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  • Research note
  • Open Access

Sphingolipid metabolism potential in fecal microbiome and bronchiolitis in infants: a case–control study

BMC Research Notes201710:325

https://doi.org/10.1186/s13104-017-2659-9

  • Received: 16 March 2017
  • Accepted: 21 July 2017
  • Published:

Abstract

Objective

Emerging evidence demonstrated that the structure of fecal microbiome is associated with the likelihood of bronchiolitis in infants. However, no study has examined functional profiles of fecal microbiome in infants with bronchiolitis. In this context, we conducted a case–control study. As a part of multicenter prospective study, we collected stool samples from 40 infants hospitalized with bronchiolitis (cases). We concurrently enrolled 115 age-matched healthy controls.

Results

First, by applying 16S rRNA gene sequencing to these 155 fecal samples, we identified the taxonomic profiles of fecal microbiome. Next, based on the taxonomy data, we inferred the functional capabilities of fecal microbiome and tested for differences in the functional capabilities between cases and controls. Overall, the median age was 3 months and 45% were female. Among 274 metabolic pathways surveyed, there were significant differences between bronchiolitis cases and healthy controls for 37 pathways, including lipid metabolic pathways (false discovery rate [FDR] <0.05). Particularly, the fecal microbiome of bronchiolitis cases had consistently higher abundances of gene function related to the sphingolipid metabolic pathways compared to that of controls (FDR <0.05). These pathways were more abundant in infants with Bacteroides-dominant microbiome profile compared to the others (FDR <0.001). On the basis of the predicted metagenome in this case–control study, we found significant differences in the functional potential of fecal microbiome between infants with bronchiolitis and healthy controls. Although causal inferences remain premature, our data suggest a potential link between the bacteria-derived metabolites, modulations of host immune response, and development of bronchiolitis.

Keywords

  • Microbiome
  • Infants
  • Bronchiolitis
  • Bacteroides
  • Sphingolipids

Introduction

Bronchiolitis is a common acute respiratory infection and the leading cause of hospitalizations in US infants [1, 2]. Although bronchiolitis has been considered virus-induced inflammation of small airways [3], recent studies demonstrate that the pathobiology involves complex interrelations among respiratory viruses, host immune response, and human microbiome [410]. Emerging evidence also indicates the existence of “gut-lung axis” in which the gut microbiome conditions immunologic responses in the lungs to environmental challenges (e.g., viral infection) [11]. Indeed, we have previously demonstrated, in a case–control study of infants hospitalized for bronchiolitis and healthy controls [11], that the taxonomy profiles of the fecal microbiome were associated with the likelihood of bronchiolitis—e.g., infants with the Bacteroides-dominant profile were more likely to have bronchiolitis. Although previous studies suggest that the gut microbiome-derived metabolites (e.g., sphingolipids) may play an important role in the host immune development [12, 13], the functional profiles of fecal microbiome in infants were not examined in the earlier study. To address this knowledge gap, we determined the predicted function of fecal microbiome in infants with bronchiolitis and healthy infants.

Main text

Methods

This study was a secondary analysis of the data from a case–control study of infants hospitalized for bronchiolitis and healthy controls. The study design, setting, participants, and methods of data collection have been reported previously [11]. In brief, as a part of a multicenter prospective cohort study, called the 35th Multicenter Airway Research Collaboration (MARC-35) [47, 9], we enrolled 40 infants (aged <12 months) hospitalized for an attending physician diagnosis of bronchiolitis from November 2013 through April 2014. Bronchiolitis was diagnosed according to the American Academy of Pediatrics guidelines [14]. Exclusion criteria were a transfer to a participating hospital >48 h after the original hospitalization, delayed consent (>24 h after hospitalization), gestational age ≤32 weeks, and known comorbidities (cardiopulmonary disease, immunodeficiency, immunosuppression). In addition, during the same period, we also enrolled 115 healthy infants as the controls (age-matched within 1.5 months of cases) [11, 1517]. We excluded infants with current fever, respiratory illness, or gastrointestinal illness, antibiotic treatment in the preceding 7 days, gestational age ≤32 weeks, or known comorbidities. Taken together, a total of 155 infants were eligible for the current analysis. From these infants, by using a standardized protocol [11, 15, 17], investigators conducted a structured interview and medical record review, and collected fecal specimens at the time of hospitalization (cases) or at home before the clinic visit (controls). The fecal samples were immediately stored at −80 °C. The institutional review board at each of the participating hospitals approved the study. Written informed consent was obtained from the parent or guardian.

