Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome

Hedvig E Jakobsson, Cecilia Jernberg, Anders F Andersson, Maria Sjölund-Karlsson, Janet K Jansson, Lars Engstrand, Hedvig E Jakobsson, Cecilia Jernberg, Anders F Andersson, Maria Sjölund-Karlsson, Janet K Jansson, Lars Engstrand

Abstract

Antibiotic administration is the standard treatment for the bacterium Helicobacter pylori, the main causative agent of peptic ulcer disease and gastric cancer. However, the long-term consequences of this treatment on the human indigenous microbiota are relatively unexplored. Here we studied short- and long-term effects of clarithromycin and metronidazole treatment, a commonly used therapy regimen against H. pylori, on the indigenous microbiota in the throat and in the lower intestine. The bacterial compositions in samples collected over a four-year period were monitored by analyzing the 16S rRNA gene using 454-based pyrosequencing and terminal-restriction fragment length polymorphism (T-RFLP). While the microbial communities of untreated control subjects were relatively stable over time, dramatic shifts were observed one week after antibiotic treatment with reduced bacterial diversity in all treated subjects in both locations. While the microbiota of the different subjects responded uniquely to the antibiotic treatment some general trends could be observed; such as a dramatic decline in Actinobacteria in both throat and feces immediately after treatment. Although the diversity of the microbiota subsequently recovered to resemble the pre treatment states, the microbiota remained perturbed in some cases for up to four years post treatment. In addition, four years after treatment high levels of the macrolide resistance gene erm(B) were found, indicating that antibiotic resistance, once selected for, can persist for longer periods of time than previously recognized. This highlights the importance of a restrictive antibiotic usage in order to prevent subsequent treatment failure and potential spread of antibiotic resistance.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Phyla distribution in throat samples.
Figure 1. Phyla distribution in throat samples.
Pie charts showing the phyla found in throat samples at day 0, day 8–13, 1 and 4 years in three controls (A, B, and C) and three antibiotic treated patients (D, E, and F). By using 16S rRNA pyrosequencing five different phyla were found in the throat samples; Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Fusobacteria.
Figure 2. Phyla distribution in fecal samples.
Figure 2. Phyla distribution in fecal samples.
Pie charts showing the phyla found in fecal samples at day 0, day 8–13, 1 and 4 years in three controls (A, B, and C) and three antibiotic treated patients (D, E, and F). By using 16S rRNA pyrosequencing four different phyla were found in fecal samples; Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes.
Figure 3. Individualized antibiotic responses.
Figure 3. Individualized antibiotic responses.
The heat maps show the relative abundance per sample of different taxonomic groups found in throat (A) or fecal (B) patient samples. The color panel shows the percent relative abundance (0–70%) of different taxonomic groups within the major phyla (Actinobacteria, Bacteroidetes, Firmicutes, Fusobacterium and Proteobacteria) detected from patients at day 0 (1), day 8–13 (2), 1 year (3) and 4 years (4) after treatment.
Figure 4. Correlation plots for the controls.
Figure 4. Correlation plots for the controls.
Correlation plots showing OTU frequency at day 0 (x-axis), and day 8–13, 1 and 4 years (y-axis) in throat (A) and fecal (B) samples in the controls (A, B, and C). Bray-Curtis values are indicated as numbers in the figure as a number. A Bray Curtis value of 0 suggest the two sites have the same composition and 1 means the two sites do not share any species. The color of the dots represent different phyla: yellow, Actinobacteria; green, Bacteroidetes; blue, Firmicutes; red, Proteobacteria; grey, other phyla. Percentages of inter-sample variation explained by the two axes are shown in the figures.
Figure 5. Correspondence analysis of the bacterial…
Figure 5. Correspondence analysis of the bacterial community found in throat samples.
Each correspondence analysis plot represents the relative abundance values for the OTUs from the 16S rRNA pyrosequencing at day 0, day 8–13, 1 and 4 years. A: Controls; A, B, and C. B: Antibiotic treated patients; D, E, and F. Percentages of inter-sample variation explained, by the two axes are shown in the figures. In controls A–C and patients D–F the third axis represented 25%, 27%, 26%, 22%, 24%, and 20% of the variation.
Figure 6. Correspondence analysis of the bacterial…
Figure 6. Correspondence analysis of the bacterial community found in fecal samples.
Each correspondence analysis plot represents the relative abundance values for the OTUs from the 16S rRNA pyrosequencing at day 0, day 8–13, 1 and 4 years. A: Controls; A, B, and C. B: Antibiotic treated patients; D, E, and F. Percentages of inter-sample variation explained, by the two axes are shown in the figures. In controls A–C and patients D–F the third axis represented 19%, 17%, 20%, 26%, 20%, and 20% of the variation.
Figure 7. Correlation plots for the patients.
Figure 7. Correlation plots for the patients.
Correlation plots showing OTU frequency at day 0 (x-axis), day 8–13, 1 and 4 years (y-axis) in throat (A) and fecal (B) samples in the patients (D, E, and F). Bray-Curtis values are indicated as numbers in the figure. A Bray Curtis value of 0 suggest the two sites have the same composition and 1 that the two sites do not share any species. The colors of the dots represent different phyla: yellow, Actinobacteria; green, Bacteroidetes; blue, Firmicutes; red, Proteobacteria; grey, other phyla.
Figure 8. erm (B) abundance over time.
Figure 8. erm(B) abundance over time.
The normalized fold increase of erm(B) compared to day 0 in community DNA extracted from fecal samples for controls (A–C) not receiving any treatment (A) and patients (D–F) receiving antibiotics (B). Each bar graph represents the mean and standard error of the normalized expression of erm(B) compared to 16S. Normalization was carried out as previously been described .

