Edited by: Eric Giannoni, Lausanne University Hospital (CHUV), Switzerland
Reviewed by: Luisa Anna Denkel, Charité Universitätsmedizin Berlin, Germany; Catherine Stanton, Teagasc, The Irish Agriculture and Food Development Authority, Ireland
This article was submitted to Neonatology, a section of the journal Frontiers in Pediatrics
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Preterm infants experience unique challenges in establishing their gut microbiota. Cesarean deliveries, extensive antenatal, and neonatal antibiotic exposure, parenteral nutrition and residing for long periods in a neonatal intensive care unit (NICU), may cause unpredictable perturbations of the gut microbiota development (
Antibiotics are the most commonly prescribed medications in the NICU (
In Norway probiotic supplementation was implemented as standard of care for extremely preterm infants in 2014. In a longitudinal multi-center study, using shotgun-metagenomic sequencing, we set out to evaluate the influence of probiotics and antibiotic therapy on the developing gut microbiota and antibiotic resistome in extremely preterm infants supplemented with probiotics. We also compared these results to very preterm infants not supplemented with probiotics and a group of healthy, full-term infants.
We prospectively planned to include two convenient groups of preterm infants from six Norwegian NICUs; one group of extremely preterm infants (gestational age 25–27 weeks) supplemented with probiotics, and one group of very preterm infants (gestational age 28–31 weeks) not supplemented with probiotics. Exclusion criteria were gestation below 25 weeks and/or an early, life threatening condition leading to high risk of not surviving the first weeks of life. We included a control group of 10 healthy, vaginally delivered full-term control (FTC) infants born at the University Hospital of Northern Norway. Sample size calculation for studies assessing gut microbiota taxonomic composition can be performed by assessing matrices of pairwise distances between groups (
After careful instructions, fecal samples were collected by a nurse in the NICU at around seven and 28 days of age, and by the parents at home at around 4 months of age. We used a commercially available sampling kit (OMNIgen GUT kit, DNA Genotek, Ottawa, Canada) allowing storage of samples at ambient temperatures for up to 14 days before DNA extraction (
Total metagenomic DNA was extracted using the NorDiag Arrow Stool DNA Extraction kit (NorDiag, Oslo, Norway). An extra beadbeating step was added to facilitate cell lysis as studies have shown that this can increase extraction of DNA from Gram-positive bacteria. DNA was quantified using the Nanodrop 1000 and QubitⓇ 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA) along with the QubitⓇ dsDNA HR assay kit (Thermo Fisher Scientific, Waltham, MA, USA). DNA was then stored at −70°C. The indexed paired-end libraries were prepared for whole genome sequencing using the Nextera XT Kit (Illumina, San Diego, CA, USA), according to the manufacturer's instructions. Fifty nanograms of genomic DNA was tagmented at 55°C for 10 min. The tagmented DNA was amplified with two primers from Nextera DNA sample preparation Index Kit. PCR products were cleaned using Agencourt AMPure XP beads (Beckman Coulter, Indiana, USA). Purified PCR products were quantified using the QubitⓇ 2.0 (Invitrogen, Carlsbad, CA, USA), along with the QubitⓇ dsDNA HS assay kit (Thermo Fisher Scientific, Waltham, MA, USA). The fragment size distribution (500–1,000 bp) was analyzed using the Agilent 2100 Bioanalyzer System (Agilent Technologies, Waldbronn, Germany). The samples were pooled at concentration of 4 nM per sample. Eight to twelve samples were pooled per each sequencing run. Pooled samples was denatured with 0.2 N NaOH, then diluted to 10 pM with hybridization buffer. Subsequently, samples were submitted for v3 reagents with 2 × 300 cycles paired-end sequencing using the Illumina Miseq platform, according to the manufacturer's instructions. In total, 184 samples were sequenced to an average (range) sequence depth of 4.8 (1.8–12.6) million reads per sample for microbiota and functional analysis. Prior to all downstream data analysis, the sequence quality was calculated using FastQC (v0.11.3). All samples were screened for human contamination using Deconseq with default parameters and build up 38 of the human genome as reference. Quality filtering of the read was performed using Trimmomatic v0.36 with LEADING:3, TRAILING:3, MINLEN:75 as parameter settings. Assemblies were performed on the trimmed reads using MEGAHIT. Functional annotation was added using an in-house genome annotation pipeline, the META-pipe (Department of Chemistry, University of Tromsø, Norway [