16S rRNA gene sequencing was performed based on the methods adapted from the NIH Human Microbiome Project. Briefly, bacterial genomic DNA was extracted using MO BIO PowerMag DNA Isolation Kit (Mo Bio Lab; Carlsbad, CA). The 16S rDNA V4 regions were amplified by PCR and sequenced in the MiSeq platform (Illumina; SanDiego, CA) using 2 × 250 bp paired-end protocol. Sequencing read pairs were demultiplexed based on the unique molecular barcodes, and reads were merged using USEARCH v7.0.1090, allowing no mismatches and a minimum overlap of 50 bases. We trimmed the merged reads at the first base with a Q5 quality score. We calculated the expected error after taking into account all Q scores across all the bases of a read and the probability of an error occurring. We also applied a quality filter to the resulting merged reads, discarded the reads containing >0.05 expected errors. We constructed rarefaction curves of bacterial operational taxonomic units (OTUs) using sequence data for each sample to ensure coverage of the bacterial diversity present (Fig. 1). 16S rRNA gene sequences were clustered into OTUs at a similarity cutoff value of 97% using the UPARSE algorithm; OTUs were mapped to the SILVA Database to determine taxonomies. Abundances were recovered by mapping the demultiplexed reads to the UPARSE OTUs.
Fig. 1
Fig. 1

Rarefaction curves for bacterial operational taxonomic units of the fecal microbiome. The horizontal axis indicates sequence depth while the vertical axis indicates the number of bacterial operational taxonomic units (OTUs). All 155 fecal specimens had sufficient depth to obtain high degree of sequence coverage (rarefaction cutoff, 1470 reads/specimen)

To infer the functional capabilities of the fecal microbiome based on the OTU (taxonomy) data, we used a bioinformatic approach, Tax4Fun [18]. This approach links the 16S rRNA gene sequences with the functional annotation of sequenced bacterial genomes by identifying a nearest neighbor based on a minimal 16S rRNA gene sequence similarity. Next, the predicted metagenomes were categorized by function at the Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog and pathway levels [19]. We tested for significant differences in the functional category abundances between cases and controls using Welch’s unequal variances t test. Resulting P values were adjusted for multiple hypothesis testing by converting to false discovery rate q values using the Benjamini–Hochberg procedure, with q values of <0.05 considered statistically significant. To validate the findings, we performed random permutation testing with 1000 permutations for each of the pathways of interest, which corresponds to the situation when the abundance of pathways is randomly assigned to cases and controls contained in dataset. Once the dataset was permuted, we tested for the differences in abundances between cases and controls. We repeated the randomization 1000 times and recorded the squared error of the models averaged for every repetition. Additionally, to further examine the differences in the pathways of interest, we constructed multivariable linear regression models adjusting for potential confounders (age, sex, race/ethnicity, maternal antibiotic use during pregnancy, history of prematurity, mode of delivery, feeding status, and lifetime history of antibiotic and corticosteroid use), based on a priori knowledge [5, 6, 9]. Furthermore, to determine the relationship between the abundance of bacteria genus and metabolic pathways of interest, we examined their correlations with the use of scatterplots fitting locally weighted scatterplot smoothed (LOWESS) curve as well as Spearman’s correlation. The analyses used R version 3.3 with phyloseq package [20] and STAMP version 2.1 [21].

Results

At the four participating hospitals, a total of 40 infants hospitalized for bronchiolitis (cases) and 115 age-matched healthy infants (controls) were enrolled (Table 1). Overall, the median age was 3 months (IQR, 2–5 months) and 55% were male. All 155 fecal specimens had sufficient depth to obtain high degree of sequence coverage (rarefaction cutoff, 1470 reads/specimen; Fig. 1). The fecal microbiome were dominated by four genera: Escherichia (22%), Bifidobacterium (19%), Enterobacter (15%), and Bacteroides (13%). The characteristics of the fecal microbiome differed between cases and controls (Table 2). For example, infants with bronchiolitis had a higher proportion of Bacteroides-dominant profile and lower proportion of Enterobacter/Veillonella-dominant profile, compared to healthy controls (P = 0.01).
Table 1

Patient characteristics of 40 cases (infants with bronchiolitis) and 115 controls (healthy infants) at enrollment

Characteristics

Infants with bronchiolitis n = 40

Healthy control infants n = 115

P value* 

Demographics

 Age (mo), median (IQR)

3.2 (1.6–4.9)

3.8 (2.0–4.9)

0.52

 Male sex

22 (55)

64 (56)

0.99

  Race/ethnicity

  

