Introduction

The nasopharyngeal tract is inhabited by specific ecosystems that may change during infections1,2,3,4 and that play a central role in the host's susceptibility to pathogens5. However, as the microbiota in this district is strongly influenced by the environment, its microbial ecology and the interactions with pathogens are of particular interest in full comprehension of diseases.

Bordetella pertussis is the main causative agent of pertussis, a highly contagious respiratory infectious disease that continues to occur worldwide, in both highly vaccinated and unvaccinated populations6. Pertussis is still a major cause of morbidity and mortality in infants and children < 5 years old7. It is estimated that ~ 53,500 (0.9%) of the 5.941 million deaths in 2015 were due to pertussis in this age group8.

A significant proportion of pertussis infections presents with a concomitant viral infection, mostly represented by rhinoviruses9,10. While the clinical significance and pathogenesis of coinfections are not definitely clarified yet, either B. pertussis and rhinovirus infections in infancy have been associated with wheezing and asthma in older ages11,12. The pathogenesis of asthma episodes may involve the ecology of the respiratory tract as suggested by some authors3,13,14.

Modulation of the nasopharyngeal microbiota in the presence of B. pertussis infection has been described in the mouse models only15,16. These studies evidenced a preventive action of the nasopharyngeal resident microbiota against B. pertussis colonization15, while the use of antibiotics produces the opposite effect, favoring B. pertussis colonization16.

Understanding the modulation of ecology of nasopharynx in humans with B. pertussis infection may help to understand determinants and potential diagnostic, preventive and therapeutic implications for this disease. In addition, no data are available on the changes that may derive from the simultaneous infection by B. pertussis and Rhinovirus.

In this study, we investigated the characteristics of nasopharyngeal microbiome in infants with B. pertussis and Rhinovirus simultaneous infection with those of either infection alone.

Results

Patients’ clinical features

Socio-demographic characteristics of patients included in the study are shown in Table 1.

Table 1 Characteristics of the patients (n = 54).
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The majority of patients were males (57.4%) with a median age of 1.5 months; patients with B. pertussis infection were slightly older than those with Rhinovirus infection or coinfection.

Children with coinfection were more likely breastfed compared to those with Rhinovirus even if the difference is small (57% vs 50% respectively, p = 0.029). On the other hand, children with B. pertussis infection were more likely formula-fed compared to those with coinfection (64% vs 14%, p = 0.029).

Table 2 shows the clinical features among the three groups. Cough, paroxysmal cough, whooping and apnea were more frequent in patients with B. pertussis infection. Cyanosis was occurred more frequently in the coinfection group by comparison to the Bp or Rv groups. In addition, the coinfection group more likely uses antibiotics before admission.

Table 2 Clinical features by group of infection (n = 54).
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Dendrogram analysis, based on clinical features’ correlation, revealed that C-reactive protein (CRP), fever and antibiotic treatment at the admission (others’ than macrolides) were correlated together and constituted a separated cluster from the other clinical variables. The other features were subgrouped into: (i) apnea, cyanosis, macrolides’ assumption and antibiotics treatment before the admission, and (ii) delivery, feeding cough, paroxysmal cough and inspiratory stridor, complications, vomit and leukocytes plus lymphocytes count (Fig. S1).

Nasopharyngeal microbiota profiling

A total of 2,393,694 sequencing reads were obtained from 54 nasopharyngeal aspirates, with a mean value of 44,328 sequences per sample. We identified an overall of 245 operational taxonomic units (OTUs), belonging to 15 phyla and 95 families.

To assess the overall differences of microbial community structures for the three groups, ecological parameters were based on alpha-diversity indexes (i.e., Shannon, Chao1, and Observed species indexes).

Despite no statistical differences were resulted, samples belonging to Rv group showed higher values of all indexes followed by Bp samples and then Bp + Rv samples (Fig. 1, panels a–c).