The relative abundance of bacteria at genus level was calculated from the trimmed reads using MetaPhlAn 2.0 (
The prediction of genes presumed to confer antibiotic resistance was performed on the assembled metagenomes using Abricate [
Beta lactamase:
Methicillin resistance:
Aminoglycosides:
Tetracyclines:
Fluoroquinolones:
MLS: Macrolide:
ABC efflux:
RND efflux pumps:
Efflux pumps:
Multidrug efflux pumps:
Chloramphenicol:
Fosfomycin
Sulfonamides:
Antibiotic target:
Vancomycin:
Metronidazole:
In order to obtain quantitative measures of the putative ARGs in each sample, the quality trimmed reads were analyzed using Short, Better Representative Extract Dataset (ShortBRED) ( Class A Beta lactamase Class C Beta lactamase Aminoglycoside acetyltransferase Aminoglycoside phosphotransferase Aminoglycoside nucleotidyltransferase Tetracycline efflux Tetracycline ribosomal protection Quinolone resistance Macrolide/MLS resistance Adenosine triphosphate (ATP)-binding cassette (ABC) efflux pump Resistance/nodulation/division (RND) antibiotic efflux Major facilitator superfamily (MFS) antibiotic efflux Multidrug efflux pump activity Multidrug resistance efflux pump Genes modulating antibiotic efflux: Small multidrug resistance (SMR) antibiotic efflux Chloramphenicol acetyltransferase Antibiotic target Genes modulating resistance: rRNA methyltransferase Other ARG:
A consensus-based protocol for probiotic supplementation was implemented in Norway in 2014 (
To quantify changes in the gut microbiota composition and resistome after antibiotic exposure, we stratified four different categories of antibiotic exposure: (i) antenatal exposure, (ii) short (≤72 h) vs. prolonged (>72 h) exposure in the first week of life (
The study was approved by the Norwegian Regional Ethical Committee (2014/930/REK nord) and registered in Clinicaltrials.gov (
Data were analyzed using IBM-SPSS version 22 (IBM, Armonk NY, USA) statistical software, the R statistical framework (version 3.2.4;
Alpha diversity was assessed by calculating the Shannon Diversity index (MEGAN, v5.10.6) (
Figure
CONSORT study flow diagram. PEP, probiotic extremely preterm; NPVP, non-probiotic very preterm; FTC, full term control; NICU, Neonatal Intensive care Unit.
Clinical background data.
Birth weight [grams], median (IQR) | 835 (680–945) | 1,290 (1,150–1,445) | 3,613 (3,394–3,733) |
Gestational age [weeks], median (IQR) | 26 (26–27) | 30 (29–30) | 40 (40–41) |
Gender | |||
Male, |
13 (42%) | 20 (57%) | 3 (30) |
Female, |
18 (58%) | 15 (43%) | 7 (70) |
Route of delivery | |||
Cesarean, |
21 (68%) | 20 (57%) | 0 (0) |
Vaginal, |
10 (32%) | 15 (43%) | 10 (100) |
CRIB score, mean (SD) | 11 (2) | 5 (2) | – |
Any antenatal antibiotic exposure, |
8 (26%) | 12 (34%) | 0 (0) |
Any antibiotic exposure first week of life |
30 (97%) | 27 (77%) | – |
Median (IQR) days—antibiotics exposed infants | 6 (4–7) | 4 (3–5) | – |
Any antibiotic exposure after first week of life, |
22 (71%) | 5 (14%) | – |
Narrow spectrum regimen after first week of life, |
14 (45%) | 3 (9%) | – |
Broad-spectrum |
8 (26%) | 2 (5%) | – |
Median (IQR) days antibiotics in exposed infants | 6.5 (3–13) | 10 (5.5–14) | |
Total days antibiotics, median (IQR); antibiotics exposed infants, |
9.5 (6–18) |
4 (3–6) |
– |
Total days of probiotic supplementation, median (IQR) | 46 (40–57) | – | – |
Parenteral nutrition, |
31 (100%) | 16 (46%) | – |
Median (IQR) days parenteral nutrition | 9 (6–13) | 5 (3–8) | – |
Exclusive human milk nutrition until discharge | 17 (55%) | 16 (46%) | 10 (100) |
On day 7, we found higher relative abundance of
Median relative abundance (%) of dominant genera in infant gut microbiota at 7, 28 days, and 4 months of age.
