http://orcid.org/0000-0002-8508-3698
Figures
Abstract
Background
Acute asthma exacerbations and pneumonia are important causes of morbidity and mortality in children and may coexist in the same children, although symptom overlap may lead to difficulties in diagnosis. Microbial and viral diversity and differential abundance of either may play an important role in infection susceptibility and the development of acute and chronic respiratory diseases.
Objectives
To describe the virome and bacteriome present in the upper respiratory tract of hospitalized children with a clinical diagnosis of asthma and pneumonia during an acute exacerbation and an acute respiratory illness ARI episode respectively.
Methods
During the winter seasons of 2013–2014 and 2014–2015, 134 nasopharyngeal swabs samples of children <15 years of age with ARI hospitalized at a referral hospital for respiratory diseases were selected based on clinical diagnosis of asthma or pneumonia. The virome and bacteriome were characterized using Whole Genome Sequencing (WGS) and in-house bioinformatics analysis pipeline.
Results
The Asthma group was represented mainly by RV-C, BoV-1 and RSV-B and the pneumonia group by Bacteriophage EJ-1 and TTMV. TTV was found in both groups with a similar amount of reads. About bacterial composition Moraxella catarrhalis, Propionibacterium acnes and Acinetobacter were present in asthma and Veillonella parvula and Mycoplasma pneumoniae in pneumonia. Streptococcus pneumoniae and Haemophilus influenzae were mostly found with both asthma and pneumonia.
Citation: Romero-Espinoza JA, Moreno-Valencia Y, Coronel-Tellez RH, Castillejos-Lopez M, Hernandez A, Dominguez A, et al. (2018) Virome and bacteriome characterization of children with pneumonia and asthma in Mexico City during winter seasons 2014 and 2015. PLoS ONE 13(2): e0192878. https://doi.org/10.1371/journal.pone.0192878
Editor: Oliver Schildgen, Kliniken der Stadt Köln gGmbH, GERMANY
Received: October 12, 2017; Accepted: January 31, 2018; Published: February 15, 2018
Copyright: © 2018 Romero-Espinoza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by Instituto de Ciencia y Tecnologia DF (ICYTDF) grant PICSA12-64 to JAVP and Fundacion Miguel Aleman A.C. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Acute respiratory infection (ARI), both from the upper and lower tract (URTI/LRTI), represent a major public health problem worldwide especially in developing countries [1,2]. Besides being one of the most common diseases, it is a leading cause for health care use, and mortality, particularly due to the presence of pneumonia [3–5]. In the clinical practice, acute asthma exacerbations and pneumonia have an overlap of symptoms, and may also coexist, leading to misdiagnosis. For example over-diagnosis of pneumonia and under-diagnosis of asthma may contribute to significantly untreated respiratory morbidity and mortality in children less than five years old in low-income countries [6]. Both groups differ with respect to the associated virus and bacteria: while asthma exacerbations have been associated to a specific rhinovirus infection, pneumonia can be related to a wide range of bacterial, fungal and viral agents, with a high prevalence of Respiratory Syncytial Virus (RSV) [2,7].
It has been proposed that microbial composition plays an important role in infection susceptibility and the development of acute and chronic respiratory diseases [8]. Recent studies describe the community of microorganisms that reside in the respiratory tract, referred to also as the microbiome, by using Next Generation Sequencing (NGS), and in most cases by evaluating bacterial components based only for specific gene (16S) [9,10]. On the other hand, the Whole Genome Shotgun (WGS) approach provides enough information to identify all viral and bacterial species not only for a specific gene [11]. While bacterial composition of the microbiome is called bacteriome, the virome (or viral metagenome) refers to the viral fraction comprising viruses responsible for acute, persistent, or latent infections, and viruses integrated into the host genome such as endogenous retroviruses [12].
A limited number of studies have attempted to characterize the virome (Yang et.al. [13]; Zoll et.al. [14]; Moesker et.al. [15]; Wang et.al. [16]; Bal et.al. [17]) and bacteriome (Hilty et.al. [10]; Taboada et.al. [18]; Teo et.al. [19]) of pediatric patients with ARIs. A few others studies have used well-defined clinical parameters of asthma exacerbation and pneumonia (Moreno-Valencia et.al. [2]; Vazquez-Perez et.al. [20]).
