Multidrug-resistant (MDR) bacterial infections cause significant

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1 crossmark Use of Shotgun Metagenome Sequencing To Detect Fecal Colonization with Multidrug-Resistant Bacteria in Children Heidi Andersen, a Natalia Connolly, b Hansraj Bangar, a Mary Staat, a Joel Mortensen, c Barbara Deburger, c David B. Haslam a Departments of Pediatric Infectious Diseases, a Biomedical Informatics, b and Pathology and Laboratory Medicine, c Cincinnati Children s Hospital Medical Center, Cincinnati, Ohio, USA Prevention of multidrug-resistant (MDR) bacterial infections relies on accurate detection of these organisms. We investigated shotgun metagenome sequencing for the detection of methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), and MDR Enterobacteriaceae. Fecal metagenomes were analyzed from high-risk inpatients and compared to those of low-risk outpatients and controls with minimal risk for a MDR bacterial infection. Principal-component analysis clustered patient samples into distinct cohorts, confirming that the microbiome composition was significantly different between cohorts (P 0.006). Microbial diversity and relative anaerobe abundance were preserved in outpatients compared to those in controls. Relative anaerobe abundance was significantly reduced in inpatients compared to that in outpatients (P 0.006). Although the potential for MDR bacteria was increased in inpatients and outpatients compared to that in controls (P < 0.001), there was no difference between inpatients and outpatients. However, 9 (53%) inpatients had colonization with a MDR bacterium that was not identified by culture. Unlike culture, shotgun sequencing quantitatively characterizes the burdens of multiple MDR bacteria relative to all of the microbiota within the intestinal community. We propose consideration of key microbiome features, such as diversity and relative anaerobe abundance, in addition to the detection of MDR bacteria by shotgun metagenome sequencing as a novel method that might better identify patients who are at increased risk of a MDR infection. Multidrug-resistant (MDR) bacterial infections cause significant morbidity and mortality and are increasingly common in children (1 3). Clinically important MDR bacteria include methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), and MDR Enterobacteriaceae with AmpC beta-lactamase resistance (AmpC), extended-spectrum beta-lactamase (ESBL) resistance, or carbapenem-resistant betalactamases (CRE). Antimicrobial exposures reduce the commensal gut anaerobes with compensatory increased Enterobacteriaceae and Enterococcus abundances as well as bacteriophages, allowing transmission of antibiotic resistance genes between species (4). This results in the loss of host resistance to abnormal fecal colonization and may select for MDR bacteria (5 7). Subsequently, the intestines serve as the primary reservoir of MDR bacteria (8, 9). Increased abundances of these organisms within the intestines precede the onset of an invasive bloodstream infection (BSI), and the risk of infection is related to the density of colonization (10 13). Timely and accurate detection of MDR bacteria is crucial to prevent the spread of MDR bacteria and to decrease rates of MDR infections (14). There is an urgent need for the development of new diagnostic methods, including metagenomics, to combat rising antibiotic resistance rates, which was explicitly stated in recent national action plans by the CDC and the White House (15, 16). Despite the advent of PCR and now metagenomics, culture remains the gold standard for clinical fecal screening of MDR bacteria. Performing fecal screening cultures is cumbersome, with inevitable breakthrough growth of unintended isolates on selective media, and has suboptimal sensitivity and specificity for the prediction of invasive infections (10, 17 19). A recent study demonstrated that even with isolate identification and antibiotic susceptibility testing, posttransplant VRE BSIs in 20% of patients were not detected, and 25% of patients without a posttransplant VRE BSI had a positive fecal screening culture (10). This suggests that other important factors beyond growth on culture must be considered to more accurately identify patients at risk for developing an invasive MDR bacterial infection. Similar to screening cultures, new rapid diagnostic tests for the detection of antibiotic resistance genes are qualitative without providing any information on vast species diversity, relative MDR bacterial abundance, or abundance of commensal anaerobes normally making up 99% of the microbiome that prevent abnormal intestinal colonization (6, 7). Therefore, it is not surprising that fecal screening cultures used to detect MDR bacteria, including VRE, are unable to accurately predict patients who develop invasive infections (10, 20). In comparison to culture, fecal metagenomics has the potential to improve clinical outcomes. Metagenome analysis is a deep sequencing-based methodology used broadly to investigate the role of microbiota from crude specimens in various disease states. However, few studies have evaluated its role in the detection of fecal colonization with MDR bacteria or changes in the gut microbiota preceding an invasive infection. Use of fecal 16S rrna meta- Received 29 September 2015 Returned for modification 27 October 2015 Accepted 13 April 2016 Accepted manuscript posted online 27 April 2016 Citation Andersen H, Connolly N, Bangar H, Staat M, Mortensen J, Deburger B, Haslam DB Use of shotgun metagenome sequencing to detect fecal colonization with multidrug-resistant bacteria in children. J Clin Microbiol 54: doi: /jcm Editor: C.-A. D. Burnham, Washington University School of Medicine Address correspondence to Heidi Andersen, heidi.andersen@cchmc.org, or David B. Haslam, david.haslam@cchmc.org. Supplemental material for this article may be found at /JCM Copyright 2016, American Society for Microbiology. All Rights Reserved jcm.asm.org Journal of Clinical Microbiology July 2016 Volume 54 Number 7