64.7 | 0.00*** | 43.9 | < |
< |
36.7 | 33.5 | 74.1 | 0.088 | 0.156 | 38.3 | 49.6 | 71.2 | 0.243 | 0.555 | |
0.00 | 0.27 | 0.02 | 0.107 | 0.245 | 1.76 | 2.10 | 0.00 | 0.351 | 0.511 | 12.1 | 15.2 | 10.10 | 0.377 | 0.754 | |
0.00 | 0.00 | 0.00 | 0.737 | 0.786 | 0.00 | 0.00 | 0.00 | 0.663 | 0.816 | 0.25 | 0.67 | 0.11 | 0.738 | 1.0 | |
0.00 | 0.00 | 0.00 | 0.125 | 0.222 | 0.00 | 0.00 | 0.00 | 0.225 | 0.360 | 0.00 | 0.00 | 0.00 | 0.110 | 0.440 | |
1.10 | 0.54 | 0.05 | 0.230 | 0.368 | 0.51 | 0.23 | 0.01* | 0.00 | 0.00 | 0.00 | 0.472 | 0.839 | |||
0.00 | 0.00* | 0.75*** | < |
< |
0.00 | 1.09* | 1.38* | 4.75 | 4.44 | 8.59 | 0.812 | 1.0 | |||
0.00 | 0.01 | 0.00 | 0.118 | 0.236 | 0.90 | 2.35 | 0.00* | 0.39 | 1.53** | 0.58 | 0.019 | 0.152 | |||
0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.996 | 1.0 | |||||
0.00 | 0.00 | 0.00 | 0.368 | 0.535 | 0.00 | 0.00* | 0.00 | 0.00 | 0.00 | 0.00 | 0.098 | 0.523 | |||
0.00 | 0.00 | 1.45*** | < |
< |
0.00 | 0.06* | 0.26* | 0.15 | 0.14 | 0.06 | 0.149 | 0.477 | |||
0.00 | 0.00 | 0.00 | 1.0 | 1.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.0 | 0.00 | 0.00 | 0.00 | 0.171 | 0.456 | |
0.00 | 0.00* | 0.23 | 0.00 | 0.00 | 0.23 | 0.26 | 0.18 | 0.42 | 0.682 | 1.0 | |||||
0.00 | 0.00 | 0.00 | 0.716 | 0.818 | 0.00 | 0.00 | 0.00 | 0.435 | 0.580 | 0.00 | 0.00** | 0.00 | |||
0.00 | 0.00 | 0.00 | 0.525 | 0.70 | 0.00 | 0.00 | 0.00 | 0.834 | 0.953 | 0.00 | 0.00 | 0.00 | 1.000 | 1.0 | |
0.00 | 0.00 | 0.14* | < |
< |
0.00 | 0.00 | 0.07** | < |
< |
0.00 | 0.00 | 0.00 | 0.996 | 1.0 | |
0.00 | 0.00 | 0.00 | 0.607 | 0.747 | 0.00 | 0.00 | 0.00 | 0.834 | 0.890 | 0.00 | 0.00 | 0.00 | 1.000 | 1.0 |
On day 28, there was a striking increase in relative abundance of
By 4 months of age, there were no significant differences in taxonomic profile between PEP- and FTC-infants. The NPVP-infants had more
We found no significant influence of antenatal antibiotic exposure on the gut microbiota composition on day 7. However, 57/66 (86%) preterm infants also received antibiotic therapy (ampicillin or penicillin + gentamicin) during the first week of life (Table
Influence of antibiotic exposure (broad* vs. narrow) on taxonomic composition in all preterm infants (both PEP- and NPVP-infants) with fecal samples and who received antibiotics after first week of life.