Here we describe the virome and bacteriome present in the Upper Respiratory Tract of hospitalized children clinically diagnosed with asthma and pneumonia, during an acute exacerbation and an ARI episode respectively, at the National Institute of Respiratory Diseases (INER, Mexico City) during 2014 and 2015 winter seasons.
Materials and methods
Ethics statement
The Science and Bioethics Committee of the National Institute of Respiratory Diseases revised and approved the protocol and the consent procedure (B2613). For all children, the corresponding legal guardians provided written informed consent.
Sample collection
Nasopharyngeal swabs (NS) were collected from children less than 15 years of age with clinical signs of ARIs hospitalized at the National Institute of Respiratory Diseases (INER) in Mexico City, a referral center for respiratory diseases, which primarily provided services for uninsured individuals, during the winter season 2013–2014 and 2014–2015. All subjects enrolled were classified with an asthmatic crisis (characterized by wheezing and difficult breathing especially in previously known asthmatics or with atopic symptoms) or pneumonia (with the presence of new lung opacities, with changes in the white blood count). Maximum sample number for each group was defined after each season, resulting in 42 for asthma, and 78 for pneumonia (Table 1).
RTqPCR viral detection
For all samples, automated nucleic acids isolation was performed on MagNa Pure LC 2.0 using the Total Nucleic Acids kit (Roche diagnostics). An In-house RT-qPCR respiratory viral panel (Moreno-Valencia et al., 2015) was used as previous screening and for virus identification (S1 Table and S1 Appendix).
Viral enrichment and random amplification
Nucleic acids were isolated directly from stored samples. For each one, 250 μl of transport media was centrifuged at 5,000xg for 5min to remove cellular debris. The supernatant was filtered through 0.2 μM-pore-size disc filters (Syringe filter, Corning). Samples were pooled in a 15ml concentrator tube (Amicon Ultra -15, Merck Millipore) and centrifuged at 4,000xg until volume was reduced to 500 μl. Nucleic acids were isolated from the concentrated sample pool using the PureLink Viral RNA/DNA kit according to the manufacturer’s instructions (Invitrogen, Waltham, MA). Nucleic acids were eluted in 60 μl of nuclease free water and stored in aliquots at -80°C.
To improve viral detection, one aliquot of 20 μL for each extraction was processed for RNA viruses and another aliquot for DNA viruses. The aliquot for RNA viruses was treated with RNase-free DNase I, (Thermo Scientific) for 30 min at 37°C and immediately chilled on ice. To reduce the amount of human DNA, the aliquot for DNA viruses was treated with NEBNext Microbiome DNA Enrichment Kit, according to the manufacturer’s instructions (New England BioLabs Inc.). For RNA viruses, reverse transcription was performed on 10 μl de RNA using a m13-random hexamer primer with Transcriptor First Strand cDNA Synthesis Kit (Roche diagnostics) Next double strand cDNA (dscDNA) was generated by two rounds of synthesis using m13-random hexamer primer with Klenow fragment (Thermo Scientific). For DNA viruses and Bacteria, DNA previously enriched was labeled using m13-random hexamer primer.
DNA from each process was amplified with Platinum Taq DNA Polymerase High Fidelity (Thermo Scientific) using m13 primers and the following program: 2 min 95°C, 30 cycles of 15 sec at 95°C, 30 sec at 50°C, 3 min at 68°C and a final step of 5 min at 68°C. DNA was quantified by Qubit dsDNA HS Assay Kit (Thermo Scientific), and length fragment was evaluated with Agilent High Sensitivity DNA Kit (Agilent Technologies).