0.04

  Non-hispanic white

23 (58)

61 (53)

 

  Non-hispanic black

6 (15)

11 (10)

 

  Hispanic

10 (25)

19 (17)

 

  Other

1 (3)

24 (21)

 

Prenatal history

 Parental history of asthma

16 (40)

21 (18)

0.01

 Maternal smoking during pregnancy

8 (20)

3 (3)

0.001

 Maternal antibiotic use during pregnancy

11 (28)

13 (11)

0.02

 Maternal antibiotic use during labor

12 (30)

35 (30)

0.82

Past medical history and home environmental characteristics

 Mode of birth, C-section

9 (23)

43 (37)

0.13

 Prematurity (32–37 weeks)

12 (30)

11 (10)

0.004

 Previous breathing problems before enrollment†

8 (20)

0 (0)

<0.001

 History of eczema

8 (20)

17 (15)

0.56

 Ever attended daycare

9 (23)

14 (12)

0.16

 Smoking exposure at home

8 (20)

4 (3)

0.002

 Mostly breastfed for the first 3 months of age

16 (40)

89 (77)

0.009

 Systemic antibiotic use before enrollmentb

8 (20)

13 (11)

0.24

 Systemic corticosteroid use before enrollment

9 (23)

0 (0)

<0.001

Clinical course

 Systemic antibiotic use during pre-hospitalization visit

8 (20)

 Systemic corticosteroid use during pre-hospitalization visit

3 (8)

 Hospital length-of-stay (day), median (IQR)

3 (2–4)

 Admission to intensive care unit

8 (20)

 Use of mechanical ventilationa

5 (13)

Data are no. (%) of infants unless otherwise indicated. Percentages may not equal 100 because of missingness or rounding

IQR interquartile range

* Chi square, Fisher exact, or Wilcoxon-Mann–Whitney tests, as appropriate

aDefined as an infant having cough that wakes him/her at night and/or causes emesis, or when the child has wheezing or shortness of breath without cough

bLifetime use of systemic antibiotic use before the enrollment. Infants with systemic antibiotic treatment in the preceding 7 days were not enrolled to the control group

Table 2

Richness, alpha-diversity, and relative abundance of fecal microbiome in infants with bronchiolitis and healthy controls

 

Infants with bronchiolitis n = 40

Healthy control infants n = 115

P value

Richness, median (IQR)

 Number of genera

17 (13–23)

13 (10–18)

0.004

Alpha-diversity, median (IQR) shannon index

2.21 (1.68–2.65)

1.93 (1.44–2.49)

0.27

Relative abundance of 10 most abundant genera, mean (standard deviation)

 Escherichia

0.21 (0.24)

0.23 (0.26)

0.91*

 Bifidobacterium

0.16 (0.20)

0.20 (0.21)

0.49*

 Enterobacter

0.10 (0.21)

0.17 (0.24)

0.27*

 Bacteroides

0.20 (0.23)

0.10 (0.19)

0.10*

 Veillonella

0.03 (0.09)

0.06 (0.12)

0.31*

 Lachnospiraceae incertae sedis

0.06 (0.10)

0.04 (0.10)

0.49*

 Streptococcus

0.02 (0.09)

0.03 (0.05)

0.91*

 Clostridium sensu strictos 1

0.01 (0.01)

0.03 (0.06)

0.16*

 Enterococcus

0.01 (0.03)

0.02 (0.04)

0.48*

 Akkermansia

0.02 (0.09)

0.02 (0.08)

0.91*

Microbiome profile, n (%)

  

0.01

 Bacteroides-dominant profile

19 (48)

24 (21)

 

 Bifidobacterium-dominant profile

6 (15)

26 (23)

 

 Escherichia-dominant profile

10 (25)

36 (31)

 

 Enterobacter/Veillonella-dominant profile

5 (12)

29 (25)

 

IQR interquartile range

* Benjamini–Hochberg corrected false discovery rate (q value) accounting for multiple comparisons