14.4 | 28.9 | 0.783 | 14.3 | 41.5 | 0.096 | 0.512 | |
44.5 | 1.40 | 0.368 | 17.4 | 9.9 | 0.209 | 0.669 | |
0.00 | 0.00 | 0.680 | 0.25 | 0.57 | 0.845 | 0.623 | |
0.00 | 0.45 | 0.123 | 0.00 | 0.00 | 0.235 | 0.627 | |
0.42 | 0.08 | 0.783 | 0.00 | 0.00 | 1.00 | 1.00 | |
0.00 | 0.00 | 0.945 | 1.25 | 6.01 | |||
2.73 | 0.68 | 0.783 | 0.64 | 0.39 | 0.647 | 1.00 | |
0.00 | 0.00 | 0.630 | 0.07 | 0.18 | 0.126 | 0.504 | |
0.00 | 0.00 | 0.891 | 0.00 | 0.87 | 0.071 | 0.568 |
Influence of antibiotic exposure (broad* vs. narrow) on taxonomic composition in only the PEP-infants with fecal samples and who received antibiotics after first week of life.
14.39 | 32.50 | 0.574 | 14.31 | 45.96 | 0.035 | 0.187 | |
44.54 | 0.69 | 0.160 | 33.06 | 9.88 | 0.179 | 0.477 | |
0.00 | 0.00 | 0.721 | 0.26 | 0.57 | 1.000 | 1.00 | |
0.00 | 0.52 | 0.195 | 0.00 | 0.00 | 0.143 | 0.572 | |
0.42 | 0.36 | 0.879 | 0.00 | 0.00 | 1.000 | 1.000 | |
0.00 | 0.00 | 0.506 | 0.96 | 6.01 | |||
2.73 | 0.15 | 0.506 | 0.33 | 0.40 | 0.536 | 0.858 | |
0.54 | 0.00 | 0.442 | 0.07 | 0.14 | 0.285 | 0.651 | |
0.00 | 0.00 | 0.959 | 0.00 | 1.21 |
We found large intra-individual differences in the gut microbiota composition, in particular at 7 and 28 days of age (Figures
In all three groups, we identified putative ARGs conferring resistance to nine different classes of antibiotics, including beta lactams, aminoglycosides, tetracyclines, fosfomycine, sulphonamides, vancomycin, and the macrolide-lincosamide-streptogramin B group. Genes conferring resistance to fluoroquinolones and chloramphenicol were only detected in PEP- and NPVP-infants. Several genes encoding efflux pumps were also identified at all three sampling time points. In total 99 unique ARGs were identified, of which 28 (28%) were located on mobile genetic elements, and these latter were found in more than 80% of all infants (Table
Distribution of classes of antibiotic resistance genes among infants in each group.