Metagenomic analysis based on NGS
A whole genome approach (shotgun) was used for both viral and bacterial genomes. Libraries were built with 1 ng of amplified DNA from each sample of RNA viruses and DNA viruses/bacteria, and processed with Nextera XT DNA Library Preparation Kit according to the manufacturer’s instructions (Illumina). Libraries were labeled with Nextera XT Index Kit (Illumina) according the S1 Table and pooled into one. The pooled library was loaded in a flow cell and sequencing was performed in a MiSeq Desktop Sequencer (Illumina) to obtain paired-end reads of 250 bp in length (2x250). An in-house pipeline was developed to perform the bioinformatics analysis for all files (Fig 1 step 3). Datasets of human, bacteria and virus sequences (downloaded from the NCBI FTP server in March 2015) were downloaded to build Smalt, BWA index, local BLASTN and BLASTX databases. All data from MiSeq instrument were trimmed through a Phred-like q20 < 20 using Fastqc software. Reads were aligned to databases described above using a standalone BLASTn for direct assignment of each read with an E-value of 1e-30. For viruses, Blastn with an E-value of 1e-30 and Blastx with an E-value of 1e-01, on trimmed reads and after human, bacterial and viral mapping, were used respectively. Velvet algorithm was used for contig assembly.
In-house experimental workflow for nucleic acids isolation, amplification, qPCR viral screening, sample pooling and enrichment, shotgun NGS sequencing and reads analysis.
To reduce data complexity for bacterial detection, human sequences were removed by mapping with human smalt index before the pathogen identification process (Fig 1, step 3). Bacterial species were identified using local BLASTn with an E-value of 1e-30 and the first 10 hits were obtained for each sequence. MEGAN [21] 5.2.2 software was used to assign reads to the most appropriate taxonomic level, by assigning a read to the lowest common taxonomic ancestor of the organisms corresponding to the set of significant hits.
Phylogenetic analysis
For the Rhinovirus analysis, full-length genome of Human Rhinovirus A, B C (RV-A, RV-B, RV-C), Enterovirus 68 and 71 (EV68, EV71) were retrieved from the Genbank Database. For phylogenetic analysis of Parvovirus B19 (PVB19), Bocavirus (BoV) and Respiratory Syncytial Virus B (RSV-B), full-length genomes of representative sequences of human strains were used. Analyses also were performed for three different Anellovirus, Torque teno virus (TTV), Torque teno midi virus (TTMDV) and Torque teno mini virus (TTMV). Alignments were created and manually edited with MEGA [22] 6.0. Unrooted maximum likelihood tree with 1,000 bootstrap replicates was constructed using the Tajima-Nei model with 5-parameter gamma distributed rates.
Results
Sequencing data
One hundred and twenty respiratory samples were grouped into seven pools: asthma 2014 (A2014), asthma 2015 (A2015), pneumonia 2014 (P2014) and pneumonia 2015 (P2015) each one with qPCR positive (qPCR+) and qPCR negative (qPCR-) results, except qPCR- for A2015 season since we did not have enough samples to analyze (S1 Table). The average amount of sequences obtained from each group after quality filtering was 2,247193 within a range of 3,634,710 to 519,470 reads. From those reads, 0.18 to 0.35% belonged to viral or bacterial sequences, and as much as 28.08% belonged to endogenous retrovirus and sequences without certain identification.
Nasopharyngeal virome from children with asthma and pneumonia
To describe the virome of the two groups of children, we grouped reads from both seasons 2014 and 2015 into one. The most represented viruses were in order of number of reads Rhinovirus C (RV-C), Bocavirus 1 (BoV-1), RSV-B and Parvovirus B19 (PVB19) in patients with asthma while Bacteriophage EJ-1, TTMV, Streptococcus phage, RSV B and RV A were in subjects with pneumonia. TTV, influenza virus and Staphylococcus phage were present in similar levels for both groups of patients (Table 2). Most of the viral reads were classified into six virus families Picornaviridae, Parvoviridae, Paramyxoviridae, Anelloviridae, Orthomyxoviridae and Herpesviridae (Fig 2A and 2B). Bacteriopaghes also had high abundance of reads (Fig 2B). The viral composition in samples was complex and dominated by the most common respiratory viruses like Rhinovirus, RSV, BoV and Influenza. We detected few reads for Adenovirus (AdV) and as well as the absence of Coronavirus. However, there were several reads for viruses with unknown role in respiratory diseases like Torque teno virus, PVB19, Enterovirus D-68, Citomegalovirus and different families of Bacteriopaghes.