Between the infants with bronchiolitis and healthy controls, we compared the functional potential of fecal microbiome inferred from the 16S rRNA gene sequencing data. Of 6402 KEGG orthologs (orthologous genes) surveyed, the abundances of 319 genes were significantly different (q < 0.05; Table 3). The functional differences involved genes with diverse metabolic functions—e.g., carbohydrate, amino acid, and lipid metabolism. To make the data presentation and interpretation more meaningful, the genes were further consolidated into 274 KEGG pathways. Among these, there were significant differences between bronchiolitis cases and healthy controls for 37 pathways, including lipid metabolic pathways (q < 0.05; Table 4; Fig. 2). Particularly, the fecal microbiome of bronchiolitis cases had consistently higher abundances of gene function related to the sphingolipid metabolic pathways compared to that of controls (all q < 0.05)—i.e., sphingolipid (ko00600) and glycosphingolipid (ko00603, ko00604) metabolic pathways (Fig. 3). For each of these 3 pathways, the permutation test was significant (all random permutation P < 0.05), supporting the validity of the observed between-group differences. In the multivariable models adjusting for 9 patient-level factors (age, sex, race/ethnicity, maternal antibiotic use during pregnancy, history of prematurity, mode of delivery, feeding status, and lifetime history of antibiotic and corticosteroid use), the difference in the abundances of 3 sphingolipid metabolic pathways remained significant (all P < 0.05). Additionally, these pathways were more abundant in infants with Bacteroides-dominant microbiome profile compared to the other microbiome profiles (all q < 0.001; Fig. 4). Likewise, there was a positive correlation between the abundance of Bacteroides genus and each of the 3 sphingolipid metabolic pathways (all P < 0.001; Fig. 5; Table 4).
Table 3

Predicted KEGG orthologs with significant differences in relative abundance between infants with bronchiolitis and healthy controls

KEGG orthologs

Mean abundance in cases (%)

Mean abundance in controls (%)

Raw P value

FDR corrected q value

K00179; indolepyruvate ferredoxin oxidoreductase, alpha subunit [EC:1.2.7.8]

0.020

0.009

<0.001

0.026

K00180; indolepyruvate ferredoxin oxidoreductase, beta subunit [EC:1.2.7.8]

0.007

0.003

<0.001

0.028

K02489; two-component system, cell cycle sensor kinase and response regulator [EC:2.7.13.3]

0.004

0.002

0.001

0.038

K03319; divalent anion:Na+ symporter, DASS family

0.019

0.044

<0.001

0.028

K08082; two-component system, LytT family, sensor histidine kinase AlgZ [EC:2.7.13.3]

0.011

0.005

0.001

0.041

K08196; MFS transporter, AAHS family, cis, cis-muconate transporter

0.001

0.002

0.002

0.044

K10715; two-component system, sensor histidine kinase RpfC [EC:2.7.13.3]

0.009

0.005

0.001

0.036

K10916; two-component system, CAI-1 autoinducer sensor kinase/phosphatase CqsS [EC:2.7.13.3 3.1.3.-]

0.001

0.000

0.001

0.031

K11382; MFS transporter, OPA family, phosphoglycerate transporter protein

0.006

0.015

0.000

0.028

K11383; two-component system, NtrC family, sensor histidine kinase KinB [EC:2.7.13.3]

0.001

0.000

0.001

0.034

K11520; two-component system, OmpR family, manganese sensing sensor histidine kinase [EC:2.7.13.3]

0.001

0.000

0.000

0.028

K11527; two-component system, unclassified family, sensor histidine kinase and response regulator [EC:2.7.13.3]

0.023

0.011

0.001

0.038

K15850; two-component system, autoinducer 1 sensor kinase/phosphatase LuxN [EC:2.7.13.3 3.1.3.-]

0.001

0.000

<0.001

0.026

K15913; UDP-4-amino-4,6-dideoxy-N-acetyl-d-glucosamine 4-acetyltransferase [EC:2.3.1.-]

0.000

0.000

0.002

0.049

K16014; ATP-binding cassette, subfamily C, bacterial CydCD

0.005

0.010

0.002

0.042

K00176; 2-oxoglutarate ferredoxin oxidoreductase subunit delta [EC:1.2.7.3]

0.001

0.001

0.001

0.035

K00177; 2-oxoglutarate ferredoxin oxidoreductase subunit gamma [EC:1.2.7.3]

0.010

0.005

0.001

0.041

K00200; formylmethanofuran dehydrogenase subunit A [EC:1.2.99.5]

0.001

0.000

0.001

0.038

K00316; spermidine dehydrogenase [EC:1.5.99.6]

0.000

0.000

0.002

0.046

K00406; cytochrome c oxidase cbb3-type subunit III

0.002

0.004

0.001

0.030

K00436; hydrogen dehydrogenase [EC:1.12.1.2]

0.002

0.001

<0.001

0.023

K00824; d-alanine transaminase [EC:2.6.1.21]

0.004

0.008

0.002

0.045

K00832; aromatic-amino-acid transaminase [EC:2.6.1.57]

0.010

0.020

<0.001

0.026

K00856; adenosine kinase [EC:2.7.1.20]