2 Metagenomic Detection of MDR Bacteria genome sequencing in stem cell transplant (SCT) patients found that reduced gut microbiome diversity was associated with decreased survival posttransplantation (21). SCT patients that had intestinal domination with Proteobacteria had a 5-fold increased risk of a subsequent Gram-negative BSI. Further, Enterococcus intestinal domination was associated with a 9-fold increased risk of a VRE BSI and was detected in 89% of patients who developed a VRE BSI compared to only 67% of patients identified using culture (13). These findings demonstrate that fecal metagenome analysis has greater sensitivity to predict patients at risk for an invasive MDR bacterial infection than culture, even in the absence of performing additional tests to validate the viability or infectivity of the organisms. Furthermore, the cost of metagenome sequencing has fallen precipitously, and this advancing technology is capable of a similar turnaround time, within 72 h, compared to that of culture. To date, metagenome studies have relied on 16S rrna gene sequencing, which uses amplified primers that recognize the bacterial 16S ribosomal gene. This method only allows for the mapping of sequences at the genus level, rendering it unable to distinguish clinically important bacterial species like Enterococcus faecium or Enterococcus faecalis from other Enterococcus spp. that less commonly cause infection. Unlike 16S rrna gene sequencing, shotgun metagenomics samples the DNA randomly from a fecal extract, allowing for quantification of bacterial, fungal, parasitic, and viral genomes as well as antibiotic resistance genes. By investigating the relative abundances of clinically important bacterial species and the antibiotic resistance genes of numerous MDR bacteria with other key characteristics of the fecal metagenome, we hypothesize that fecal shotgun metagenome sequencing will more accurately reflect the relative MDR bacterial burden of colonization in the intestinal community for differentiation of patients with various risk levels for invasive MDR infection. MATERIALS AND METHODS Study design. This is a proof-of-principle pilot study to develop a novel methodology for quantitation of clinically important bacterial species and antibiotic resistance genes from fecal samples in lieu of screening cultures. Samples from patients with presumed high and low risk for MDR bacteria were selected at a ratio of 1:2 with controls for a sample size of 90, which can achieve a power of 99% with deep sequencing in this study at a type 1 error rate of 0.03 for metagenome analysis using the HMP package in R with corrections using the Bonferroni method (22 24). As all patient samples were deidentified and no clinical information was obtained in association with any sample, institutional board review and informed consent were not required. Specimens. (i) High-risk inpatients. Inpatient samples were discarded, deidentified crude stool samples collected from hospitalized patients aged 7 months to 31 years (median of 9 years old with an interquartile range of 5 to 14 years old) who had a clinical fecal VRE screening culture. Stools were stored in the Diagnostic Infectious Diseases Laboratory at Cincinnati Children s Hospital Medical Center (CCHMC) at 4 C and were obtained within 24 to 48 h of collection. At CCHMC, more than 96% of all clinical VRE screening cultures are from SCT or hematology/ oncology patients, where routine testing is only performed in hospitalized SCT patients for isolation purposes. Phenotypic testing was done at the time of specimen obtainment, and samples were stored at 80 C for DNA extraction. (ii) Low-risk outpatients. Outpatient samples were discarded, deidentified previously collected and frozen stool specimens from children aged 14 days to 11 years presenting at CCHMC for outpatient well-child visits. These specimens were originally obtained for healthy controls in epidemiological gastroenteritis research and were stored at 80 C (25). None of the children had gastrointestinal illness for 2 weeks or respiratory illness for 3 days prior to specimen collection, and none were treated with antibiotics at the time of stool collection. However, prior antibiotic exposures were unknown. (iii) Minimal-risk controls. Publicly available fecal metagenomes obtained by the shotgun metagenome sequencing of DNA extracted from stool specimens for the Human Microbiome Project (HMP) were used as controls (downloaded from the HMP website at /HMASM/) (26). HMP participants were healthy 18- to 40-year-old adults without any underlying diseases from Houston or St. Louis who had no probiotic or antibiotic use for at least 6 months prior to stool collection. In addition, newly published, publicly available pediatric shotgun fecal metagenome sequences were also included as controls (downloaded from SRA252126)(27, 28). These were obtained from healthy children 7 to 20 years old (median of 14 years old) without antibiotic exposures for at least 6 months prior to specimen collection. Although pediatric and adult controls with presumed minimal risk for a MDR bacterial infection were obtained from different laboratories, all of these samples were obtained within 24 h of collection, stored at 80 C, underwent DNA extraction using the PowerSoil kit by MO BIO, and sequenced by the HiSeq Illuminex. Screening cultures. Inpatient stool samples weighing 0.10 g were diluted 1:10 with sterile water followed by homogenization using a pestle and vortex. One hundred microliters of each homogenized sample was used to inoculate chromid VRE, chromid MRSA, and VACC media (to isolate ESBLs and CRE). No other prepared culture medium for the isolation of CRE was commercially available at the time of this study. Mac- Conkey agar supplemented with 50 g/ml of cefoxitin was used to isolate AmpC-resistant