(A) Viral reads identified in the asthma samples by Blastn and Blastx at family level, the total number of reads for each one are presented. (B) Viral reads identified in the pneumonia samples by Blastn and Blastx at family level, the total number of reads for each one are presented.
Comparing the results between metagenomics and RT-PCR assay showed high consistency, with the exception of Coronavirus 229E in the asthma group and AdV and Coronavirus 229E in the pneumonia group, as the only viruses do not detect by metagenomics. By contrast, this methodology detected others viruses like Coxsackie, AdV, PVB19, Torque teno virus and Torque teno mini virus in asthma group and Hepatitis, Torque teno virus and Torque teno mini virus in pneumonia group.
Complete and partial viral genome and phylogenetic analysis
Four representative viruses were used to compare the genome coverage and depth. Contigs were ensembled to generate partial consensus genomes of RV-C (97% coverage, 6,824 bp) (S1 Fig), BoV-1 (76% coverage, 4055 bp) (S2 Fig), PVB19 (17% coverage, 963 bp) (S3 Fig), and RSV-B (7.8% coverage, 1184 bp) (S4 Fig). The phylogenetic analysis for the contigs built, identified a new variant called RV-C Mex14 (Genebank accession number KM486097) that formed a robust distinct cluster with sequences of strains isolated in USA, Italy and Hong Kong, between 2007 and 2011 (HRV-C45, HRV-C11 y HRV-C 025 respectively) (S5 Fig). BoV1 MexINER (MF947543) formed a cluster with Brazilian strains KU3 and San Salvador 1 (S6 Fig). RSVB, the Mexican strain (MF953391) formed a cluster with North America strain and European strain from Netherland (S7 Fig). Conversely, TTV_INERMex15 (MF953393) showed 68% of homology with TTV 8 genotype 22 from Indonesia (S8 Fig). Finally TTMV_INERMex14 (MF953394) showed 53% of homology to TTMV 3 from Japan (S9 Fig).
Nasopharyngeal bacteriome from children with asthma and pneumonia
The bacterial composition in samples was also complex. Streptococcus pneumoniae and Haemophilus influenzae were the species with more reads in both groups. Staphylococcus aureus, Mycoplasma pneumoniae Veillonella parvula and different Streptococcus spp had more reads in the pneumonia group (Fig 3A and 3B). While Moraxella catarrhalis, Acinetobacter and Propionibacterium acnes had more reads in the asthma group. Other bacteria genus detected of clinical interest with low frequency were: Pseudomonas, Neisseria and Prevotella.
(A) Bacterial reads identified in the asthma samples by Blastn at species level, the total number of reads for each one are presented. (B) Bacterial reads identified in the pneumonia samples by Blastn at species level, the total number of reads for each one are presented.
Discussion
We conducted a metagenomic analysis of the virome and bacteriome of children with asthma and pneumonia. This is the first report to describe both communities in a well-defined group of pediatric patients. Previously, our group reported the role of specific viral infections on asthma exacerbations in hospitalized children using RT-qPCR detection [2,20]. Although RT-qPCR is the gold standard for viral and bacterial diagnosis, it is still limited to specific target assays. By contrast, NGS is a platform that generates millions of reads without the need of previous knowledge of the sequences present in a sample. It is also a powerful tool to confirm and discover microbial etiologies in clinical samples.
Whole genome shotgun sequencing (WGS) provides enough information to identify microbes to the species level as a result of a more accurate alignment with genome reference sequences, not just in particular for specific genes such as 16S in bacteria [11]. In our analyses, most of the bacterial reads were classified into a species level, implying that WGS can be a useful tool to describe the microbiome in a clinical sample. In our study, 90% of reads were classified as Q30, and only 5% were discarded due to low sequencing quality. After being analyzed, human reads ranged from 71 to 91% in each group despite the use of an experimental strategy to reduce human DNA. These values are found in the same range for equivalent sample types previously reported by Yang et al. [13] and Nakamura et al. [23]: 76–95% and 90–94.6%, respectively. However, the percentage range obtained for bacterial and viral reads (0.10–0.32%) was lower than the quantities previously reported by Yang et al.(1.03%) [13], Nakamura et al.(1.33–3.48%) [23] and Taboada et al.(9.4%) [18].