References

    1. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, et al. Bacterial community variation in human body habitats across space and time. Science. 2009;326:1694–1697.
    1. Gibson GR, Roberfroid MB. Dietary modulation of the human colonic microbiota: introducing the concept of prebiotics. J Nutr. 1995;125:1401–1412.
    1. Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, et al. Metagenomic analysis of the human distal gut microbiome. Science. 2006;312:1355–1359.
    1. Round JL, Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and disease. Nat Rev Immunol 2009
    1. Guarner F. Enteric flora in health and disease. Digestion. 2006;73(Suppl 1):5–12.
    1. Hooper LV. Bacterial contributions to mammalian gut development. Trends Microbiol. 2004;12:129–134.
    1. Adamsson I, Nord CE, Lundquist P, Sjostedt S, Edlund C. Comparative effects of omeprazole, amoxycillin plus metronidazole versus omeprazole, clarithromycin plus metronidazole on the oral, gastric and intestinal microflora in Helicobacter pylori-infected patients. J Antimicrob Chemother. 1999;44:629–640.
    1. Jernberg C, Lofmark S, Edlund C, Jansson JK. Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. Isme J. 2007;1:56–66.
    1. Sjölund M, Tano E, Blaser MJ, Andersson DI, Engstrand L. Persistence of resistant Staphylococcus epidermidis after single course of clarithromycin. Emerg Infect Dis. 2005;11:1389–1393.
    1. Sjölund M, Wreiber K, Andersson DI, Blaser MJ, Engstrand L. Long-term persistence of resistant Enterococcus species after antibiotics to eradicate Helicobacter pylori. Ann Intern Med. 2003;139:483–487.
    1. Jakobsson H, Wreiber K, Fall K, Fjelstad B, Nyren O, et al. Macrolide resistance in the normal microbiota after Helicobacter pylori treatment. Scand J Infect Dis. 2007;39:757–763.
    1. Sullivan A, Edlund C, Nord CE. Effect of antimicrobial agents on the ecological balance of human microflora. Lancet Infect Dis. 2001;1:101–114.
    1. Courvalin P. Transfer of antibiotic resistance genes between gram-positive and gram-negative bacteria. Antimicrob Agents Chemother. 1994;38:1447–1451.
    1. Graham DY. Therapy of Helicobacter pylori: current status and issues. Gastroenterology. 2000;118:S2–8.
    1. de Boer WA, Tytgat GN. Regular review: treatment of Helicobacter pylori infection. Bmj. 2000;320:31–34.
    1. Dunn BE, Cohen H, Blaser MJ. Helicobacter pylori. Clin Microbiol Rev. 1997;10:720–741.
    1. Jönsson M, Qvarnström Y, Engstrand L, Swedberg G. Clarithromycin treatment selects for persistent macrolide-resistant bacteria in throat commensal flora. Int J Antimicrob Agents. 2005;25:68–74.
    1. Roberts MC. Update on macrolide-lincosamide-streptogramin, ketolide, and oxazolidinone resistance genes. FEMS Microbiol Lett. 2008;282:147–159.
    1. Portillo A, Ruiz-Larrea F, Zarazaga M, Alonso A, Martinez JL, et al. Macrolide resistance genes in Enterococcus spp. Antimicrob Agents Chemother. 2000;44:967–971.
    1. Buhling A, Radun D, Muller WA, Malfertheiner P. Influence of anti-Helicobacter triple-therapy with metronidazole, omeprazole and clarithromycin on intestinal microflora. Aliment Pharmacol Ther. 2001;15:1445–1452.
    1. Tanaka J, Fukuda Y, Shintani S, Hori K, Tomita T, et al. Influence of antimicrobial treatment for Helicobacter pylori infection on the intestinal microflora in Japanese macaques. J Med Microbiol. 2005;54:309–314.
    1. Suau A, Bonnet R, Sutren M, Godon JJ, Gibson GR, et al. Direct analysis of genes encoding 16S rRNA from complex communities reveals many novel molecular species within the human gut. Appl Environ Microbiol. 1999;65:4799–4807.
    1. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, et al. Diversity of the human intestinal microbial flora. Science. 2005;308:1635–1638.