Enterobacteriaceae (29). Cultures were incubated aerobically at 37 C and evaluated at 48 h. Isolates were identified using matrixassisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) with antibiotic susceptibilities by the Vitek 2 for determination of true positives, false positives, and false negatives. Additional information is detailed in the supplemental material (see supplemental file S1). Shotgun metagenome sequencing. DNA extraction was performed using 0.25 g of a stool sample with the PowerFecal DNA isolation kit by MO BIO per manufacturer recommendations. DNA concentration was measured using NanoDrop. Amplified library generation was performed with Nextera XT adapters, and sequencing was performed to obtain 125-bp DNA paired end reads to a depth of 2.5 G base pairs per sample using the HiSeq Illuminex 2500 in the Core Research Laboratory at CCHMC. This closely mirrored the conditions used for the adult and pediatric controls, and the same annotation pipeline was used for all fecal metagenomes in this study. Further details can be found in the supplemental material along with the computer code (see supplemental file S1) and a list of the species and antibiotic resistance genes (see supplemental file S2). Data analysis. In order to establish that shotgun metagenome sequencing was capable of distinguishing our three a priori patient cohorts, we performed principal-component analysis (PCA) with Euclidean distances and heatmaps to cluster samples by their microbiome and resistome metagenome data using the ade4 and gplots packages in R (30, 31). A machine learning algorithm, random forest, was used to identify key species (antibiotic resistance genes) to better distinguish the three patient cohorts using the randomforest and varselrf packages. Comparison between all groups used a multipermutation response (MRPP) test from the vegan package in R (32, 33). Species and antibiotic resistance gene diversities were calculated using the Shannon index with the vegan package in R. Relative abundances of all species (antibiotic resistance genes) in the microbiome (resistome) were considered. A single species with a relative abundance of 30% of the total mapped reads was considered intestinal domination, in contrast to the genera level as previously described (13). July 2016 Volume 54 Number 7 Journal of Clinical Microbiology jcm.asm.org 1805

3 Andersen et al. TABLE 1 Qualitative fecal screening cultures for detection of MDR bacteria in high-risk inpatients requires additional labor-intensive evaluation to minimize false-positive results Qualitative culture result Positive visual appearance No. (%) of inpatients with screening culture result MRSA VRE AmpC ESBL 9 (53) 9 (53) 8 (47) 11 (65) True positive 2 (12) 2 (12) 3 (18) 0 (0) False positive 7 (41) 7 (41) 5 (29) 11 (65) Negative visual 4 (24) 4 (24) NA a NA appearance No growth 4 (24) 4 (24) 9 (53) 6 (35) a NA, not applicable. Reductions of the microbiome and resistome were performed using clinically relevant categories for identification of potentially important characteristics associated with abnormal colonization of the gut following antibiotic exposures as described in the literature. MDR bacterium potential was defined by the presence of at least one species of interest and at least one corresponding antibiotic resistance gene with a Z-score of 5.0. Calculation of all Z-scores used the mean and standard deviation of the abundances for all individual reads in the controls. Specifically, S. aureus and meca determined MRSA potential. Enterococcus faecium, Enterococcus faecalis, and vana were used for VRE potential. Enterobacteriaceae spp. and either the AmpC, ESBL, or CRE genes were used for AmpC, ESBL, and CRE potential, respectively. The Lahey clinical database was used for classification of beta-lactamases according to their previously described type of resistance (34, 35). The species and antibiotic resistance genes used for detection of MDR bacterium potential are included in the supplemental material (see supplemental file S3). All statistical analyses were corrected for multiple testing using the Bonferroni method, and additional details of statistical methods are detailed in the supplemental material (see supplemental file S1). RESULTS Fecal screening culture results. Of the 21 inpatient stool samples obtained, 1 was excluded due to insufficient quantity. The remaining 20 had fecal screening cultures performed. There were 5 (25%) inpatient samples that grew at least one MDR bacterium, including 2 with two different MDR bacteria. On average, 16 (80%) samples had growth of an isolate other than that intended on 2 of the 4 screening cultures. False-positive isolates were recovered from all types of screening media with a false-positive rate of 29% to 65% (Table 1). The two samples with known negative clinical VRE screening cultures had negative qualitative results in this study. There were 3 samples without growth on culture, and 2 only grew yeast. A total of 20 samples with phenotypic testing underwent DNA extraction. Insufficient DNA concentrations from 3 samples prohibited metagenome sequencing. Of these 3 samples, 1 had no growth on culture, 1 only grew Candida dubliniensis, and 1 grew vancomycin-susceptible E. faecalis on the MRSA culture and vancomycin-susceptible Enterococcus raffinosus/enterococcus avium on the MRSA-, VRE-, and ESBL-selective media. These 3 samples were excluded from further analysis. Fecal metagenome results. A total of 17 inpatient and 11 outpatient samples had shotgun metagenome sequencing performed. In addition, 30 adult and 25 pediatric fecal metagenomes were included as controls using the same analysis pipeline in conjunction with the other sequences, giving an average of 31,273,436 total reads (3.9 G base pairs) per sample. This sample size provided a type I error rate of 0.01 after corrections for multiple testing with a power of 99% for metagenome analysis. All