Pulmonary Metagenomic Sequencing Suggests Missed Infections in Immunocompromised Children

Abstract

Background: Despite improved diagnostics, pulmonary pathogens in immunocompromised children frequently evade detection, leading to significant mortality. Therefore, we aimed to develop a highly sensitive metagenomic next-generation sequencing (mNGS) assay capable of evaluating the pulmonary microbiome and identifying diverse pathogens in the lungs of immunocompromised children.

Methods: We collected 41 lower respiratory specimens from 34 immunocompromised children undergoing evaluation for pulmonary disease at 3 children's hospitals from 2014-2016. Samples underwent mechanical homogenization, parallel RNA/DNA extraction, and metagenomic sequencing. Sequencing reads were aligned to the National Center for Biotechnology Information nucleotide reference database to determine taxonomic identities. Statistical outliers were determined based on abundance within each sample and relative to other samples in the cohort.

Results: We identified a rich cross-domain pulmonary microbiome that contained bacteria, fungi, RNA viruses, and DNA viruses in each patient. Potentially pathogenic bacteria were ubiquitous among samples but could be distinguished as possible causes of disease by parsing for outlier organisms. Samples with bacterial outliers had significantly depressed alpha-diversity (median, 0.61; interquartile range [IQR], 0.33-0.72 vs median, 0.96; IQR, 0.94-0.96; P < .001). Potential pathogens were detected in half of samples previously negative by clinical diagnostics, demonstrating increased sensitivity for missed pulmonary pathogens (P < .001).

Conclusions: An optimized mNGS assay for pulmonary microbes demonstrates significant inoculation of the lower airways of immunocompromised children with diverse bacteria, fungi, and viruses. Potential pathogens can be identified based on absolute and relative abundance. Ongoing investigation is needed to determine the pathogenic significance of outlier microbes in the lungs of immunocompromised children with pulmonary disease.

Keywords: immunocompromised host; intensive care units; metagenomics; microbiota; pediatric; respiratory tract infections.

Figure 1.

Aspergillus niger lower limit of detection by optimized next-generation sequencing. At the lower limit of detection (LLOD) of RNA sequencing (RNAseq), the optimized assay was able to detect as few as 0.42 Aspergillus niger colony-forming units (CFU; 95% confidence interval [CI], 0.12–1.40), whereas at the LLOD of DNAseq, the optimized assay was able to detect as few as 6.13 A. niger CFU (95% CI, 4.16–9.04; paired T test P < .001). Red data represent RNAseq and blue data represent DNAseq. Dotted lines represent the 95% CIs for each linear regression. (Insert) Parallel detection of A. niger RNA using digital droplet polymerase chain reaction assay. As nucleic acid bioavailability may vary across Aspergillus species, these results may not be directly extrapolated to other Aspergillus species and other medically relevant molds. Abbreviations: BAL, bronchoalveolar lavage; CFU, colony-forming unit; ddPCR, droplet digital polymerase chain reaction; seq, sequencing.

Figure 2.

Microbial alignments detected in the lungs of immunocompromised children. Red dots represent potentially pathogenic microbes that are both abundant (≥10 rpm for bacteria or ≥1 rpm for fungi or viruses) and identified at levels greater than most other samples in the cohort (Z-score ≥2). Hollow red dots indicating Bocavirus and Pneumocystis are used to indicate organisms observed only once in this cohort. Blue dots represent all other potentially pathogenic microbes; light blue dots represent typically nonpathogenic microbes. Subplots show (A) all bacteria, (B) fungi, (C) RNA viruses, and (D) DNA viruses identified across all samples in the cohort. For the purpose of the Z-score calculation, the value of log₁₀-transformed reads for undetected microbes was assumed to equal –2, just below the lower limit of detection for our sequencing depth (log₁₀[0.01rpm] = –2). Abbreviation: seq, sequencing.

Figure 3.

Respiratory samples with outlier pathogens have depressed bacterial alpha-diversity. Diversity of the bacterial microbiome was significantly decreased in samples with potentially pathogenic bacteria present at ≥10 rpm of the pulmonary bacterial microbiome and Z-score ≥2 (median, 0.61; interquartile range [IQR], 0.33–0.72; n = 13 vs median, 0.96; IQR, 0.94–0.96; n = 28; P < .001). Simpson diversity index cutoffs of ≥0.8 or ≥0.9 showed 90.3% (95% confidence interval, 77.6–96.2) and 100% negative predictive value for the presence of an outlier bacterial pathogen, suggesting that the identification of bacterial dysbiosis may be a useful screen for recognizing possible bacterial infections.

Figure 4.

Comparison of clinical laboratory results vs metagenomic next-generation sequencing (mNGS) results. Clinical laboratory results were determined by review of medical charts. n = 17 patients had samples with a pathogen detected clinically, as determined by interpretation of clinical microbiologic testing by the treating physician. Of these, n = 11 had concordant pathogens of outlier quantities on mNGS (Adenovirus/Rhinovirus, Aspergillus fumigatus, Enterobacter cloacae, Escherichia coli, Haemophilus influenzae, Haemophilus influenzae/Parainfluenza virus, Mycoplasma pneumoniae [n = 2], Pneumocystis jirovecii/Rhinovirus-A, Rhinovirus-C, and Staphylococcus aureus); n = 3 had concordant pathogens identified on mNGS but not in outlier quantities (Aspergillus [n = 2] and Rhinovirus-A); and n = 3 had an alternative pathogen identified on mNGS (Human coronavirus 229E [n = 2] and Human coronavirus OC43). n = 24 patients had samples without a pathogen detected clinically. Of these, n = 11 had a potential pathogen present in outlier quantities on mNGS (Candida glabrata, Cytomegalovirus, Cryptococcus [n = 2], Enterobacter cloacae, Human herpesvirus-6, Mycoplasma pneumoniae, Rhinovirus-A, Pseudomonas aeruginosa/Influenza-A, Staphylococcus epidermidis, and Streptococcus pneumoniae) and n = 13 did not. Abbreviation: mNGS, metagenomic next-generation sequencing.