0.002

0.001

0.001

0.034

K00908; Ca2+/calmodulin-dependent protein kinase [EC:2.7.11.17]

0.001

0.000

<0.001

0.023

K01235; alpha-glucuronidase [EC:3.2.1.139]

0.017

0.008

0.002

0.041

K01601; ribulose-bisphosphate carboxylase large chain [EC:4.1.1.39]

0.003

0.010

0.002

0.042

K01841; phosphoenolpyruvate phosphomutase [EC:5.4.2.9]

0.008

0.004

0.002

0.049

K01906; 6-carboxyhexanoate–CoA ligase [EC:6.2.1.14]

0.003

0.007

0.001

0.037

K01912; phenylacetate-CoA ligase [EC:6.2.1.30]

0.037

0.018

0.001

0.041

K02121; V-type H+ -transporting ATPase subunit E [EC:3.6.3.14]

0.010

0.005

<0.001

0.024

K02655; type IV pilus assembly protein PilE

0.002

0.005

<0.001

0.023

K03330; glutamyl-tRNA (Gln) amidotransferase subunit E [EC:6.3.5.7]

0.001

0.003

0.001

0.042

K03404; magnesium chelatase subunit D [EC:6.6.1.1]

0.007

0.019

0.002

0.046

K03756; putrescine:ornithine antiporter

0.007

0.016

<0.001

0.024

K04561; nitric oxide reductase subunit B [EC:1.7.2.5]

0.006

0.017

<0.001

0.026

K05586; bidirectional [NiFe] hydrogenase diaphorase subunit [EC:1.6.5.3]

0.001

0.000

0.001

0.032

K05588; bidirectional [NiFe] hydrogenase diaphorase subunit [EC:1.6.5.3]

0.001

0.000

0.002

0.043

K05589; cell division protein FtsB

0.002

0.003

<0.001

0.024

K05989; alpha-l-rhamnosidase [EC:3.2.1.40]

0.056

0.028

0.002

0.045

K06138; pyrroloquinoline quinone biosynthesis protein D

0.001

0.000

0.001

0.034

K07326; hemolysin activation/secretion protein

0.001

0.003

<0.001

0.026

K07536; 2-ketocyclohexanecarboxyl-CoA hydrolase [EC:3.1.2.-]

0.001

0.002

0.001

0.041

K09002; hypothetical protein

0.006

0.018

0.001

0.041

K09020; ureidoacrylate peracid hydrolase [EC:3.5.1.110]

0.002

0.003

0.001

0.031

K09162; hypothetical protein

0.003

0.008

0.001

0.034

K09459; phosphonopyruvate decarboxylase [EC:4.1.1.82]

0.005

0.003

0.003

0.050

K09477; citrate:succinate antiporter

0.008

0.016

0.001

0.034

K09758; aspartate 4-decarboxylase [EC:4.1.1.12]

0.015

0.007

<0.001

0.026

K09800; hypothetical protein

0.025

0.053

<0.001

0.026

K09824; hypothetical protein

0.007

0.014

<0.001

0.026

K10960; geranylgeranyl reductase [EC:1.3.1.83]

0.001

0.000

<0.001

0.027

K10974; cytosine permease

0.007

0.016

<0.001

0.026

K11016; hemolysin

0.001

0.002

0.001

0.029

K11106; l-tartrate/succinate antiporter

0.009

0.020

<0.001

0.026

K11607; manganese/iron transport system ATP-binding protein

0.004

0.009

<0.001

0.022

K11707; manganese/zinc/iron transport system substrate-binding protein

0.003

0.007

0.001

0.041

K11708; manganese/zinc/iron transport system permease protein

0.003

0.007

0.002

0.042

K11709; manganese/zinc/iron transport system permease protein

0.004

0.008

0.001

0.034

K11719; lipopolysaccharide export system protein LptC

0.003

0.007

<0.001

0.023

K11931; biofilm PGA synthesis lipoprotein PgaB [EC:3.-.-.-]

0.008

0.016

<0.001

0.028

K12341; adhesin YadA

0.005

0.012

0.001

0.041

K12681; pertactin

0.001

0.002

0.002

0.041

K12982; heptosyltransferase I [EC:2.4.-.-]

0.001

0.002

<0.001

0.028

K13256; protein PsiE

0.003

0.006

<0.001

0.029

K13498; indole-3-glycerol phosphate synthase/phosphoribosylanthranilate isomerase [EC:4.1.1.48 5.3.1.24]

