Sze et al. BMC Pulmonary Medicine (2016):4
DOI 10.1186/s12890-016-0303-4
RESEARCH ARTICLE
Open Access
The bronchial epithelial cell bacterial
microbiome and host response in patients
infected with human immunodeficiency
virus
Marc A. Sze1*, Stella Xu1, Janice M. Leung1, Emily A. Vucic2, Tawimas Shaipanich3, Aida Moghadam4,
Marianne Harris4,5,6, Silvia Guillemi4,5,6, Sunita Sinha7, Corey Nislow7, Darra Murphy8, Cameron Hague8,
Jonathon Leipsic8, Stephen Lam2, Wan Lam2, Julio S. Montaner6,9, Don D. Sin1,3 and S. F. Paul Man1,3
Abstract
Background: Chronic Obstructive Pulmonary Disease (COPD) is an important comorbidity in patients living with
human immunodeficiency virus (HIV). Previous bacterial microbiome studies have shown increased abundance of
specific bacterium, like Tropheryma whipplei, and no overall community differences. However, the host response to
the lung microbiome is unknown in patients infected with HIV.
Methods: Two bronchial brush samples were obtained from 21 HIV-infected patients. One brush was used for bacterial
microbiome analysis using the Illumina MiSeqTM platform, while the other was used to evaluate gene expression patterns
of the host using the Affymetrix Human Gene ST 2.0 array. Weighted gene co-expression network analysis was used to
determine the relationship between the bacterial microbiome and host gene expression response.
Results: The Shannon Diversity was inversely related to only one gene expression module (p = 0.02); whereas evenness
correlated with five different modules (p ≤ 0.05). After FDR correction only the Firmicutes phylum was significantly
correlated with any modules (FDR < 0.05). These modules were enriched for cilia, transcription regulation, and immune
response. Specific operational taxonomic units (OTUs), such as OTU4 (Pasteurellaceae), were able to distinguish HIV
patients with and without COPD and severe emphysema.
Conclusion: These data support the hypothesis that the bacterial microbiome in HIV lungs is associated with specific
host immune responses. Whether or not these responses are also seen in non-HIV infected individuals needs to be
addressed in future studies.
Keywords: Bronchial brushing, Bacterial microbiome, HIV, Lungs, Gene expression
Background
The increased susceptibility of patients infected with
human immunodeficiency virus (HIV) to lung diseases,
in particular chronic obstructive pulmonary disease
(COPD), has now been recognized in numerous
epidemiological studies [1–3]. Because cigarette smoke
exposure only partially explains this elevated risk [1], the
* Correspondence: marcsze@med.umich.edu
1
Centre for Heart Lung Innovation, St. Paul’s Hospital & Department of
Medicine, University of British Columbia, Rm 166 – 1081 Burrard St.,
Vancouver, BC V6Z 1Y6, Canada
Full list of author information is available at the end of the article
pathogenesis of comorbid lung disease in HIV is largely
a mystery. Investigation of the HIV lung bacterial microbiome using bronchoalveolar lavage fluid has suggested
a greater abundance of Tropheryma whipplei in the HIV
lung [4] and no significant impact of anti-retroviral therapy on the bacterial community composition in HIVinfected individuals [5]. However, the impact of the lung
microbiota on the pathogenesis of chronic lung diseases
such as COPD in HIV is unclear. Moreover, there is little information whether the lung microbiome is associated with significant host responses in the lungs.
© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Sze et al. BMC Pulmonary Medicine (2016):4
Although characterization of bacterial community
composition in a disease state is an important first step
in uncovering the possible clinical relevance of the lung
microbiome [4, 6], the next logical step is to discover
whether or not changes in the lung microbiome induce
a host response that may be important in disease pathogenesis. We have recently shown, using lung tissue samples from non-HIV infected individuals with COPD, that
shifts in the lung microbiome are associated with important changes in inflammatory response in these lungs
[7]. One important limitation of that study was that the
microbiome was characterized in a block of lung tissue
and as such cell-specificity could not be ascertained.
Moreover, this study did not include any patients with
HIV infection. Here, we extend these observations by
investigating the interactions between the host gene
expression response and the bacterial microbiome in
bronchial epithelial cells of small airways collected from
the same site in patients infected with HIV. The specific
aims of this study were to describe the bacterial community composition of the HIV bronchial epithelium and to
determine whether the bacterial microbiome of the HIV
bronchial epithelium is associated with specific gene
expression signatures of the host that may reveal the
underlying pathogenesis of chronic airways disease in
HIV-infected individuals.
Methods
Patient population
All subjects provided written informed consent for the
collection of cytologic brushings for research purposes
under the UBC Providence Health Care ethics protocol
H14-03267. Subjects were recruited from patients undergoing bronchoscopy for pulmonary nodules, masses, or
pneumonia (all conditions were diagnosed radiographically by computed tomography (CT) imaging at St. Paul’s
Hospital, Vancouver, BC). Entry criteria into the study included documented HIV-1 infection and ≥19 years of age.
All subjects performed spirometry according to the
American Thoracic Society/European Respiratory Society
guidelines [8] within three months, except for five subjects
who underwent bronchoscopy for acute infection. COPD
was defined by post-bronchodilator forced expiratory
volume in one second (FEV1)/forced vital capacity (FVC)
ratio of less than 70 %.