Figure 1
figure 1

Microbiota diversity analysis. Alpha diversity analysis (a–c). Box plots show Shannon, Chao1, and Observed species indexes for each patients’ group. Microbiota beta diversity analysis (d–f). Principal coordinates analysis (PCoA) plot of bacterial beta-diversity based on Bray Curtis dissimilarity (d) and unweighted (e) and weighted (f) UniFrac phylogenetic distance. The plots show the first two principal coordinates (axes) of PCoA. Beta diversity distance box plots (g–i). Data are expressed as minimum, maximum and median values, and 25th and 75th percentiles values. The statistical significant differences are determined by permanova-pairwise tests and indicated by the star. Box plots of OTUs statistically significant by Kruskal–Wallis test (j–m). In y-axis are reported OTU’s relative abundances; in x-axis are reported median, minimum and maximum values, and the 25th and 75th percentile values.

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Regarding beta diversity analysis, only Bray Curtis algorithm evidenced a statistically significant separation amongst groups (PERMANOVA p value ≤ 0.05), while phylogenetic unweighted and weighted UniFrac algorithms not (PERMANOVA p values > 0.05) (Fig. 1, panels d–f). Analyses of the intra-group distances revealed statistically significant differences between Bp and Rv for Bray–Curtis and Unweighted UniFrac metrics (Fig. 1, panels g–i).

The OTU distribution was investigated at the phylum and genus levels. At phylum level, Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes were the most representative phyla in the three groups (Fig. S2, Panel A). At genus level, Enterobacteriaceae, Veillonella, Staphylococcus, Gemellaceae, Alcaligenaceae and Achromobacter were increased in Bp ecosystem, while Streptococcus and Moraxella in Rv group (Fig. S2, Panel B).

Kruskal–Wallis test confirmed the higher abundance of Achromobacter and Alcaligenaceae in Bp group compared to the other groups (raw p value < 0.05) (Fig. 1, panels j and k), and the higher abundance of Moraxella and Moraxellaceae in Rv compared with others (raw p value < 0.05) (Fig. 1, panels l and m).

In the comparison amongst the three groups the principal component analysis (PCA) (Fig. S3, Panel a) and the partial least square (PLS) (Fig. S3, Panel b) analyses, performed on OTU’s profiles at genus levels, reveled a more separated Bp clusters compared to the other two groups. The same analyses, restricted to Bp and Rv comparison, revealed two separated PCA clusters (Fig. S3, Panel c) and none in PLS (Fig. S3, Panel d).

Correlation between clinical features and nasopharyngeal microbiota global distribution

At family level, Pearson’s correlation revealed that all considered clinical features showed a correlation with a least one OTU. In particular, Alcaligenaceae was positively correlated with cough, paroxysmal cough and inspiratory stridor, while negatively with CRP. Moraxellaceae was negatively correlated with paroxysmal cough and inspiratory stridor (Fig. 2, panel a).

Figure 2
figure 2

Pearson’s heat-map correlation. The Pearson’s correlation is calculated between clinical features and OTUs’ relative abundances at family (a) and genus (b) levels. The color scale represents the scaled level of each variable: red, positive correlation values; blue, negative correlation values. The stars indicate the statistically significant correlations (p value ≤ 0.05).

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At genus level, antibiotic treatment before the admission was positively correlated with Paracoccus and Haemophilus and negatively with Rothia. Cough was negatively correlated with Clostridium, Blautia, Staphylococcus and Ruminococcus and positively with Achromobacter.

Paroxysmal cough was positively correlated with Neisseria and Achromobacter and negatively with Moraxella. Inspiratory stridor was positively correlated with Campylobacter and Achromobacter and negatively with Blautia, Enterococcus and Moraxella. Cyanosis was negatively correlated with Aggregatibacter. Apnea was positively correlated with Prevotella, Neisseria, Fusobacterium and Porphyromonas and negatively with Bacteroides, Oscillospira, Bifidobacterium and Rothia. CRP had positive correlation with Haemophilus and negative with Achromobacter. Finally, complications were positively related with Methanobrevibacter and Streptococcus (Fig. 2, panel b).

Comparison between Bp- and Rv-related microbiota and patients’ clinical profiles

To focus onto microbiota specific features associated to B. pertussis or Rhinovirus infection and to highlight their correlations with patients' clinical traits, we focalized on Bp- and Rv-related microbiota, excluding the coinfection group (Bp + Rv).