Staphylococcus, Enterococcus, and Enterobacteriaceae spp. in our database were detected in at least one sample. A total of 10 methicillin, 7 vancomycin (plus 33 vancomycin regulatory resistance genes), 44 AmpC, 115 ESBL, and 29 CRE resistance genes were detected in at least one sample. Comparison of fecal metagenomes between cohorts. Between-class principal-component analysis of the microbiome and resistome distinguished our three groups (Fig. 1). The two plots demonstrate little overlap between samples from different cohorts, indicating distinct species and antibiotic resistance gene compositions between all groups. Further, the microbiome composition was significantly different between all groups as indicated FIG 1 Principal-component analysis (PCA) of the microbiome (a) and resistome (b). Species and antibiotic resistance gene compositions of all samples were clustered using the Euclidean distances for the abundance of each species (antibiotic resistance gene) between samples. The dots at the end of each radial vector represent the spatial orientation of each patient sample, with the distance between dots representing the maximum variance between samples. The error sum of squares is the center (centroid) of each cluster. Species composition was statistically unique between all three patient cohorts by MRPP (P 0.006; A 0.077). There was no significant difference in the resistome compositions between groups (P 0.126; A 0.012) jcm.asm.org Journal of Clinical Microbiology July 2016 Volume 54 Number 7

4 Metagenomic Detection of MDR Bacteria FIG 2 Box and whisker plot of microbiome diversity (a) and resistome diversity (b) by Shannon Index using the median and interquartile ranges for each cohort. There was significantly reduced microbial diversity in high-risk inpatients compared to that in controls (P 0.005) and a nonsignificant decreased microbial diversity in high-risk inpatients compared to that in low-risk outpatients (P 0.163). Preservation of microbial diversity was clearly demonstrated in low-risk outpatients compared to that in controls (P 0.631). However, resistome diversity was not significantly different between groups (P 0.110). by MRPP (P 0.006; effect size [A] 0.077). However, there was no significant difference in resistome compositions between groups after multiple testing corrections (P 0.126; A 0.012). Heatmaps can be found in the supplemental material (see Fig. S4). Finally, there was no difference in the proportion of mapped reads to total reads between cohorts (see Fig. S5 in the supplemental material). To optimize the accuracy of clustering samples into their a priori groups, we used random forest, a supervised machine learning algorithm, to identify species (antibiotic resistance genes) with the greatest dissimilarity between groups. When all 2,680 species were included, random forest differentiated the groups with 88% accuracy. However, feature selection identified 79 key species that classified samples into their respective cohorts with 95% accuracy. Comparing the abundances of only the key species identified by feature selection retained significant differences between all groups (P 0.004; A 0.107) (see Fig. S6a in the supplemental material). Similarly, antibiotic gene composition subjected to random forest-based feature selection resulted in 60 genes that best distinguished patient cohorts. The abundances of these key antibiotic resistance genes were not significantly different between all groups (P 0.072; A 0.043) (see Fig. S6b in the supplemental material). The key species and antibiotic genes identified by feature selection can be found in the supplemental material (see supplemental file S7). We then investigated the microbiome and resistome diversities of the groups. Microbiome diversity was statistically different between all groups (P 0.024), with a nonsignificant decrease in inpatients compared to outpatients (P 0.163) (Fig. 2a). As expected, inpatients had significantly decreased microbiome diversity compared to that of controls (P 0.005), and there was no difference between outpatients and controls (P 0.631). However, resistome diversity was not significantly different between groups (P 0.110) (Fig. 2b). Alternatively, we examined the differences in fecal metagenomes more broadly between cohorts by classifying each microbial species (antibiotic resistance gene) into a clinically relevant category. The relative abundances of these categories were compared between cohorts. Statistical significance between all groups was retained when species were classified into clinically relevant categories (P 0.006; A 0.319) (Fig. 3a). The most striking difference between cohorts was relative anaerobe abundance (P 0.001). The median relative anaerobe abundance was markedly reduced to 25% in the microbiome of inpatients from 92% in outpatients (P 0.006) and 97% in controls (P 0.001), while there was no difference in the median relative anaerobe abundance of outpatients compared to that of controls (P 0.190). Similarly, the relative abundances of antibiotic resistance genes using clinically relevant categories were significantly different between all groups (P 0.048; A 0.275) (Fig. 3b). Traditional comparison of the relative genera abundances also differentiated patient cohorts by linear discriminant analysis (LDA) (see supplemental file S8). In summary, these three a priori patient cohorts had the distinct biosignatures of the microbial species abundances detectable by fecal screening using shotgun metagenome sequencing, regardless of whether we compared them at the detailed species level, genera level, or a broader level of comparison using clinically relevant categories. Intestinal domination. Other investigators have described an association between intestinal domination and the risk of a subsequent BSI using 16S rrna metagenome sequencing (13). Using species-level shotgun metagenomics, there were 11 (65%) inpatient, 3 (27%) outpatient, and 18 (33%) control samples with intestinal domination, but the dominant species varied remarkably between groups (Table 2). We found that intestinal domination in inpatients was largely due to species other than anaerobes (91%) compared to predominantly anaerobes in outpatients (67%) and controls (100%). Detection of MDR bacterium potential by culture and metagenomics. We detected 9 (53%) inpatient samples with MDR bacterium potential by metagenomics compared to 8 (73%) outpatient samples and no control samples (Table 3). Although the overall proportion of MDR potential was significantly different July 2016 Volume 54 Number 7 Journal of Clinical Microbiology jcm.asm.org 1807