Beta lactamases | 10/20 | 24/30 | 3/10 | 19/24 | 22/31 | 6/9 | 18/24 | 25/29 | 4/7 |
MecA gene | 9/20 | 11/30 | – | 5/24 | 5/31 | – | – | – | – |
Aminoglycoside | 8/20 | 14/30 | 3/10 | 11/24 | 16/31 | 2/9 | 12/24 | 16/29 | 2/7 |
Tetracycline | 9/20 | 22/30 | 8/10 | 17/24 | 30/31 | 9/9 | 23/24 | 29/29 | 7/7 |
Fluoroquinolones | – | 1/30 | – | 1/24 | – | – | 3/24 | 4/29 | – |
Macrolides | 7/20 | 5/30 | 2/10 | 6/24 | 2/31 | – | 2/24 | – | – |
MLS | 3/20 | 9/30 | 3/10 | 4/24 | 11/31 | 3/9 | 8/24 | 15/29 | 4/7 |
ABC efflux pumps | 6/20 | 7/30 | – | 16/24 | 24/31 | 4/9 | 17/24 | 23/29 | 7/7 |
RND efflux pumps | 7/20 | 12/30 | 2/10 | 12/24 | 18/24 | 4/9 | 12/24 | 19/24 | 5/7 |
Efflux pumps | 3/20 | 3/30 | 8/10 | 2/24 | 4/31 | 2/9 | 6/24 | 8/24 | 3/7 |
Multidrug Efflux pump | 9/20 | 14/30 | 1/10 | 11/24 | 7/31 | 1/9 | – | – | – |
Chloramphenicol | 3/30 | 9/30 | – | 6/24 | 7/31 | – | 9/24 | 3/29 | – |
Fosfomycine | 18/20 | 21/30 | 3/10 | 22/24 | 25/31 | 5/9 | 20/24 | 27/29 | 4/7 |
Sulfonamides | 2/20 | 3/30 | – | 6/24 | 7/31 | – | 10/24 | 9/29 | 2/7 |
Antibiotic target | 1/20 | 1/30 | – | 4/24 | 4/31 | – | 6/24 | 3/29 | 3/7 |
Antibiotic inactivation | – | 2/30 | 1/10 | 1/24 | 1/31 | – | 6/24 | 7/29 | 2/7 |
Vancomycin | – | – | – | – | – | – | 5/24 | 8/29 | 3/7 |
Metronidazole | – | – | – | – | – | – | – | 1/29 | – |
We found 21 different genes encoding beta-lactamases, including broad-spectrum and extended-spectrum beta lactamases (ESBLs). ESBL-genes were represented at all three time points in NPVP- and FTC-infants, but not detected in PEP-infants. The methicillin resistance gene (
On day 7 NPVP-infants had higher abundance of ARGs from four different ARG classes and PEP-infants higher abundance of ARGs from two other ARG classes (Table
Median abundance of antibiotic resistance genes among infants in each group.
Class A Beta lactamase | 0.61 | 4.2* | 0.00* | 0.00 | 0.00 | 0.00 | 0.080 | 0.586 | 1.43 | 1.0 | 0.00 | 0.443 | 1.327 | ||
Class C Beta lactamase | 0.00 | 0.00 | 0.20 | 0.126 | 0.229 | 0.98 | 0.22 | 0.00 | 0.492 | 0.812 | 9.1 | 12.7 | 9.5 | 0.605 | 1.134 |
Aminoglycoside acetyltransferase | 0.00 | 0.00 | 0.00 | 0.202 | 0.311 | – | – | – | – | – | – | – | – | – | – |
Aminoglycoside phosphotransferase | 0.00 | 0.00 | 0.00 | 0.590 | 0.653 | 0.00 | 0.16 | 0.00 | 0.114 | 0.497 | – | – | – | – | – |
Aminoglycoside nucleotidyltransferase | 0.00 | 0.00 | 0.00 | 0.765 | 0.765 | 0.00 | 0.00 | 0.00 | 0.296 | 0.426 | 0.00 | 0.00 | 0.00 | 0.584 | 0.814 |
