Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation

Abstract

The indigenous human microbiota is essential to the health of the host. Although the microbiota can be affected by many features of modern life, we know little about its responses to disturbance, especially repeated disturbances, and how these changes compare with baseline temporal variation. We examined the distal gut microbiota of three individuals over 10 mo that spanned two courses of the antibiotic ciprofloxacin, analyzing more than 1.7 million bacterial 16S rRNA hypervariable region sequences from 52 to 56 samples per subject. Interindividual variation was the major source of variability between samples. Day-to-day temporal variability was evident but constrained around an average community composition that was stable over several months in the absence of deliberate perturbation. The effect of ciprofloxacin on the gut microbiota was profound and rapid, with a loss of diversity and a shift in community composition occurring within 3-4 d of drug initiation. By 1 wk after the end of each course, communities began to return to their initial state, but the return was often incomplete. Although broadly similar, community changes after ciprofloxacin varied among subjects and between the two courses within subjects. In all subjects, the composition of the gut microbiota stabilized by the end of the experiment but was altered from its initial state. As with other ecosystems, the human distal gut microbiome at baseline is a dynamic regimen with a stable average state. Antibiotic perturbation may cause a shift to an alternative stable state, the full consequences of which remain unknown.

Fig. 1.

Heat map displaying the relative abundance of refOTUs in three prominent clades of bacteria. Relative abundance is based on the number of pyrosequencing reads clustering into each refOTU after normalizing the number of reads per sample. Clades are indicated on the left; Ba is all 174 refOTUs within the Bacteroidetes phylum. Within the Firmicutes phylum (Fi), the figure shows clades within the Lachnospiraceae (573 refOTUs) and the Ruminococcaceae (211 refOTUs) that contain the prominent genera named on the right. Each column corresponds to a sample in sequential order for each of subjects D, E, and F, with the times of Cp administration indicated by white bars at the top and bottom of the heat map. Each row corresponds to 1 refOTU, listed in phylogenetic order as defined by the Silva 100 reference tree. Color intensity is proportional to the logarithm of refOTU abundance from 0 to 200 reads as indicated by the scale; color is saturated for abundance of 200 or more.

Fig. 2.

Three measures of biological diversity for samples from subjects D (A), E (B), and F (C). Calculations were made after rarefying to an equal number of reads for all samples to control for unequal sampling effort. Narrow gray rectangles indicate the 5-d Cp courses; daily sampling around these times allowed visualization of daily fluctuations in diversity parameters that were not evident during less frequent sampling. RefOTU richness (number of refOTUs observed per sample) is shown on the left y axis; phylogenetic diversity (PD; total branch length of the phylogenetic tree relating all refOTUs in the sample) and the Shannon index of diversity are shown on the right y axis. The x axis reflects experiment day.

Fig. 3.

PCoA of unweighted UniFrac distances, a phylogenetically aware measure of intersample (β) diversity. A covers the period encompassing the first Cp, with samples before Cp (pre-Cp), during the first Cp course (first Cp), during the first week post-Cp (first WPC), and in the interim period between Cp courses (interim) progressing from darker to lighter shades. B covers the second Cp period, with interim samples in the darkest shades and the second Cp course, second WPC, and post-Cp periods becoming progressively lighter. Data points from A are also shown on B with small gray symbols to facilitate comparisons between the two parts of the experiment. Numbers adjacent to some samples track the Cp perturbation by indicating the number of days elapsed since the start of Cp administration; missing numbers indicate that samples were not collected on those days.

Fig. 4.

Distance-based redundancy analysis (15) of Bray–Curtis intersample distances calculated with log₂-transformed abundance data. As in Fig. 3, A and B show samples surrounding the first and second Cp course respectively, with interim samples appearing in both panels. Symbol color and numbering are as described for Fig. 3.