All viruses detected by the qPCR panel were identified through NGS except Coronavirus 229E in the asthma group and Coronavirus 229E and Adenovirus in the pneumonia group, probably owing to a low number of reads and the dilution effect of sample-pooling. However, the NGS method combined with the bioinformatics workflow showed greater sensitivity for detection of InfA H1N1, PIV-1 and BoV-2 compared with qPCR detection, probably as a result of the identification of less conserved sequences. Moreover, other viruses such as TTV, TTMV, TTMDV and different phages like Propionibacterium phage, Streptococcus phage, Staphylococcus phage and Bacteriopage EJ-1 were also detected without having knowledge of the viruses.
A diverse viral community was found in the respiratory tract of children with asthma and pneumonia; RV-C and RSV-B were the predominant species. Viruses found in these respiratory samples belonged to different families, mainly Picornaviridae, Orthomyxoviridae and Paramyxoviridae. Members of the Anelloviridae family were found in both groups of pediatric patients; although members of this family are usually considered ubiquitous and benign, a potential role of TTMV in pneumonia pathogenesis has been suggested [24], Other studies have implicated TTV as contributing elements in lung impairment and it has been associated with asthma [25] and chronic obstructive pulmonary disease (COPD) [26]. Even so, more studies are needed to elucidate the role of respiratory pathogenicity of the Torque teno viruses. EVD68 has been associated with asthma exacerbation and pneumonia in children [20], In this study, EV-D68 was detected in the 2014–2015 winter seasons, confirming the presence of this virus in Mexico City. With respect to CMV, some studies have found evidence of its implications in asthma pathogenesis and asthmatic inflammation [27]. InfA H1N1pdm09 was found in most of the studied groups, its participation and association with bacterial pneumonia [7] are well known, but a strong association with asthma exacerbation has been reported as well [28]. Among other of the found viruses, Adenovirus (AdV) has been associated with pneumonia [29], and BoV has been deemed as a cause of SARI in the absence of viral and bacterial co-infections [15]. Finally, Wiersbitzky et al. [30] suggested that PVB19 can cause acute respiratory diseases with obstructive ventilatory disturbances of the upper or lower airways in children with a specific endogenous predisposition. Other components detected in the viruses were bacteriophages, viruses commonly excluded in the final analysis of the viral community, Yet, these highly suggest the presence of their specific bacterial hosts in the sample.
We compared our results to previous reports that have used metagenomics in children [17], and shown strategies that use pooling of samples (Lysholm et.al. [31] and Wang et.al. [16]), where greater diversity has been found. In our study, we detected 25 different species of viruses using this same strategy. This is most likely due to a greater ability of analyzing more samples in the same library, thereby increasing the probability to detect more viruses. In our study, we found the preferred virus associated with respiratory diseases, Rhinovirus, and consistent with others reports (Lysholm et.al. [31] and Wylie et.al. [12]), however in contrast with a report from Wang et.al. [16] that found a higher prevalence of RSV-B. Furthermore, few reports included healthy individuals as a control group (Wang et.al [16]), detecting lesser viral diversity with a high prevalence of viruses belonging to the Anelloviridae family. According to this report, the Torque teno virus and Torque teno mini virus that we found in children with asthma and pneumonia in our study, may be otherwise normal residents in children free of respiratory disease. Further data is lacking concerning the prevalence of viruses in the respiratory tracts of healthy children, which may be a limitation of our study.
In performing the bacteriome analysis, we found Streptococcus pneumoniae and Haemophilus influenzae had the greater amount of identified reads in both groups with a significant presence of Moraxella and Staphylococcus aureus in asthma and pneumonia groups respectively (Fig 3A and 3B). These findings are consistent with data reported by Bisgaard et al. [32] and Hilty et al. [10] for children with asthma.