5 Andersen et al. FIG 3 Bubble graphs showing relative changes in the relative abundances of all species in the microbiome (a) and all antibiotic resistance genes in the resistome (b) after classification into clinically relevant categories between cohorts. There were significant differences between all groups after the classification of species (P 0.006; A 0.319) and antibiotic resistance genes (P 0.048; A 0.275) into categories by MRPP. Relative anaerobe abundance was statistically different between all groups (P 0.001). High-risk inpatients had significantly reduced anaerobe abundance compared to outpatients (P 0.006), but there was no difference between low-risk outpatients and controls (P 0.190). between all groups (P 0.001), there was no significant difference between inpatients and outpatients (P 0.549). Cocolonization with MDR potential was detected by culture in 3 (18%) inpatients compared to 5 (29%) inpatients using metagenomics, which also detected MDR potential in 6 (55%) outpatients. More than 2 MDR bacterium potentials were detected in 4 (24%) inpatient samples and 2 (18%) outpatients using metagenomics, including 2 (12%) inpatients with more than 3 different MDR potentials. The Z-scores of both species and antibiotic resistance gene abundances used for the detection of MDR potentials are summarized in the supplemental material (see supplemental file S9). Overall, detection of MDR bacterium potential by culture and metagenomics in inpatient samples was not statistically different between groups given our small sample size (see supplemental file S10 in the supplemental material). However, the Cohen kappa coefficient was with a 95% confidence interval of to and with 79% observed agreement compared to 62% agreement by chance, which indicated that there was only fair agreement in the detection of MDR bacteria using these two methodologies. There were 3 inpatient samples with 4 MDR bacteria detected by culture (5.9% of all screening cultures) without detection of MDR potential by metagenomics. However, 9 inpatient samples detected with 14 MDR potentials (16.5% of all MDR bacterium potentials) by metagenomics were not detected by culture (see supplemental file S10). MRSA potential. Culture-based screening isolated MRSA from 2 (12%) inpatient samples while another 9 (53%) samples grew false-positive isolates. MRSA potential by metagenomics was not detected in any samples, as none had increased S. aureus abundance (Fig. 4a). Overall, increased meca abundance was found in 4 (24%) inpatient samples, 1 (9%) outpatient sample, and no control samples. We considered the two samples that grew MRSA on culture as true positives by culture and false negatives by metagenomics. Each of these samples had 1% relative abundances of S. aureus and meca, including 1 sample lacking detection of meca and yielding growth of only a single colony. Whole-genome sequencing (WGS) of this isolate confirmed that it was S. aureus and harbored an intact meca gene. VRE potential. Fecal VRE screening culture isolated vancomycin-resistant E. faecium from 2 inpatient samples while 7 (41%) grew false-positive isolates. In addition, Enterococcus hirae and E. avium grew in 2 (12%) VRE screening cultures, but both were false positives with susceptibility to vancomycin. Neither of the 2 samples included with a known negative clinical VRE screening culture grew VRE in this study. By metagenomics, VRE potential was detected in 2 (12%) inpatients but no outpatients or controls (Fig. 4b). Each inpatient sample had increased vana abundance with a Z-score of 5.0, but only 1 grew a VRE isolate. Additionally, 2 other inpatient samples and 1 outpatient sample without VRE potential had increased E. faecium abundances with Z-scores of 5.0, including the inpatient with E. faecium comprising 98% of its microbiome with 96% of reads mapped by Kraken. No outpatients or controls had an increased vana abundance, which was found in 2 other inpatients without detection of VRE potential. AmpC potential. Cefoxitin screening cultures isolated cefoxitin-resistant Enterobacter cloacae/enterobacter asburiae from 3 in jcm.asm.org Journal of Clinical Microbiology July 2016 Volume 54 Number 7

6 Metagenomic Detection of MDR Bacteria TABLE 2 Intestinal domination comparing the species identified with 30% relative abundance in the microbiome between cohorts No. (%) of indicated group with intestinal domination Species Inpatients Outpatients Pediatric controls Enterococcus 3 (18) Enterococcus faecium 1 (6) Enterococcus hirae 2 (12) Enterobacteriaceae 0 1 (9) 0 0 Klebsiella pneumoniae Other Aerobes 1 (6) Actinomyces odontolyticus Fungi 2 (12) Candida glabrata 1 (6) Candida krusei 1 (6) Streptococcus 1 (6) Streptococcus thermophiles Lactobacillus 3 (18) Lactobacillus rhamnosus 2 (12) Lactobacillus planterum 1 (6) Adult controls Anaerobes 1 (6) 2 (18) 7 (28) 11 (37) Bacteroides vulgatus (24) 9 (30) Bacteroides fragilis 0 1 (9) 0 0 Eubacterium rectale (4) 1 (3) Faecalibacterium (3) prausnitzii Bifidobacterium breve 0 1 (9) 0 0 Alistipes finegoldii 1 (6) Alistipes shahii 1 (6) patient samples, including 2 of the 4 (29%) inpatients detected by metagenomics with AmpC potential (Fig. 4c). Of the inpatient samples with AmpC potential, 1 sample had Z-scores of 5.0 for 12 Enterobacteriaceae