The Pearson’s correlation performed on OTU profiles at genus level revealed two wide clusters. The first (a) was associated to Rv patients (9/16) with a nasopharyngeal microbiota profile rich in genera belonging to Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria, and related to low levels of leukocytes + lymphocytes (14/16) (Fig. 3). The second (b) was composed by two sub-clusters, namely b1 and b2. The cluster b1 was prevalently associated to Bp patients (6/7), characterized by high levels of leukocytes and lymphocytes, Caesarian section delivery and formula milk or mixed milk feeding and by a nasopharyngeal microbiota principally composed by Proteobacteria (Fig. 3). The patients in cluster b2, mostly Rv infected, was characterized by low level of leukocytes and lymphocytes, no antibiotic at the admission and not specific feeding and delivery modality and with a nasopharyngeal microbiota profile prevalently composed by Firmicutes and Proteobacteria (Fig. 3).

Figure 3
figure 3

Hierarchical cluster of Bp and Rv patients according to OTU distribution at the genus level. The heat-map reports the hierarchical Ward-linkage clustering based on the Pearson’s correlation coefficient amongst OTUs at genus level. The color scale represents the scaled level of each variable: yellow, high level; blue, low level. The column bar is colored according to the subject category and clinical features’ groups. The left bar is colored according to the phylum level taxonomy.

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Model classifications analysis

To investigate if nasopharyngeal microbiota profile could be predictive of a specific type of infection, we used the model classification analysis. This analysis, at genus level, revealed that the microbiota had capability to classify the 90% of the Bp- and Rv-related patients by the models Extra Trees Classifier, Gradient Boosting Classifier, Bagging Classifier and Decision Tree Classifier. A lower level of classification for Bp- (80%) was obtained compared to the classification of Rv-related patients (100%) (Table S1). Moreover, applying Random Forest Classifier and XGBRF Classifier, the microbiota profile reached the 100% of capability in correctly classifying both groups of patients.

In Fig. 4 were reported the OTUs selected by the model analysis and the respective contribution value (importance score) in the model prediction.

Figure 4
figure 4

Important OTUs selected by model classification analysis. The bars represent the importance scores of each OTUs in the prediction of models.

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Discussion

Our study describes for the first time the nasopharyngeal microbiota in children with pertussis.

We showed a different microbiota profiles consisting in the increase of Enterobacteriaceae, Veillonella, Staphylococcus Gemellaceae, Alcaligenaceae and Achromobacter in Bp group and of Streptococcus and Moraxella in Rv group.

The absence of comparison between B. pertussis infected patients with healthy subjects, due to the invasiveness of the nasopharyngeal aspirate in healthy infants, represents a limitation of this study. For this reason and in consideration of the high prevalence of cases of B. pertussis-Rhinovirus coinfection, we compared the nasopharyngeal microbiota profile of infants affected by single infection of B. pertussis or Rhinovirus with that of coinfected patients, who routinely underwent nasopharyngeal aspirate.

The nasopharyngeal microbiota of children is densely colonized by commensal bacteria, such as Moraxella, Haemophilus, Streptococcus, Flavobacterium, Dolosigranulum, Corynebacterium, Neisseria and Fusobacterium. However, Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis are considered potential airway pathogens17. When the nasopharyngeal eubiotic status is perturbed, these potentially pathogenic microorganisms can invade adjacent sites and cause different diseases17,18,19,20,21,22. It remains not clear which is the mechanism favoring pathogens’ colonization; however the interaction amongst multiple pathogens including viral coinfections, the progressive shift from health to disbiotic status and the effect of antibiotic administration could represent triggering factors17,20,23.

Based on our results, a reduction of nasopharyngeal microbial richness was evident in coinfection related microbiota compared to that related to single infections. The reduction of the diversity of the nasopharyngeal microbiota has been actually associated with the increase frequency of upper respiratory infections4,24. Moreover, considering the comparison between coinfected and Rv groups, this effect could also depend to macrolide intake. In fact, 90.5% of coinfected patients took macrolides respect to only 16.7% of Rv. On the contrary, also Bp patients took macrolides (87.6%), then the difference in microbiota diversity with coinfected patients could be driven by the different type of infection.