Before rendering judgment, is important to remember that the individual presence of any virus or bacteria does not determine a direct cause for disease. Interactions between invaders and host commensals may offer a better understanding about respiratory diseases [33]. Identifying coinfections between different microorganisms either between viruses or viruses and bacteria is essential in determining the etiology of the respiratory disease. Identifying the etiologic agents present on each disease may assist in developing better treatments and thereby improving patient care. Metagenomic and bioinformatic analyses are very powerful tools to describe the microbiome, which allow discerning the etiologic agents involved, and eventually may lead to the discovery of new respiratory viruses.
Supporting information
S1 Table. Sample pooling of asthma and pneumonia samples from winter season 2013 and 2014.
https://doi.org/10.1371/journal.pone.0192878.s001
(DOCX)
S1 Appendix. Multiplex RT-qPCR for respiratory virus detection.
https://doi.org/10.1371/journal.pone.0192878.s002
(DOCX)
S1 Fig. RV-C contig obtained by metagenomic analysis.
Longest contig obtained using velvet and RV-C Mex14 genome map.
https://doi.org/10.1371/journal.pone.0192878.s003
(TIF)
S2 Fig. BoV-1 contig obtained by metagenomic analysis.
Longest contig obtained using velvet and BoV-1 genome map.
https://doi.org/10.1371/journal.pone.0192878.s004
(TIF)
S3 Fig. PVB19 contig obtained by metagenomic analysis.
Longest contig obtained using velvet and PVB19 genome map.
https://doi.org/10.1371/journal.pone.0192878.s005
(TIF)
S4 Fig. RSV-B contig obtained by metagenomic analysis.
Longest contig obtained using velvet and RSV-B genome map.
https://doi.org/10.1371/journal.pone.0192878.s006
(TIF)
S5 Fig. Maximum-likelihood (ML) phylogenetic trees for RV-C contigs obtained from asthma and pneumonia children samples.
ML tree from 49 HRV-A, B and C viruses and Enterovirus 68 and 71 registered in GenBank were produced. The consensus sequence of Mexican 2014 and Bootstrap values are shown in each node.
https://doi.org/10.1371/journal.pone.0192878.s007
(TIF)
S6 Fig. Maximum-likelihood (ML) phylogenetic trees for BoV-1 contig obtained from asthma and pneumonia children samples.
ML tree from 191 Human Bocavirus sequences was calculated using complete VP1 region (2025 bp), cluster with MexINER14 and Brazilian strains are indicated (red rectangle). Bootstrap values are shown in each node. Bocavirus 2 (open circle), Bocavirus 4 (filled circle), Bocavirus 3 (open rectangle), Bocavirus 1 (open triangle).
https://doi.org/10.1371/journal.pone.0192878.s008
(TIF)
S7 Fig. Maximum-likelihood (ML) phylogenetic trees for RSV-B contig obtained from asthma and pneumonia children samples.
ML tree from 52 and 74 sequences of RSVA and RSVB respectively was calculated using partial N protein and partial P protein, cluster with RSVB MexINER14, USA and Netherlands strains are indicated (red rectangle). RSVB (open circle), RSVA (open rectangle).
https://doi.org/10.1371/journal.pone.0192878.s009
(TIF)
S8 Fig. Maximum-likelihood (ML) phylogenetic trees for TTV contig obtained from asthma and pneumonia children samples.
ML tree from 176 of Torque teno virus was calculated using partial ORF1 protein. Mexican strain (TTV MexINER15) is indicated (red circle). Tree was produced with 1,000 bootstraps replicates and bootstrap values are shown in each node. TTV_HD (open circle), TTV_tth (filled circle), TTV_sle (open rectangle), TTV_species (filled triangle), TTV_8 (open triangle), TTV_Simian (open diamond), TTV_Marten (filled diamond).
https://doi.org/10.1371/journal.pone.0192878.s010
(TIF)
S9 Fig. Maximum-likelihood (ML) phylogenetic trees for TTMV contig obtained from asthma and pneumonia children samples.
ML tree from 33 sequences of Torque teno mini virus was calculated using partial ORF1 protein. Mexican strain (TTMV MexINER14) is indicated. Tree was produced with 1,000 bootstraps replicates.
https://doi.org/10.1371/journal.pone.0192878.s011
(TIF)
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