0.008

0.017

<0.001

0.022

K13818; molybdopterin-guanine dinucleotide biosynthesis protein

0.001

0.003

0.001

0.029

K14448; (2S)-methylsuccinyl-CoA dehydrogenase

0.002

0.001

0.001

0.032

K14564; nucleolar protein 56

0.001

0.000

0.002

0.043

K14665; amidohydrolase [EC:3.5.1.-]

0.002

0.004

0.001

0.031

K15125; filamentous hemagglutinin

0.032

0.092

0.000

0.024

K15669; d-glycero-alpha-d-manno-heptose 1-phosphate guanylyltransferase [EC:2.7.7.71]

0.002

0.001

0.001

0.031

K15905; nitrite oxidoreductase alpha subunit

0.001

0.003

0.001

0.041

K16201; dipeptide transport system permease protein

0.001

0.003

0.002

0.045

Of 6402 KEGG orthologs surveyed, the relative abundances of 319 genes were significantly different (FDR, q < 0.05) between infants with bronchiolitis and healthy controls. Of these, 74 orthologs with a ratio of abundance >2.0 are displayed

Table 4

Predicted KEGG pathways with significant differences in relative abundance between infants with bronchiolitis and healthy controls

KEGG pathway

Difference in relative abundance

Correlation with Bacteroides abundance

Mean abundance in cases (%)

Mean abundance in controls (%)