Tetracycline efflux | 0.00 | 0.00* | 0.00 | 0.00 | 0.00 | 0.00 | 0.173 | 0.423 | 0.00 | 0.00 | 0.00 | 0.174 | 1.949 | ||
Tetracycline ribosomal protection | 0.00 | 0.26 | 4.4* | 0.118 | 0.52 | 3.7 | 1.77 | 0.397 | 0.615 | 6.4 | 23.4 | 23.4 | 0.407 | 1.041 | |
Quinolone resistance† | 9.0 | 21.6 | 5.3 | 0.062 | 0.138 | 9.81 | 7.6 | 0.77 | 0.133 | 0.470 | 9.2 | 9.4 | 7.1 | 0.501 | 1.186 |
Macrolide/MLS resistance | 0.00 | 0.00 | 0.00 | 0.757 | 0.797 | – | – | – | – | – | – | – | – | – | – |
ABC efflux pump† | 0.13 | 1.15 | 0.25 | 0.206 | 0.294 | 1.06 | 1.35 | 0.06* | 0.013 | 0.414 | 0.70 | 0.96 | 0.83 | 0.766 | 0.887 |
RND antibiotic efflux | 5.2 | 41.9* | 38.4 | 37.7 | 53.7 | 4.1 | 0.170 | 0.683 | 94.0 | 116.7 | 90.3 | 0.674 | 0.936 | ||
MFS antibiotic efflux | 1.16 | 113.3 | 29.0 | 0.339 | 0.342 | 85.8 | 119.1 | 16.0 | 0.056 | 0.489 | 105.2 | 119.5 | 84.7 | 0.614 | 0.839 |
Multidrug efflux pump activity | 0.00 | 24.6 | 1.92 | 0.337 | 0.449 | 20.9 | 21.7 | 4.9 | 0.346 | 0.478 | 10.0 | 14.0 | 8.1 | 0.616 | 1.552 |
Multidrug resistance efflux pump | 0.00 | 0.00 | 0.00 | 0.668 | 0.742 | 0.00 | 0.00 | 0.00 | 0.603 | 0.678 | 0.18 | 0.00 | 0.60 | 0.496 | 0.819 |
Gene modulating antibiotic efflux | 5.6 | 41.0** | 0.76 | 14.7 | 20.1 | 0.34 | 0.163 | 0.376 | 19.7 | 27.7 | 27.5 | 0.645 | 0.871 | ||
SMR antibiotic efflux | – | 1.2 | – | – | – | 0.00 | 0.00 | 0.00 | 0.914 | 0.932 | – | – | – | – | – |
Chloramphenicol acetyltransferase | 0.00 | 0.00 | 0.00 | 0.142 | – | – | – | – | – | – | – | – | – | – | |
Antibiotic target† | 0.48 | 0.00 | 0.00** | 0.00 | 0.00 | 0.00 | 0.266 | 0.396 | 0.00 | 0.00 | 0.00 | 0.720 | 0.768 | ||
Gene modulating resistance | 53.5 | 8.1** | 39.2 | 37.6 | 27.8 | 44.6 | 0.419 | 0.419 | 37.5 | 45.8 | 46.2 | 0.678 | 1.286 | ||
rRNA methyltransferase† | 0.00 | 10.6 | 10.6 | 0.128 | 0.213 | 6.0 | 8.8 | 1.72 | 0.008 | 0.464 | 4.1 | 5.4 | 4.4 | 0.665 | 0.887 |
Other ARG† | 5.3 | 16.7** | 2.02 | 7.3 | 8.4 | 0.26 | 0.132 | 0.413 | 7.2 | 10.5 | 6.3 | 0.613 |
On day 7 and at 4 months of age, different antibiotic exposure did not result in significant difference in total abundance of ARGs. However, on day 28, we detected significantly higher abundances of four classes of ARGs, including genes encoding beta-lactam and aminoglycoside resistance, in preterm infants exposed to broad-spectrum antibiotics compared to infants treated with narrow-spectrum regimens (Table
Influence of antibiotic exposure (broad vs. narrow spectrum regimen after first week of life) on abundance of antibiotic resistance genes (ARGs) in all preterm infants.