    1. Andersson AF, Lindberg M, Jakobsson H, Bäckhed F, Nyren P, et al. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS ONE. 2008;3:e2836.
    1. Ley RE, Peterson DA, Gordon JI. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell. 2006;124:837–848.
    1. Pei Z, Bini EJ, Yang L, Zhou M, Francois F, et al. Bacterial biota in the human distal esophagus. Proc Natl Acad Sci U S A. 2004;101:4250–4255.
    1. Bik EM, Eckburg PB, Gill SR, Nelson KE, Purdom EA, et al. Molecular analysis of the bacterial microbiota in the human stomach. Proc Natl Acad Sci U S A. 2006;103:732–737.
    1. Franks AH, Harmsen HJ, Raangs GC, Jansen GJ, Schut F, et al. Variations of bacterial populations in human feces measured by fluorescent in situ hybridization with group-specific 16S rRNA-targeted oligonucleotide probes. Appl Environ Microbiol. 1998;64:3336–3345.
    1. Jernberg C, Sullivan A, Edlund C, Jansson JK. Monitoring of antibiotic-induced alterations in the human intestinal microflora and detection of probiotic strains by use of terminal restriction fragment length polymorphism. Appl Environ Microbiol. 2005;71:501–506.
    1. Donskey CJ, Hujer AM, Das SM, Pultz NJ, Bonomo RA, et al. Use of denaturing gradient gel electrophoresis for analysis of the stool microbiota of hospitalized patients. J Microbiol Methods. 2003;54:249–256.
    1. Tannock GW, Munro K, Harmsen HJ, Welling GW, Smart J, et al. Analysis of the fecal microflora of human subjects consuming a probiotic product containing Lactobacillus rhamnosus DR20. Appl Environ Microbiol. 2000;66:2578–2588.
    1. Zoetendal EG, Akkermans AD, De Vos WM. Temperature gradient gel electrophoresis analysis of 16S rRNA from human fecal samples reveals stable and host-specific communities of active bacteria. Appl Environ Microbiol. 1998;64:3854–3859.
    1. Krogius-Kurikka L, Kassinen A, Paulin L, Corander J, Makivuokko H, et al. Sequence analysis of percent G+C fraction libraries of human faecal bacterial DNA reveals a high number of Actinobacteria. BMC Microbiol. 2009;9:68.
    1. Kassinen A, Krogius-Kurikka L, Makivuokko H, Rinttila T, Paulin L, et al. The fecal microbiota of irritable bowel syndrome patients differs significantly from that of healthy subjects. Gastroenterology. 2007;133:24–33.
    1. Moore WE, Moore LH. Intestinal floras of populations that have a high risk of colon cancer. Appl Environ Microbiol. 1995;61:3202–3207.
    1. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457:480–484.
    1. Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. Defining the normal bacterial flora of the oral cavity. J Clin Microbiol. 2005;43:5721–5732.
    1. Williams JD, Maskell JP, Shain H, Chrysos G, Sefton AM, et al. Comparative in-vitro activity of azithromycin, macrolides (erythromycin, clarithromycin and spiramycin) and streptogramin RP 59500 against oral organisms. J Antimicrob Chemother. 1992;30:27–37.
    1. 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.
    1. Löfmark S, Jernberg C, Jansson JK, Edlund C. Clindamycin-induced enrichment and long-term persistence of resistant Bacteroides spp. and resistance genes. J Antimicrob Chemother. 2006;58:1160–1167.
    1. Cresti S, Lattanzi M, Zanchi A, Montagnani F, Pollini S, et al. Resistance determinants and clonal diversity in group A streptococci collected during a period of increasing macrolide resistance. Antimicrob Agents Chemother. 2002;46:1816–1822.
    1. Cole JR, Chai B, Farris RJ, Wang Q, Kulam-Syed-Mohideen AS, et al. The ribosomal database project (RDP-II): introducing myRDP space and quality controlled public data. Nucleic Acids Res. 2007;35:D169–172.
    1. Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991;173:697–703.
    1. Muyzer G, de Waal EC, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700.
    1. Hayek LCaMAB. New York: Columbia University Press; 1996. Surveying natural populations.

Source: PubMed

3
Prenumerera