Raw P value

FDR corrected q value

Spearman’s rho

P value

ko00051; fructose and mannose metabolism

1.691

1.447

0.001

0.043

0.55

<0.001

ko00052; galactose metabolism

1.473

1.298

0.004

0.035

0.66

<0.001

ko00140; steroid hormone biosynthesis

0.098

0.063

0.007

0.049

0.66

<0.001

ko00190; oxidative phosphorylation

1.332

1.215

0.005

0.041

0.41

<0.001

ko00311; penicillin and cephalosporin biosynthesis

0.068

0.060

0.001

0.041

0.44

<0.001

ko00450; selenocompound metabolism

0.626

0.703

0.001

0.046

−0.61

<0.001

ko00460; cyanoamino acid metabolism

0.280

0.222

0.004

0.037

0.82

<0.001

ko00472; d-arginine and d-ornithine metabolism

0.001

0.002

0.004

0.037

−0.52

<0.001

ko00480; glutathione metabolism

0.678

0.764

0.001

0.035

−0.49

<0.001

ko00511; other glycan degradation

1.198

0.910

0.002

0.035

0.72

<0.001

ko00520; amino sugar and nucleotide sugar metabolism

2.751

2.450

0.002

0.037

0.77

<0.001

ko00523; polyketide sugar unit biosynthesis

0.196

0.163

0.002

0.035

0.80

<0.001

ko00531; glycosaminoglycan degradation

0.256

0.152

0.005

0.044

0.73

<0.001

ko00532; glycosaminoglycan biosynthesis

0.035

0.026

0.001

0.048

0.72

<0.001

ko00591; linoleic acid metabolism

0.120

0.113

0.006

0.047

0.52

<0.001

ko00600; sphingolipid metabolism

0.489

0.351

0.003

0.034

0.77

<0.001

ko00603; glycosphingolipid biosynthesis

0.134

0.097

0.003

0.031

0.73

<0.001

ko00604; glycosphingolipid biosynthesis

0.061

0.039

0.006

0.048

0.74

<0.001

ko00642; Ethylbenzene degradation

0.084

0.075

0.002

0.034

0.25

0.001

ko00660; C5-branched dibasic acid metabolism

0.171

0.191

<0.001

0.028

−0.50

<0.001

ko00940; phenylpropanoid biosynthesis

0.213

0.163

0.006

0.048

0.81

<0.001

ko00944; flavone and flavonol biosynthesis

0.015

0.009

0.002

0.036

0.77

<0.001

ko03015; mRNA surveillance pathway

0.003

0.001

0.003

0.032

0.80

<0.001

ko04141; protein processing in endoplasmic reticulum

0.063

0.049

0.006

0.049

0.69

<0.001

ko04142; lysosome

0.310

0.194

0.006

0.047

0.76

<0.001

ko04210; apoptosis

0.043

0.025

0.002

0.030

0.69

<0.001

ko04612; antigen processing and presentation

0.014

0.010

0.002

0.031

0.58

<0.001

ko04621; NOD-like receptor signaling pathway

0.056

0.042

0.001

0.040

0.74

<0.001

ko04721; synaptic vesicle cycle

0.001

0.000

0.002

0.033

0.58

<0.001

ko04725; cholinergic synapse

0.002

0.005

0.003

0.031

−0.12

0.14

ko04914; progesterone-mediated oocyte maturation

0.014

0.010

0.002

0.033

0.58

<0.001

ko04930; type II diabetes mellitus

0.026

0.028

0.001

0.036

−0.47

<0.001

ko04962; vasopressin-regulated water reabsorption

0.001

0.000

0.002

0.036

0.58

<0.001

ko05110; vibrio cholerae infection

0.001

0.004

0.006

0.046

−0.41

<0.001

ko05133; pertussis

0.356

0.609

0.002

0.029

−0.24

0.03

ko05211; renal cell carcinoma

0.013

0.019

<0.001

0.028

−0.58

<0.001

ko05215; prostate cancer

0.016

0.011

0.002

0.037

0.63

<0.001

Italics results are the pathways of interest (sphingolipid metabolic pathways)

Fig. 2
Fig. 2

Predicted KEGG pathways with significant differences in relative abundance between infants with bronchiolitis and healthy controls. Of 274 KEGG pathways surveyed, the relative abundance of 37 genes was significantly different (false discovery rate, q < 0.05) between infants with bronchiolitis and healthy controls

Fig. 3
Fig. 3

Box-whisker plots of the three sphingolipid metabolic pathways that distinguish between the fecal microbiome of infants with bronchiolitis and that of healthy controls. The predicted metagenome of fecal microbiome in infants with bronchiolitis had a higher abundance of the a ko00600 (q = 0.03), b ko00603 (q = 0.03), and c ko00604 (q = 0.048) pathways compared to that in healthy controls. The horizontal line represents the median; the bottom and the top of the box represent the 25th and the 75th percentiles; whiskers represent 5 and 95% percentiles

Fig. 4
Fig. 4

Box-whisker plots of the three sphingolipid metabolic pathways that distinguish four fecal microbiome profiles. The relative abundance of a ko00600, b ko00603, and c ko00604 pathways were consistently higher in infants with Bacteroides-dominant microbiome profile compared to the others (all q < 0.001). The four fecal microbiota profiles were derived using partitioning around medoids clustering method with Bray–Curtis distance. The optimal number of clusters was identified by the use of gap statistic. The horizontal line represents the median; the bottom and the top of the box represent the 25th and the 75th percentiles; whiskers represent 5 and 95% percentiles. BCP Bacteroides-dominant profile, BFP Bifidobacterium-dominant profile, ESP Escherichia-dominated profile, EVP Enterobacter/Veillonella-dominant profile

Fig. 5
Fig. 5

Correlations between the abundance of Bacteroides and the three sphingolipid metabolic pathways. There was a positive correlation between the abundance of Bacteroides and each of the three sphingolipid metabolic pathways. a ko00600 (Spearman’s r = 0.77; P < 0.001), b ko00603 (Spearman’s r = 0.73; P < 0.001), and c ko00604 (Spearman’s r = 0.74; P < 0.001). The fitted line represents locally weighted scatterplot smoothed (lowess) curve

Discussion

By predicting the functional potential of the fecal microbiome from 40 infants with bronchiolitis and 115 healthy age-matched controls enrolled in a case–control study, we found significant differences in the abundance of genes related to multiple metabolic pathways. Of these, the gene function related to sphingolipid metabolic pathways was consistently more abundant in the fecal microbiome of bronchiolitis cases compared to that of healthy controls. The current study extends the previously identified association of Bacteroides-dominated fecal microbiome profile with higher likelihoods of bronchiolitis by demonstrating the functional potential of the gut microbiome in infants.

Sphingolipids are a class of complex lipids containing a backbone of sphingoid bases. These lipids have long been known as structural components of human cell membranes and as a component of surfactant, but have more recently emerged as signaling molecules that modulate the host immune response and contribute to the pathogenesis of respiratory diseases, such as bronchiolitis, pneumonia, and asthma [7, 22]. While sphingolipids production is ubiquitous in eukaryotes, it is also produced by several bacteria genera such as Bacteroides, Prevotella, and Porphyromonas [12]. Recently, experimental models reported that Bacteroides-derived sphingolipids (e.g., α-galactosylceramide) play an important role in host immunomodulation similar to lipopolysaccharide (LPS), another family of bacteria-derived glycolipid. For example, Wieland Brown et al. demonstrated that Bacteroides-derived α-galactosylceramide binds to CD1d and activates mouse and human invariant natural killer T (iNKT) cells both in vitro and in vivo [12]. In contrast, An et al., using neonatal mouse models, found that treatment with a different Bacteroides-derived glycosphingolipids (GSL-Bf717) reduces the number of colonic iNKT cells and subsequent colonic inflammation [13]. Although reverse causation—e.g., bronchiolitis per se or treatments for bronchiolitis result in perturbation of the fecal microbiome—is also possible, these prior studies, coupled with our findings, collectively suggest that Bacteroides-dominant microbiome in the gut, through their sphingolipid production, may contribute to inappropriate immune responses and bronchiolitis pathogenesis in infants. Our data should encourage future investigations into the mechanisms linking the individual gut microbiome-derived metabolites to the host immune response in the gut and respiratory tract (the gut-lung axis).