Class A Beta lactamase | 0.00 | 0.00 | 0.447 | 0.731 | 5.00 | 3.01 | 0.324 | 0.864 |
Class C Beta lactamase | 44.96 | 0.00 | 9.11 | 8.16 | 0.235 | 0.752 | ||
Aminoglycoside phosphotransferase | 6.14 | 0.00 | 0.078 | 0.281 | – | – | – | – |
Aminoglycoside nucleotidyltransferase | 0.93 | 0.00 | 0.00 | 0.00 | 0.794 | 0.851 | ||
Tetracycline efflux | 52.29 | 0.00 | 7.92 | 0.00 | 0.235 | 0.94 | ||
Tetracycline ribosomal protection | 5.97 | 0.00 | 0.210 | 0.540 | 11.68 | 2.17 | 0.393 | 0.886 |
Quinolone resistance | 29.75 | 9.43 | 0.298 | 0.671 | 9.40 | 8.34 | 0.357 | 0.816 |
ABC efflux pump | 3.23 | 1.07 | 0.392 | 0.784 | 0.70 | 0.64 | 0.471 | 0.814 |
RND antibiotic efflux | 312.10 | 37.73 | 0.875 | 0.875 | 94.00 | 84.96 | 0.393 | 0.63 |
MFS antibiotic efflux | 272.36 | 117.02 | 0.490 | 0.68 | 119.50 | 107.51 | 0.404 | 0.59 |
Multidrug efflux pump activity | 22.08 | 26.53 | 0.581 | 0.70 | 19.08 | 13.63 | 0.647 | 0.69 |
Multidrug resistance efflux pump | 0.00 | 0.00 | 0.162 | 0.486 | 3.02 | 0.00 | 0.017 | 0.272 |
Gene modulating antibiotic efflux | 75.30 | 15.53 | 0.490 | 0.73 | 19.65 | 20.86 | 0.393 | 0.63 |
SMR antibiotic efflux | 0.00 | 0.00 | 0.447 | 0.805 | – | – | – | – |
Antibiotic target | 1.70 | 0.00 | 2.36 | 0.00 | 0.096 | 0.512 | ||
Gene modulating resistance | 16.25 | 22.83 | 0.535 | 0.69 | 9.68 | 39.10 | 0.043 | 0.344 |
rRNA methyltransferase | 8.59 | 9.07 | 0.581 | 0.65 | 8.41 | 5.56 | 0.601 | 0.67 |
Other ARG | 24.40 | 12.15 | 0.680 | 0.72 | 7.21 | 7.36 | 0.601 | 0.74 |
Influence of antibiotic exposure (broad vs. narrow after first week of life) on abundance of antibiotic resistance genes (ARGs) in probiotic supplemented extremely preterm (PEP) infants.