Analyzing the distance matrices and the composition of the microbiota, the co-infected microbiota profile was similar to both single infections' microbiota, even if with a higher similarity to the Rv group.

The comparison amongst the three groups revealed the increase of Alcaligenaceae and Achromobacter in the Bp infection group. Achromobacter spp., belonging to Alcaligenaceae family, are environmental microorganisms, causing a series of opportunistic illnesses, including respiratory diseases25,26. In our study, Achromobacter resulted related to cough, paroxysmal cough and inspiratory stridor, suggesting a role of this microorganism in the disease prognosis. Moreover, Moraxellaceae and Moraxella were increased in Rv infection group, as confirmed also by literature27,28,29. During Rv infection it has been described a colonization by as S. pneumoniae, H. influenzae and M. catarrhalis3,13,30. In particular, it seems that the nasopharyngeal colonization by Streptococcus and Moraxella precedes the Rv infection, probably due to the decreasing in ciliary function of epithelial cells and increasing of pro-inflammatory molecule release30. The simultaneous presence of Rv and Moraxella seems to be associated with severe lower respiratory infections, suggesting a correlation between specific viral/bacterial interactions and increase of disease severity3.

Evaluating the impact of delivery and feeding modality, count of leukocytes, administration of antibiotics it was possible to identify specific taxa as representative of Rv and Bp groups. Particularly, patients with vaginal delivery, maternal feeding, low count of leukocytes plus lymphocytes, low administration of antibiotics and rich microbiota profile resulted those infected by Rhinovirus. On the contrary, patients infected by B. pertussis were characterized by caesarian section delivery, formula milk feeding, high counts of leukocytes, antibiotic administration and high level of Proteobacteria. Thanks to these evidences we speculate that these conditions could shape the microbiota to be more prone to the airway infection by B. pertussis.

In conclusion, external factors since the first moments of life contribute to the alteration of nasopharyngeal microbiota, and this event could increase the susceptibility of the host to the pathogens' infections. In our opinion, when the infection is triggered, the presence of infectious agents could further modify the microbiota ecological niche favoring the overgrowth of other commensal bacteria that turn in pathobionts, hence contributing to the disease severity.

Methods

Study design and population

This study was performed at Bambino Gesù Children’s Hospital (OPBG), of Rome, Italy. From January 2016 to December 2019, all infants younger than 1 year of age, accessing the emergency room with symptoms compatible with pertussis, according to European Centre for Disease Prevention and Control (ECDC) case definition, were routinely screened for pertussis and respiratory viruses in the context of an enhanced surveillance program. A subset of patients with either B. pertussis (Bp) or Rhinovirus (Rv) or both infections (Bp + Rv), for whom a leftover of the nasopharyngeal aspirate was available for the analysis of the pharynx microbiota, was selected for this study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by Bambino Gesù Children’s Hospital Ethics Committee (1355_OPBG2017). Informed consent was obtained from the parents or legal guardians of all participants.

Data collection and laboratory confirmation of pertussis

Sociodemographic and clinical data were collected for each patient. Nasopharyngeal aspirate were collected within 24 h of hospital admission and processed immediately, or stored at − 80 °C until analysis. Two-hundred μl of nasopharyngeal sample were used for nucleic acids extraction by the EZ1 Virus Mini Kit v. 2.0 on the EZ1 Advanced XL platform (Qiagen, GmbH, Hilden, Germany).

Bordetella Real Time (RT) PCR kits were used to investigate the presence of B. pertussis, amplifying the inter-space (IS) 481 target (Bordetella R-gene™ assay; Argene, Biomerieux, Marcy l’Etoile, France). To prevent misdiagnosis of Bordetella holmesii as B. pertussis, all samples positive for B. pertussis were confirmed by the amplification of ptxP (promoter of pertussis toxin) locus by a RT-PCR assay specific for B. pertussis. Moreover, culture for B. pertussis was also performed, using Regan-Lowe and Bordet-Gengou selective culture media at 37 °C under aerobic conditions. Nasopharyngeal aspirates were also processed by a multiplex RT-PCR using a specific panel detecting the 16 following viruses: Respiratory syncytial virus (RSV) A and B; Influenza virus A and B; Coronaviruses OC43, 229E, cNL63, and HUK1; adenovirus; human Rhinovirus (hRV); parainfluenza virus 1–2–3–4; human Metapneumovirus-hMPV and human Bocavirus-hBoV.