In sum, on the basis of the predicted metagenome in this case–control study, we found significant differences in the functional potential of fecal microbiome between infants with bronchiolitis and healthy controls. Particularly, the fecal microbiome in infants with bronchiolitis had consistently higher abundances of gene function related to the sphingolipid metabolic pathways. Although causal inferences remain premature, our data may suggest a potential link between the bacteria-derived metabolites, modulations of host immune response, and development of bronchiolitis. Our findings should facilitate further metagenomic, metatranscriptomic, and metabolomic (including Bacteroides-derived galactosylceramide [13]) investigations into the role of gut microbiome in the bronchiolitis pathogenesis. Our data also encourage researchers to integrate these “omics” approaches with mechanistic evaluations in experimental models in order to develop new preventive and therapeutic strategies (e.g., microbiome modification) for infants with bronchiolitis.

Limitations

Our study has several potential limitations. First, the location of fecal sample collection differed between cases and controls. However, in both populations, the fecal samples were refrigerated immediately after collection and the literature reported that refrigeration is associated with no significant alteration in fecal microbiota composition [23]. Second, the functional potential of fecal microbiome was inferred from the 16S rRNA gene sequencing data rather than measured by metabolomics or metatranscriptomics, or from metagenomic sequencing. However, a study has shown a strong correlation between the predicted metagenome and metagenome sequencing data (r > 0.85) in the NIH Human Microbiome Project samples (including fecal samples) [18]. Third, the concentration of metabolites was not measured in the fecal samples. This is an important area for examination in our future work. Lastly, the study design precluded us from examining the succession of fecal microbiome and its relation to the development of respiratory disease in early childhood. To address this question, the study populations are currently being followed longitudinally to age 6 years, with fecal sample collections at multiple time-points.

Abbreviations

iNKT: 

invariant natural killer T

KEGG: 

Kyoto Encyclopedia of Genes and Genomes

MARC: 

Multicenter Airway Research Collaboration

OTU: 

operational taxonomic unit

Declarations

Authors’ contributions

KH carried out the statistical analysis, drafted the initial manuscript, and approved the final manuscript as submitted. CJS carried out the initial analyses, reviewed and revised the manuscript, and approved the final manuscript as submitted. RWL conceptualized and designed the study, enrolled the subjects, reviewed and revised the manuscript, and approved the final manuscript as submitted. JMM and CAC conceptualized and designed the study, obtained the funding, reviewed and revised the manuscript, and approved the final manuscript as submitted. NJA and JFP generated the microbiome data, carried out the initial statistical analysis, reviewed and revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors thank Pedro A. Piedra, MD (Baylor College of Medicine), Ashley F. Sullivan, MS, MPH (Massachusetts General Hospital), the site investigators at Massachusetts General Hospital, Alfred I. duPont Hospital for Children, Boston Children’s Hospital, and Kosair Children’s Hospital, and all of the study families for their contributions to the study.

Competing interests

Dr. Mansbach has provided bronchiolitis-related consultation for Regeneron. Drs. Ajami and Petrosino own shares at Diversigen Inc., a microbiome research company. The authors declare that they have no competing interests.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to the data sharing agreement (based on the informed consent) but may be available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The institutional review board at each of the participating hospitals approved the study. Written informed consent was obtained from the parent or guardian.

Funding

This study was supported by the Grants U01 AI-087881, R01 AI-114552, R01 AI-108588, R01 AI-127507, R21 HL-129909, and UG3 OD-023253 from the National Institutes of Health (Bethesda, MD). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors’ Affiliations

(1)
Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, 125 Nashua Street, Suite 920, Boston, MA 02114-1101, USA
(2)
Department of Molecular Virology and Microbiology, Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, TX, USA
(3)
Department of Medicine, Boston Children’s Hospital, Boston, MA, USA
(4)
Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA

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Copyright

© The Author(s) 2017

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