Class A Beta lactamase | 0.00 | 0.00 | 0.799 | 0.846 | 1.43 | 3.01 | 0.596 | 0.867 |
Class C Beta lactamase | 45.96 | 0.00 | 0.009 | 0.162 | 9.11 | 9.52 | 0.328 | 0.875 |
Aminoglycoside phosphotransferase | 6.14 | 0.00 | 0.082 | 0.369 | – | – | – | – |
Aminoglycoside nucleotidyltransferase | 0.93 | 0.00 | 0.104 | 0.312 | 0.00 | 0.00 | 0.860 | |
Tetracycline efflux | 29.55 | 0.00 | 0.019 | 0.171 | 7.92 | 7.92 | 0.375 | 0.857 |
Tetracycline ribosomal protection | 6.49 | 0.00 | 0.082 | 0.369 | 11.68 | 28.48 | 0.246 | 0.787 |
Quinolone resistance | 29.75 | 7.08 | 0.506 | 0.828 | 9.40 | 9.40 | 0.425 | 0.85 |
ABC efflux pump | 3.23 | 0.43 | 0.279 | 0.628 | 0.70 | 1.10 | 0.479 | 0.852 |
RND antibiotic efflux | 312.10 | 19.81 | 0.799 | 0.900 | 94.00 | 93.09 | 0.536 | 0.858 |
MFS antibiotic efflux | 272.36 | 79.67 | 0.506 | 0.759 | 70.92 | 111.28 | 0.860 | 0.917 |
Multidrug efflux pump activity | 22.08 | 24.71 | 0.879 | 0.879 | 19.08 | 6.55 | 0.647 | 0.863 |
Multidrug resistance efflux pump | 0.00 | 0.00 | 0.234 | 0.602 | 3.02 | 3.02 | 0.069 | 0.368 |
Gene modulating antibiotic efflux | 75.30 | 13.81 | 0.328 | 0.656 | 19.65 | 24.88 | 0.008 | 0.128 |
SMR antibiotic efflux | 0.00 | 0.00 | 0.506 | 0.759 | – | – | – | – |
Antibiotic target | 1.70 | 0.00 | 0.064 | 0.030 | 2.36 | 0.00 | 0.151 | 0.604 |
Gene modulating resistance | 16.25 | 33.15 | 0.442 | 0.756 | 9.68 | 60.81 | 0.043 | 0.344 |
rRNA methyltransferase | 5.15 | 6.23 | 0.799 | 0.846 | 8.41 | 2.85 | 0.930 | 0.930 |
Other ARG | 24.40 | 7.31 | 0.506 | 0.700 | 7.21 | 7.21 | 0.724 | 0.891 |
The main aim of this explorative, observational multi-center study was to obtain in-depth knowledge on how probiotics and antibiotic therapy influenced the developing gut microbiota and antibiotic resistome of preterm infants. Previous studies have shown that the gut microbiota in preterm infants differs from term infants with limited diversity and delayed acquisition of a stable profile (
Bifidobacteria strongly dominated the gut microbiota in extremely preterm infants only few days after commencing probiotic supplementation, in sharp contrast to very preterm infants not receiving probiotics who predominantly had
There is no consensus on the optimal dose of probiotics. One study from India compared standard and high-dose probiotic regimens and found no difference in proportion of infants colonized or quantitative colonization rates with probiotic species (
A lower relative abundance of
In line with others, we found that the gut antibiotic resistome of preterm and term infants is established early, independent of antibiotic exposure (
At the time of this study, probiotic supplementation to extremely preterm infants was considered “standard of care” in Norway. We were therefore beyond equipoise to perform a randomized study comparing probiotic to no probiotic supplementation in this population. The NPVP-infant group has limitations as a control group due to maturational differences and the difference in antibiotic exposure compared to the PEP-infants. However, more antibiotic exposure in the PEP-infants would most likely have led to less diversity and higher abundance of ARGs. Still, we found few differences between the two preterm groups at 28 days and 4 months of age, suggesting a protective effect of probiotics in the PEP-infant group. The gut microbiota composition of preterm infants may differ between hospitals (
Probiotic-supplemented extremely preterm (PEP) infants had a high relative abundance of
The raw data supporting the conclusion of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.
EE organized all phases of the study, analyzed data, wrote the first version of the manuscript, and revised the manuscript. She had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. TP, JA, SR, RS, and BN were responsible for inclusion of patients at participating centers, data retrieval, and revised the manuscript. JC, EH, and NW took part in study design, were responsible for microbiological (JC) and bioinformatic (EH, NW) analyses and revised the manuscript. CK conceptualized and designed the study, directed all phases of the study, and revised the final manuscript. He had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We thank the parents of the infants participating in this study and the nurses at the participating centers for helping collect fecal samples.
Antibiotic resistance genes
Comprehensive antibiotic resistance database
Colony forming units
False discovery rate
Full-term control
Necrotizing enterocolitis
Neonatal intensive care unit
Non-metrical multidimensional scaling
Non-probiotic very preterm
Probiotic extremely preterm.