Comparison of the Respiratory Resistomes and Microbiota in Children Receiving Short versus Standard Course Treatment for Community-Acquired Pneumonia

Abstract

Pediatric community-acquired pneumonia (CAP) is often treated with 10 days of antibiotics. Shorter treatment strategies may be effective and lead to less resistance. The impact of duration of treatment on the respiratory microbiome is unknown. Data are from children (n = 171), ages 6 to 71 months, enrolled in the SCOUT-CAP trial (NCT02891915). Children with CAP were randomized to a short (5 days) versus standard (10 days) beta-lactam treatment strategy. Throat swabs were collected at enrollment and the end of the study and used for shotgun metagenomic sequencing. The number of beta-lactam and multidrug efflux resistance genes per prokaryotic cell (RGPC) was significantly lower in children receiving the short compared to standard treatment strategy at the end of the study (Wilcoxon rank sum test, P < 0.05 for each). Wilcoxon effect sizes were small for beta-lactam (r: 0.15; 95% confidence interval [CI], 0.01 to 0.29) and medium for multidrug efflux RGPC (r: 0.23; 95% CI, 0.09 to 0.37). Analyses comparing the resistome at the beginning and end of the trial indicated that in contrast to the standard strategy group, the resistome significantly differed in children receiving the short course strategy. Relative abundances of commensals such as Neisseria subflava were higher in children receiving the standard strategy, and Prevotella species and Veillonella parvula were higher in children receiving the short course strategy. We conclude that children receiving 5 days of beta-lactam therapy for CAP had a significantly lower abundance of antibiotic resistance determinants than those receiving standard 10-day treatment. These data provide an additional rationale for reductions in antibiotic use when feasible. IMPORTANCE Antibiotic resistance is a major threat to public health. Treatment strategies involving shorter antibiotic courses have been proposed as a strategy to lower the potential for antibiotic resistance. We examined relationships between the duration of antibiotic treatment and its impact on resistance genes and bacteria in the respiratory microbiome using data from a randomized controlled trial of beta-lactam therapy for pediatric pneumonia. The randomized design provides reliable evidence of the effectiveness of interventions and minimizes the potential for confounding. Children receiving 5 days of therapy for pneumonia had a lower prevalence of two different types of resistance genes than did those receiving the 10-day treatment. Our data also suggest that children receiving longer durations of therapy have a greater abundance of antibiotic resistance genes for a longer period of time than do children receiving shorter durations of therapy. These data provide an additional rationale for reductions in antibiotic use.

Keywords: antibiotic resistance; children; community-acquired pneumonia; microbiota; resistome; respiratory tract infections.

Conflict of interest statement

The authors declare a conflict of interest. VGF reports personal consultancy fees from Novartis, Novadigm, Durata, Debiopharm, Genentech, Achaogen, Affinium, Medicines Co., Cerexa, Tetraphase, Trius, MedImmune, Bayer, Theravance, Basilea, Affinergy, Janssen, xBiotech, Contrafect, Regeneron, Basilea, Destiny, Amphliphi Biosciences. Integrated Biotherapeutics; C3J, Armata, Valanbio; Akagera, Aridis; Grants from NIH, MedImmune, Allergan, Pfizer, Advanced Liquid Logics, Theravance, Novartis, Merck; Medical Biosurfaces; Locus; Affinergy; Contrafect; Karius; Genentech, Regeneron, Basilea, Janssen, Royalties from UpToDate; Stock options Valanbio; a patent pending in sepsis diagnostics; educational fees from Green Cross, Cubist, Cerexa, Durata, Theravance, and Debiopharm; and an editor's stipend from IDSA. CBC reports personal consultancy fees from Astellas, Vir Biotechnology, Horizon Therapeutics, Altimmune, Premier Healthcare; grants from Merck and GSK; royalties from UpToDate. JMM reports grants from NIH and Merck and consultancy fees from Merck. WCH is a member of Endpoint Adjudication Committee for Pfizer and advisory board member for ADMA Biologics. E.B.W reports potential conflicts due to research support received from Pfizer and Moderna and as a member of a scientific advisory board for Vaxcyte.

