Complex and unexpected outcomes of antibiotic therapy against a polymicrobial infection

Abstract

Antibiotics are our primary approach to treating complex infections, yet we have a poor understanding of how these drugs affect microbial communities. To better understand antimicrobial effects on host-associated microbial communities we treated cultured sputum microbiomes from people with cystic fibrosis (pwCF, n = 24) with 11 different antibiotics, supported by theoretical and mathematical modeling-based predictions in a mucus-plugged bronchiole microcosm. Treatment outcomes we identified in vitro that were predicted in silico were: 1) community death, 2) community resistance, 3) pathogen killing, and 4) fermenter killing. However, two outcomes that were not predicted when antibiotics were applied were 5) community profile shifts with little change in total bacterial load (TBL), and 6) increases in TBL. The latter outcome was observed in 17.8% of samples with a TBL increase of greater than 20% and 6.8% of samples with an increase greater than 40%, demonstrating significant increases in community carrying capacity in the presence of an antibiotic. An iteration of the mathematical model showed that TBL increase was due to antibiotic-mediated release of pH-dependent inhibition of pathogens by anaerobe fermentation. These dynamics were verified in vitro when killing of fermenters resulted in a higher community carrying capacity compared to a no antibiotic control. Metagenomic sequencing of sputum samples during antibiotic therapy revealed similar dynamics in clinical samples. This study shows that the complex microbial ecology dictates the outcomes of antibiotic therapy against a polymicrobial infection.

The Cystic Fibrosis Upper and Lower Airway Metagenome

“…CF airway ecology has been investigated by the 16S rRNA gene profile of bacterial taxa ( 5 – 8 , 10 – 26 , 61 ) and by WGS sequencing of the metagenome ( 3 , 34 – 36 , 38 – 48 , 50 ). A common rationale for metagenome sequencing is the identification of the genetic content and the functional potential of the microbiota, particularly if most taxa of the habitat of interest are still unknown ( 33 , 64 ).…”

“…The focus of past microbial metagenome studies investigating the lower CF airway has been on small cohorts of CF adolescents and adults who naturally expectorate relatively large volumes of respiratory secretions ( 34 – 48 ). We have optimized our protocols so that deep microbial metagenome sequencing has become feasible with samples retrieved from all age groups ( 3 , 49 , 50 ).…”

Effects of Lumacaftor-Ivacaftor on Airway Microbiota-Mycobiota and Inflammation in Patients with Cystic Fibrosis Appear To Be Linked to Pseudomonas aeruginosa Chronic Colonization

“…After pretreatment with an equal volume of Sputasol (Oxoid, Basingstoke, UK) for 30 min at 37°C followed by centrifugation (1,500 × g for 10 min), the supernatants and pellets were separately frozen at −20°C for calprotectin and GM assessments and DNA extraction, respectively ( 4 , 52 ). We used the DNeasy PowerSoil kit (Qiagen, Les Ulis, France) to extract DNA from the samples, as described previously ( 53 ), after ensuring that it allowed the lysis of all bacteria and fungi in our artificial community (see the supplemental material). Next, we followed the manufacturer’s protocol and enhanced the mechanical lysis step with Precellys evolution (two cycles of 30 s at 7,000 rpm), as described previously ( 54 ).…”

Inflammation and Infection in Cystic Fibrosis: Update for the Clinician

“…A study by Ghuneim et al tested in vitro a mathematical model of interactions between the different CF microbial communities [ 20 ]. This mathematical model was biofilm-based and was developed by the same researchers in order to predict the changes in microbial populations based on oxygen and ph gradients [ 19 ].…”

“…The most common bacteria were found to be Pseudomonas, Streptococcus, Veillonella, Hemophilus, Fusobacterium, Prevotella, Staphylococcus, Achromobacter, and Neisseria and the bacterial genera that were primarily responsible for the community differentiation were Pseudomonas, Streptococcus and Staphylococcus. Different antibiotics had different impacts, but all antibiotics made an impact on the CF microbiome, thus highlighting that the dynamics of microbial interactions in the CF lung are constantly changing and also depend on antibiotic use [ 20 ].…”