spp. (including E. cloacae, Enterobacter aerogenes, and Enterobacter sp. 638) and 2 act genes. Overall, there were 9 AmpC resistance genes with increased abundances among inpatient samples with AmpC potential, including act and cmy genes. All 6 (55%) outpatients detected with AmpC potential had increased abundances of a Klebsiella sp. with Z-scores of 5.0, including a single outpatient with increased abundances of E. cloacae, E. asburiae, and E. aerogenes. There were 3 different types of AmpC resistance genes found in outpatient samples with AmpC potential, including act, cmy, and mir genes. One inpatient had an increased AmpC gene abundance without an increased abundance of at least one Enterobacteriaceae spp., and this was also observed in 4 adult controls with act, cmy, and mir genes. ESBL potential. Only 1 inpatient sample was considered to be a true positive with growth of third-generation cephalosporinresistant Enterobacteriaceae spp. on the ESBL screening culture (these included Escherichia coli and Citrobacter freundii). By metagenomic analysis, this sample was considered to be a true positive for ESBL potential with increased abundances of 4 Enterobacteriaceae spp. (including C. freundii) and a single ESBL resistance gene TABLE 3 Detection of fecal colonization by culture and shotgun metagenome sequencing in high-risk inpatients had only fair agreement between these two methodologies a MDR bacterium potential No. (%) of inpatients (n 17) with positive result by screening culture No. (%) of indicated group with positive result by shotgun metagenome sequencing b Inpatients (n 17) Outpatients (n 11) Controls (n 30) MRSA 2 (12) VRE 2 (12) 2 (12) 0 0 AmpC 3 (18) 4 (24) 6 (55) 0 ESBL 1 (6) 8 (47) 7 (64) 0 CRE 0 4 (24) 3 (27) 0 a Detection of fecal colonization with MDR bacterium potential by shotgun metagenome sequencing was significantly different between all groups (P 0.001). Increased MDR bacterium potential was indistinguishable between high-risk inpatients and low-risk outpatients (P 0.549). b Z-score 5.0. with a Z-score 5.0. Metagenome screening detected another 7 (41%) culture-negative inpatient samples with ESBL potential, which was likewise detected in 7 (64%) outpatients and no controls (Fig. 4d). There were 2 samples (1 inpatient and 1 outpatient) detected with ESBL potential that had increased abundances of the ctx-m gene. Interestingly, the majorities of inpatient and outpatient samples with ESBL potential exclusively had either increased abundances of shv (37.5% and 57%) or tem (37.5% and 0%) genes, respectively. Only 2 (18%) outpatients were found with increased abundances of more than one type of ESBL gene. There was 1 (6%) inpatient and 6 (11%) controls (3 pediatric and 3 adult), each with a single ESBL gene with a Z-score of 5.0 without an increased abundance of at least one Enterobacteriaceae sp. CRE potential. No screening cultures isolated a carbapenemresistant isolate. CRE potential was detected in 4 (24%) inpatient, 3 (27%) outpatient, and no control samples (Fig. 4e). The most common types of CRE genes with increased abundances were the oxa genes in inpatients and the ges genes in outpatients. Although 1 inpatient and 10 control (3 pediatric and 7 adult) samples had increased abundances of a CRE gene without increased abundances of an Enterobacteriaceae sp., this was not observed in any outpatients. DISCUSSION We described a proof-of-principle study comparing fecal screening cultures and shotgun metagenome sequencing for the detection of MDR bacteria. In agreement with prior studies, we found that the use of fecal screening cultures was cumbersome and required additional testing given the growth of isolates that were not intended to be selected on all types of screening media. We cannot exclude the possibility that the use of a fecal dilution for the inoculation of screening cultures may have resulted in decreased sensitivity. In comparison, fecal shotgun metagenome screening was performed as a single diagnostic test with precise and clear quantitative detection of clinically important species and antibiotic resistance genes of multiple MDR bacteria relative to the abundances of all other species and antibiotic resistance genes within the gut microbiota. We found MDR colonization in 25% of highrisk inpatients by culture compared to 53% identified by shotgun metagenomics. Detection of MDR colonization by shotgun metagenomics July 2016 Volume 54 Number 7 Journal of Clinical Microbiology jcm.asm.org 1809

7 Andersen et al. Downloaded from FIG 4 Radar graphs comparing the abundances of clinically important species and antibiotic resistance genes for the detection of MDR bacterium potential in high-risk inpatients (red shaded upper left quadrant) and low-risk outpatients (blue shaded upper right quadrant) relative to the abundances in controls (off white shaded lower part of circle) for MRSA (a), VRE (b), AmpC (c), ESBL (d), and CRE (e). The Z-scores for all relevant species and antibiotic resistance genes are plotted on the vertical axes individually in color curated categories for broad visualization. Each stool sample is represented by the radial lines. The abundances of the species and antibiotic resistance genes for each sample relative to controls is depicted by the distance the colored line extends from the core. The central core (dark gray) represents the abundances in controls within 5 standard deviations (Z-score 5.0). Samples with a relative abundance of at least one relevant species and antibiotic resistance gene with a Z-score of 5.0 were considered positive for MDR bacterium potential by shotgun metagenome sequencing. on March 18, 2019 by guest alone was not statistically different between high-risk inpatients with clinical VRE screening and healthy outpatient children at CCHMC in this pilot study. We were surprised to find a relatively high prevalence of clinically important bacterial species and antibiotic resistance genes in the stools of outpatient children, despite no antibiotic therapy at the time of stool collection. For example, 73% of outpatient children showed detection of MDR bacterium potential compared to none of the controls. Recent literature suggests that fecal colonization with MDR bacteria is not uncommon, even in otherwise healthy children in the community setting (36, 37). However, it is clear from this analysis with pediatric controls that healthy children without antibiotic exposure for at least 6 months have minimal colonization with bacterial species and antibiotic resistance genes of multiple clinically important MDR 1810 jcm.asm.org Journal of Clinical Microbiology July 2016 Volume 54 Number 7

