaek NE, Juul A, Andersson AM (2013) Bisphenol a and other phenols in urine from danish children and adolescents analyzed by isotope diluted turboflow-lc-ms/ms. Int J Hyg Environ Health 216:710–720CrossRefGoogle Scholar
  13. Giuliano CA, Rybak MJ (2015) Efficacy of triclosan as an antimicrobial hand soap and its potential impact on antimicrobial resistance: a focused review. Pharmacotherapy 35:328–336CrossRefGoogle Scholar
  14. Hu J, Raikhel V, Gopalakrishnan K, Fernandez-Hernandez H, Lambertini L, Manservisi F, Falcioni L, Bua L, Belpoggi F, L.Teitelbaum S, Chen J (2016) Effect of postnatal low-dose exposure to environmental chemicals on the gut microbiome in a rodent model. Microbiome 4:26CrossRefGoogle Scholar
  15. Kennedy RC, Menn FM, Healy L, Fecteau KA, Hu P, Bae J et al (2015) Early life triclocarban exposure during lactation affects neonate rat survival. Reprod Sci 22:75–89CrossRefGoogle Scholar
  16. Kennedy RC, Fling RR, Robeson MS, Saxton AM, Donnell RL, Darcy JL, Bemis DA, Liu J, Zhao L, Chen J (2016a) Temporal development of gut microbiota in triclocarban exposed pregnant and neonatal rats. Sci Rep 6:33430CrossRefGoogle Scholar
  17. Kennedy RC, Terry PD, Chen J (2016b) Triclocarban and health: the jury is still out. mSphere 1:e00239–e00216CrossRefGoogle Scholar
  18. Kindt R, Coe R (2005) Tree diversity analysis: a manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre, Nairobi PMCid: PMC1156951Google Scholar
  19. Lankelma JM, Belzer C, Hoogendijk AJ, de Vos AF, de Vos WM, van der Poll T, Wiersinga WJ (2016) Antibiotic-induced gut microbiota disruption decreases tnf-alpha release by mononuclear cells in healthy adults. Clin Transl Gastroenterol 7:e186CrossRefGoogle Scholar
  20. Lee GC, Reveles KR, Attridge RT, Lawson KA, Mansi IA, Lewis JS 2nd et al (2014) Outpatient antibiotic prescribing in the United States: 2000 to 2010. BMC Med 12:96CrossRefGoogle Scholar
  21. Levy SB (2001) Antibacterial household products: cause for concern. Emerg Infect Dis 7:512–515CrossRefGoogle Scholar
  22. Lim MY, You HJ, Yoon HS, Kwon B, Lee JY, Lee S, Song YM, Lee K, Sung J, Ko GP (2017) The effect of heritability and host genetics on the gut microbiota and metabolic syndrome. Gut 66:1031–1038CrossRefGoogle Scholar
  23. Lindheim L, Bashir M, Munzker J, Trummer C, Zachhuber V, Leber B et al (2017) Alterations in gut microbiome composition and barrier function are associated with reproductive and metabolic defects in women with polycystic ovary syndrome (pcos): a pilot study. PLoS One 12:e0168390CrossRefGoogle Scholar
  24. Lippert K, Kedenko L, Antonielli L, Kedenko I, Gemeier C, Leitner M, Kautzky-Willer A, Paulweber B, Hackl E (2017) Gut microbiota dysbiosis associated with glucose metabolism disorders and the metabolic syndrome in older adults. Benef Microbes 8:545–556CrossRefGoogle Scholar
  25. McDonnell G, Russell AD (1999) Antiseptics and disinfectants: activity, action, and resistance. Clin Microbiol Rev 12:147–179Google Scholar
  26. McMurdie PJ, Holmes S (2013) Phyloseq: an r package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217CrossRefGoogle Scholar
  27. Mueller NT, Bakacs E, Combellick J, Grigoryan Z, Dominguez-Bello MG (2015) The infant microbiome development: mom matters. Trends Mol Med 21:109–117CrossRefGoogle Scholar
  28. Narrowe AB, Albuthi-Lantz M, Smith EP, Bower KJ, Roane TM, Vajda AM, Miller CS (2015) Perturbation and restoration of the fathead minnow gut microbiome after low-level triclosan exposure. Microbiome 3:6CrossRefGoogle Scholar
  29. Navas-Molina JA, Peralta-Sanchez JM, Gonzalez A, McMurdie PJ, Vazquez-Baeza Y, Xu Z et al (2013) Advancing our understanding of the human microbiome using qiime. Methods Enzymol 531:371–444CrossRefGoogle Scholar
  30. Nolen GA, Dierckman TA (1979) Reproduction and teratogenic studies of a 2:1 mixture of 3,4,4′-trichlorocarbanilide and 3-trifluoromethyl-4,4′-dichlorocarbanilide in rats and rabbits. Toxicol Appl Pharmacol 51:417–425CrossRefGoogle Scholar
  31. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D et al (2016) Vegan: Community ecology package. CRAN - Package vegan -
  32. Pasch E, Voltmer L, Gemmell S, Walter J, Walton KL (2009) Effects of triclosan on the normal intestinal microbiota and on susceptibility to experimental murine colitis. FASEB J 23:977.910–977.910Google Scholar