16S rRNA targeted-metagenomics of nasopharyngeal microbiota

DNA from 200 µl of nasopharyngeal samples was automatically extracted using the EZ1 DNA Tissue Kit and biorobot EZ1 extractor following manufacturer’s instructions (Qiagen, Hilden, Germany). The V3-V4 region of 16S rRNA gene (~ 460 bp) was amplified by PCR using the primers 16S_F 5′-(TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG)-3′ and 16S_R 5′-(GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C)-3′ as reported in the MiSeq rRNA Amplicon Sequencing protocol (Illumina, San Diego, CA). The first amplification was carried out setting up the following conditions: initial denaturation at 95 °C for 3 min, 32 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s, and a final extension step at 72 °C for 5 min, with Fast Start Hifi Taq kit (Roche Diagnostics, Mannheim, Germany). DNA amplicons were cleaned with 20 ul of KAPA Pure Beads (Roche Diagnostics, Mannheim, Germany) and tagged with unique index combinations using Nextera technology in a second amplification step. Following steps consisted of cleaning of the final library, quantification with Quant-iT™ PicoGreen® dsDNA Assay Kit (Thermo Fisher Scientific, Waltham, MA) and dilution to 4 nM concentrations. Pooling, denaturation and dilution to 7 pM were performed before the sequencing on an Illumina MiSeqTM platform (Illumina, San Diego, CA, United States) where paired-end reads of 300 base-length were generated.

All raw sequences have been archived in NCBI database: PRJNA730698 (https://www.ncbi.nlm.nih.gov/bioproject).

Biocomputational and statistical analyses

Illumina Miseq reads were checked for quality, length and chimera presence using the Qiime v1.9 pipeline31, and USEARCH algorithms32. Then, sequences were organized into Operational Taxonomic Units (OTUs) with a 97% of clustering threshold of pairwise identity by UCLUST with open reference method32. PyNAST v.0.1 software31 was used to carry out a multiple sequence alignment against Greengenes 13_08 database with a 97% similarity for bacterial sequences and to build a phylogenetic tree33. Alpha diversity was performed by scikit-bio (http://scikit-bio.org/) of Python 3 package using Shannon index, Chao1 estimator, and observed species indices, and the p value for group comparisons was determined by analysis of variance (ANOVA). Beta diversity analysis of samples, based on phylogenetically informed weighted and unweighted Unifrac distance matrices34 and Bray Curtis matrices, was used and graphed by principal coordinate analysis (PCoA) plots. The association between the covariates and beta diversity measures was assessed by permutational analysis of variance (PERMANOVA)35.

Categorical variables were tabulated as frequencies and percentages and compared using Pearson’s Chi square test.

OTUs were filtered to select only the features of interest by retaining OTUs with interquartile range (IQR) ≠ 0 and OTUs present in at least 25% of the samples.

The non-parametric Mann–Whitney U-test, Wilcoxon signed-rank, Kruskal–Wallis test, corrected for FDR p value ≤ 0.05, were used to compare OTUs relative abundance amongst groups.

Both the OTUs and clinical variables (considered both raw values and categorical variables) were clustered using the Pearson’s correlation coefficient as the distance measure. In order to correlate clinical variables and OTUs, the Pearson’s coefficient with relative p value was calculated and represented by heat map.

Multiple machine learning (ML) was trained for the classification tasks. The pipeline consisted of a tenfold cross-validation with a train-test split of 70–30%. To evaluate the model, the global and the single class accuracies were considered. The tested models were: Logistic Regression, SGD Classifier, Logistic Regression CV, Hist Gradient Boosting Classifier, Random Forest Classifier, Extra Trees Classifier, Gradient Boosting Classifier, Bagging Classifier, Ada Boost Classifier, XGB Classifier, XGBRF Classifier, MLP Classifier, Linear SVC, SVC, Gaussian NB, Decision Tree Classifier, Quadratic Discriminant Analysis, K Neighbors Classifier, Gaussian Process Classifier.