Figures

FIG 1

SCOUT-CAP study design and timeline. SCOUT-CAP was a multicenter, randomized, double-blind, placebo-controlled, superiority clinical trial, which evaluated a short course (5 days) versus standard course (10 days) strategy of beta-lactam therapy for outpatient treatment of pediatric CAP. Participants were enrolled on days 3 to 6 of their initially prescribed oral beta-lactam therapy and randomized to 5 days of matching placebo (short course strategy) or an additional 5 days of their prestudy antibiotic (standard course strategy). Outcome assessment visits (OAV) occurred on study day 6 to 10 (OAV1) and study day 19 to 25 (OAV2). Throat swabs used in this study were collected at enrollment and OAV2.

FIG 2

Boxplot of beta-lactam, macrolide, and multidrug efflux resistance genes per prokaryotic cell (RGPC) in throat swabs from 171 participants at the end of the study. The boxplots depict the distribution of RGPC for beta-lactam (top left), macrolide (top right), and multidrug efflux (bottom left) resistance genes. The line reflects the median RGPC, lower and upper hinges correspond to the first and third quartiles, respectively, and upper and lower whiskers extend from the hinge to the highest value that is within 1.5× interquartile range of the hinge. A one-sided Wilcoxon rank sum test was used to assess statistically significant differences (alpha level 2 normalized RGPC data were used for visualization.

FIG 3

Comparison of abundances of beta-lactam and multidrug efflux resistance genes per prokaryotic cell (RGPC) at the enrollment visit and end of the study by treatment strategy (n = 158). Boxplots of beta-lactam (left) and multidrug efflux (right) resistance genes per prokaryotic cell (RGPC) in throat swabs from 158 participants at the enrollment visit and end of the study stratified by treatment strategy. The line reflects the median RGPC, lower and upper hinges correspond to the first and third quartiles, respectively, and upper and lower whiskers extend from the hinge to the highest value that is within 1.5× interquartile range of the hinge. A one-sided Wilcoxon signed-rank test was used to assess statistically significant differences (alpha level <0.05) between the numbers of RGPC in participants at enrollment (study day 1) and at the end of the study (outcome assessment visit 2 [OAV2]); participants were stratified by assignment to a short course (light and dark purple) or standard course (light and dark blue) strategy. FDR-adjusted P values are shown. Log2 normalized RGPC data were used for visualization.

FIG 4

Principal-coordinate analysis (PCoA) plot of Bray-Curtis dissimilarity of the resistome at enrollment and OAV2 by treatment strategy (n = 158). Principal-coordinate analysis (PCoA) plot of Bray-Curtis dissimilarity of all type-level resistance genes per prokaryotic cell (RGPC) (i.e., the resistome) at enrollment (study day 1) and end of the study (outcome assessment visit 2 [OAV2]) stratified by treatment strategy. Study day 1 is shown in red, and OAV2 is shown in blue.

FIG 5

Heatmap comparing the abundances of 92 bacterial species in 171 children at outcome assessment visit 2 (OAV2). Column-scaled heatmap of log2 transformed relative abundance of the 92 prevalence-filtered bacterial taxa identified in throat samples of 171 children at OAV2 (color key is indicated in the upper left corner). Complete linkage clustering of samples was based on the species and abundance. Bars represent treatment strategy assignment and quartiles of beta-lactamase, macrolide, and multidrug resistance gene types; the color key is indicated in the lower left corner.

FIG 6

Species showing a significant difference in abundance in samples with a high versus low abundance of beta-lactam resistance genes. The relative abundances of species identified as differentially abundant, in samples with a high versus low abundance of beta-lactam resistance genes, by ANCOM-BC. Effect sizes were estimated via log fold (natural log) change in relative abundance of each species between high and low. Taxa more abundant in the high-beta-lactam RGPC group have an effect size shifted to the left and are shown in red, whereas taxa more abundant in the low-beta-lactam RGPC group have an effect size shifted to the right and are in blue.