8 Metagenomic Detection of MDR Bacteria bacteria. Our findings likely reflect true colonization with MDR bacteria in these healthy outpatient children, which we speculate is the result of prior antibiotic exposures. However, healthy children in the community setting rarely develop invasive MDR infections. This implies that additional characteristics beyond the detection of MDR bacteria need to be considered for diagnostic clinical screening tests to predict patients at risk of developing a MDR infection. Using shotgun metagenome sequencing, we were able to clearly demonstrate differences in the microbiome and resistome compositions of these three patient cohorts. The ability to discern species with unique variances between groups using random forest allowed for the clustering of samples into their a priori cohort with an accuracy of 95%. This proof-of-principle approach to classification of clustered samples was based on perceived risk groups, as our data lacked clinical information associated with individual samples, such as antibiotic exposure and history of infection. In the future, we propose to use the relative species and antibiotic resistance gene abundances within the intestinal microbiota to predict patients who will develop invasive MDR infections. Preservation of microbial diversity was demonstrated in the healthy children and adults in this pilot study. High-risk inpatients were characterized by a nonsignificant decrease in microbial diversity compared to healthy outpatient children, but there was significantly decreased microbial diversity in high-risk inpatients compared to healthy pediatric and adult controls. Significant loss of potentially protective anaerobes within the gut microbiome was clearly demonstrated in high-risk inpatients relative to that of healthy children and adults. We suspect that these findings reflect changes in the intestinal community that are associated with antibiotic exposures in these high-risk patients. We believe that these two factors contribute to the risk for MDR transmission and invasive infection. We found relatively high Enterobacteriaceae spp. abundances in our healthy outpatient children compared to those in healthy pediatric and adult controls, which may be explained in part by differences in age. An abundance of Enterobacteriaceae spp. is increased at the time of birth and then decreases with a compensatory increase in anaerobes equal to those levels found in healthy adults by the time most children are 3 years of age (38). However, we found that the median relative anaerobe abundance was only 25% (interquartile range of 6% to 83%) in high-risk inpatients, which is well below the median relative anaerobe abundance of 92% (interquartile range of 89% to 93%) observed in healthy outpatient children. It seems implausible that differences in age alone can explain the variation in relative anaerobe abundances between these groups. Therefore, we conclude that relative anaerobe abundance likely has significant clinical importance that can help to determine risk for an invasive infection. In future studies, we will use age matching between cohorts to control for this potential confounding factor. Comparison of culture and shotgun metagenomics to detect MDR bacteria found VRE potential in 1 (6%), AmpC potential in 2 (12%), ESBL potential in 7 (41%), and CRE potential in 4 (24%) of our hospitalized patients with clinical concern for colonization with MDR bacteria that were not detected by culture. Many prior studies have relied on the use of culture as the gold standard to determine the sensitivities of other clinical microbiology diagnostic tests. We would caution against the use of culture methodologies as the gold standard to evaluate advanced molecular methods such as next-generation sequencing, especially for calculation of sensitivity and specificity (39). There is likely a threshold for the detection of species and antibiotic resistance genes using shotgun fecal metagenome sequencing, which is in part related to the depth of sequencing performed. Despite obtaining 20 million reads per sample in this pilot study, two inpatient samples had positive MRSA screening cultures without detection of increased S. aureus or meca abundance, including one without detection of meca from the stool. Wholegenome sequencing confirmed that this isolate was S. aureus with an intact meca gene. The clinical significance of intestinal MDR colonization at very low density is unknown, but studies using 16S sequencing have suggested that risk of an invasive infection is related to abundance (13). It is unlikely that the minimal abundance found in this sample would pose an increased risk for an invasive MRSA infection. Using fecal shotgun metagenome screening for the detection of ESBL potential, the sample with the greatest ESBL gene and Enterobacteriaceae spp. abundances detected with ESBL potential was not detected by culture. By comparing the abundances by metagenomics in culture-positive and culture-negative inpatient samples, we found a wide variability in the density of MDR bacteria regardless of culture results. This suggests that the growth of MDR bacteria on culture does not reflect the relative abundance within the gut microbiota. Future studies will