  33. Perencevich EN, Wong MT, Harris AD (2001) National and regional assessment of the antibacterial soap market: a step toward determining the impact of prevalent antibacterial soaps. Am J Infect Control 29:281–283CrossRefGoogle Scholar
  34. Poole AC, Pischel L, Ley C, Suh G, Goodrich JK, Haggerty TD, Ley RE, Parsonnet J (2016) Crossover control study of the effect of personal care products containing triclosan on the microbiome. mSphere 1:e00056–e00015CrossRefGoogle Scholar
  35. Prosser RS, Lissemore L, Topp E, Sibley PK (2014) Bioaccumulation of triclosan and triclocarban in plants grown in soils amended with municipal dewatered biosolids. Environ Toxicol Chem 33:975–984CrossRefGoogle Scholar
  36. Pycke BF, Geer LA, Dalloul M, Abulafia O, Jenck AM, Halden RU (2014) Human fetal exposure to triclosan and triclocarban in an urban population from Brooklyn, New York. Environ Sci Technol 48:8831–8838CrossRefGoogle Scholar
  37. Scharpf LG Jr, Hill ID, Maibach HI (1975) Percutaneous penetration and disposition of triclocarban in man: body showering. Arch Environ Health 30:7–14CrossRefGoogle Scholar
  38. Schebb NH, Inceoglu B, Ahn KC, Morisseau C, Gee SJ, Hammock BD (2011) Investigation of human exposure to triclocarban after showering and preliminary evaluation of its biological effects. Environ Sci Technol 45:3109–3115CrossRefGoogle Scholar
  39. Sekirov I, Russell SL, Antunes LC, Finlay BB (2010) Gut microbiota in health and disease. Physiol Rev 90:859–904CrossRefGoogle Scholar
  40. Sweeney TE, Morton JM (2013) The human gut microbiome: a review of the effect of obesity and surgically induced weight loss. JAMA Surg 148:563–569CrossRefGoogle Scholar
  41. Syed AK, Ghosh S, Love NG, Boles BR (2014) Triclosan promotes staphylococcus aureus nasal colonization. MBio 5:e01015CrossRefGoogle Scholar
  42. Vangay P, Ward T, Gerber JS, Knights D (2015) Antibiotics, pediatric dysbiosis, and disease. Cell Host Microbe 17:553–564CrossRefGoogle Scholar
  43. Velmurugan G, Ramprasath T, Gilles M, Swaminathan K, Ramasamy S (2017) Gut microbiota, endocrine-disrupting chemicals, and the diabetes epidemic. Trends Endocrinol Metab 28:612–625CrossRefGoogle Scholar
  44. Wopereis H, Oozeer R, Knipping K, Belzer C, Knol J (2014) The first thousand days - intestinal microbiology of early life: establishing a symbiosis. Pediatr Allergy Immunol 25:428–438CrossRefGoogle Scholar
  45. Wu X, Ernst F, Conkle JL, Gan J (2013) Comparative uptake and translocation of pharmaceutical and personal care products (ppcps) by common vegetables. Environ Int 60:15–22CrossRefGoogle Scholar
  46. Yassour M, Vatanen T, Siljander H, Hamalainen AM, Harkonen T, Ryhanen SJ et al (2016) Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci Transl Med 8:343ra381CrossRefGoogle Scholar
  47. Ye X, Zhou X, Furr J, Ahn KC, Hammock BD, Gray EL, Calafat AM (2011) Biomarkers of exposure to triclocarban in urine and serum. Toxicology 286:69–74CrossRefGoogle Scholar
  48. Zaura E, Brandt BW, Teixeira de Mattos MJ, Buijs MJ, Caspers MP, Rashid MU et al (2015) Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. MBio 6:e01693–e01615CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Rebekah C. Kennedy
    • 1
    • 2
    • 3
  • Russell R. Fling
    • 4
  • Michael S. Robeson
    • 5
  • Arnold M. Saxton
    • 6
  • Liesel G. Schneider
    • 6
  • John L. Darcy
    • 7
  • David A. Bemis
    • 8
  • Ling Zhao
    • 9
  • Jiangang Chen
    • 1
  1. 1.Department of Public HealthThe University of Tennessee KnoxvilleKnoxvilleUSA
  2. 2.Comparative and Experimental MedicineThe University of Tennessee KnoxvilleKnoxvilleUSA
  3. 3.Department of Pharmacology and ToxicologyMichigan State UniversityEast LansingUSA
  4. 4.Department of Microbiology and Molecular GeneticsMichigan State UniversityEast LansingUSA
  5. 5.Department of Biomedical InformaticsUniversity of Arkansas for Medical SciencesLittle RockUSA
  6. 6.Department of Animal ScienceThe University of Tennessee KnoxvilleKnoxvilleUSA
  7. 7.Department of Ecology and Evolutionary BiologyUniversity of ColoradoBoulderUSA
  8. 8.Department of Biomedical and Diagnostic SciencesThe University of TennesseeKnoxvilleUSA
  9. 9.Department of NutritionThe University of TennesseeKnoxvilleUSA

Personalised recommendations