help clarify the relationship between colonization density and the risk of invasive infection using metagenomics. This work shows the importance of future metagenome studies to define its sensitivity and specificity for the detection of MDR bacteria with an optimization of analysis techniques and the currently available species and antibiotic resistance gene reference databases. In addition, further investigation is needed to determine the impact that metagenomics can have on patient outcomes and health care costs through preventing the spread of MDR bacteria and decreasing rates of MDR infection. Our protocol uses shotgun metagenomics to sample the entire metagenome randomly rather than the bacterium-specific 16S rrna gene sequencing method, which has been the most common approach to metagenome analysis. The first of two major limitations of 16S rrna gene sequencing is that this method does not sample antibiotic resistance genes and, therefore, has no way to determine whether organisms detected in a clinical sample possess antibiotic resistance genes. Second, 16S rrna gene sequencing is limited in resolution that only distinguishes bacteria to the genus level. Shotgun metagenomics has the capacity to overcome both of these limitations. The most obvious advantage of using shotgun methodology over 16S rrna metagenome sequencing is the ability to identify all known functional antibiotic resistance genes in addition to the species present. Patients with MDR colonization can be discerned from those lacking detection of clinically relevant antibiotic resistance genes. Patients with an increased abundance of a clinically important species while lacking the relevant corresponding antibiotic resistance genes were easily distinguished from patients harboring these genes; this was clearly demonstrated by one sample that had 98% of its microbiome comprised of E. faecium while lacking detection of any vancomycin resistance genes. We found that the resolution of species is an important consideration for surveillance of MDR bacteria. This is particularly important when assessing for VRE. There are several Enterococcus spp., but invasive infections are rare with species other than E. July 2016 Volume 54 Number 7 Journal of Clinical Microbiology jcm.asm.org 1811

9 Andersen et al. faecalis and E. faecium. We found 5 inpatient samples with high abundances of E. hirae, which is an uncommon cause of invasive infection. Overall, increased Enterococcus abundances were found in 9 (53%) inpatient and 2 (18%) outpatient samples but no controls. Of these, 3 (18%) inpatient samples had increased E. faecium and/or E. faecalis abundances, whereas the remaining inpatients and outpatients had increased abundances of other Enterococcus spp. All inpatient samples with increased E. faecium and/or E. faecalis were also associated with increased abundances of other Enterococcus spp. The role of these species and other gut commensals harboring clinically important antibiotic resistance genes in the transmission of antibiotic resistance genes to more invasive species (like E. faecalis and E. faecium) warrants further investigation. Given the ability to resolve species, use of shotgun rather than 16S rrna gene sequencing can likely improve the specificity of fecal metagenome screening to detect clinically important MDR bacteria and to determine the potential role of commensal gut microbiota in their acquisition, selection, and transmission. Using shotgun metagenomics, incorporation of numerous characteristics that were detectable in the gut microbiota allows for complex pattern recognition from a small sample size with alternative statistical methods to improve clinical diagnostic testing, which was previously not possible using more traditional methodologies. Toward this end, we plan to develop prediction models in a future prospective study to stratify patients according to their risk of an invasive MDR bacterial infection. Use of fecal shotgun metagenome sequencing for clinical surveillance in the future has tremendous capacity to improve clinical outcomes from invasive MDR bacterial infections in our children. ACKNOWLEDGMENTS We thank Thomas Blom and Biomedical Informatics for statistical expertise in analysis of this study and the Diagnostic Infectious Diseases Laboratory at CCHMC for materials and guidance with microbiology testing. We thank Leslie Korbee for editing. We declare no conflicts of interest. FUNDING INFORMATION This work, including the efforts of Heidi M. Andersen, was funded by NIH (T32ES and UL1TR000077). Funding for this study was supported by the NIH (grant T32ES ) under the MECEH Program at the University of Cincinnati, Haslam s Laboratory at Cincinnati Children s Hospital Medical Center, and the CTSA at CCHMC, which is funded by the NIH (award UL1TR000077). This project was supported in part by PHS grant P30 DK from the DNA Sequencing Core of the Digestive Disease Research Core Center in Cincinnati. These funding agencies had no role in the study design, data collection and interpretation, or the decision to submit the work for publication. REFERENCES 1. Di Pentima MC, Chan S, Briody C, Power M, Hossain J Driving forces of vancomycin-resistant E. faecium and E. faecalis blood-stream infections in children. Antimicrob Resist Infect Control 3: McNeil JC Staphylococcus aureus antimicrobial resistance and the immunocompromised child. Infect Drug Resist 7: Sick AC, Tschudin-Sutter S, Turnbull AE, Weissman SJ, Tamma PD Empiric combination therapy for Gram-negative bacteremia. Pediatrics 133:e1148 e Modi SR, Lee HH, Spina CS, Collins JJ Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome. 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Antimicrob Agents Chemother 30: Taur Y, Jenq RR, Perales MA, Littmann ER, Morjaria S, Ling L, No D, Gobourne A, Viale A, Dahi PB, Ponce DM, Barker JN, Giralt S, van den 1812 jcm.asm.org Journal of Clinical Microbiology July 2016 Volume 54 Number 7