Patients underwent thoracic CT imaging using a 64 detector CT scanner (Discovery HD 750 or a VCT, GE
Healthcare, Milwaukee, WI). A central imaging core laboratory (SPH CT Corelab), blinded to spirometry and
clinical data, interpreted the CT images for emphysema
based on a modified method of Kazerooni, et al. [9].
Emphysema severity was qualitatively scored according to
an established algorithm (see Additional file 1). CT scans
were also qualitatively scored for respiratory bronchiolitis
Page 2 of 10
(none, trivial, mild, moderate, and severe) and bronchiectasis (presence or absence). Details on bronchoscopy and specimen collection can be found in the
Additional file 1. Bronchial epithelial cells were obtained from sites away from the acute infection,
masses or nodules.
Bacterial microbiome analysis
DNA was extracted using the Qiagen DNeasy Blood and
Tissue Kit (Qiagen, Toronto, Ontario) from both patient
samples and background negative environmental
controls. Total 16S load was quantified using a droplet
digital polymerase chain reaction (ddPCR) assay [10].
These background controls were used to assess whether
the bacterial community of the HIV samples were impacted by the instruments and reagents used during the
extraction and PCR process. To assess the 16S load
within the samples the average 16S load from the
negative controls were subtracted from each HIV 16S
sample. Touchdown PCR [11] of the 16S rRNA gene V4
region was used to generate a DNA template for sequencing. Cycle conditions for the touchdown PCR can be
found in the Additional file 1. Sequencing was performed on an Illumina MiSeqTM (Illumina, Redwood
City, CA, USA) with 2 × 250 paired end-read chemistry.
The protocol established by Kozich, et al. was used for
the sequencing and subsequent data cleanup within the
program mothur (V1.34.4) [12]. After processing,
sequence cleanup, and chimera removal, a total of
3,559,398 reads remained. Data analysis was performed
in R (V3.2.0) and R studio (V0.99.441) employing the
vegan (V2.3-0) package [13, 14]. In order to adequately
perform alpha and beta diversity analysis subsampling to
the lowest total reads (3164) was performed [15]. Along
with a 97 % similarity threshold, a total of 451 different
Operational Taxonomic Units (OTUs) were identified.
Sequence data has been deposited in the NCBI sequence
read archive under the accession number SRP068430.
The corresponding metadata can be found at http://www.
ncbi.nlm.nih.gov/Traces/study/?acc=SRP068430&go=go.
Alternatively, one can use the following link http://www.
ncbi.nlm.nih.gov/Traces/study/ and search for SRP068430
to bring up the relevant information needed. The relevant
samples used for this study are BIDC1-4, BIDC7-10,
BIDC14-26 for the HIV samples and Neg1-4 for the background negative control samples.
Microarray analysis
RNA was extracted using the Qiagen RNeasy Plus
Universal Kit (Qiagen, Toronto, Ontario). 1 ug of RNA
was processed and hybridized onto the Affymetrix
Human Gene ST 2.0 array (Affymetrix Inc, Santa Clara,
USA) according to the manufacturer’s protocol at the
Hospital for Sick Children, Centre for Applied Genomics
Sze et al. BMC Pulmonary Medicine (2016):4
(Toronto, Ontario). Raw CEL files were processed and
RMA normalized in R (V3.2.0) and R studio (V0.99.441)
using a standard protocol from the oligo package
(V1.32.0) [16]. Gene symbols and names were obtained
from the hugene20sttranscriptcluster.db from bioconductor [17].
Data analysis and integration
For phyla level comparisons a t-test with Bonferroni correction was applied. A random forest algorithm with
Boruta feature selection [18, 19] was used to identify any
OTUs that could be discriminative of specific clinical
traits in the patient population (e.g. smoking status,
COPD, etc.). Traits were chosen for Boruta feature
selection analysis based on whether or not their
PERMANOVA value was less than or equal to 0.1. We
determined differences in the bacterial community
composition between groups by a Bonferroni corrected
PERMANOVA [20] of ≤ 0.0125. Robustly co-expressed
sets of genes (i.e. modules) were identified in airway
expression data using a weighted gene co-expression
network analysis (WGCNA) [21, 22]. Modules eigengene
vector values were then compared to alpha diversity
measures (Shannon Diversity, OTU richness, and
evenness) and phyla measures using the WGCNA
(V1.46) R package [21, 22]. For this analysis no grouping
was performed by smoking, CD4 cell count, or viral load
status as these variables were not significantly associated
with microbiome measures. The Database for Annotation, Visualization, and Integrated Discovery (DAVID)
[23] was used to identify the most relevant pathway clusters for each module that were significantly correlated to
the specific bacterial microbiome measurements. The
top 10 enrichment clusters were used as a guide to discovering pathways that were most strongly associated
with each module. A false discovery rate (FDR) of less
than 0.05 was considered significant. In addition to this
module comparison versus microbiome metrics for our
network used p-values of less than 0.05 as well. The
OTU data was reported to the lowest taxonomic identification, either within the text or in the respective figure.
Results
Overview of the bacterial microbiome in the HIV cohort
An overview of the clinical characteristics of the study
subjects showed that all individuals were between 40
and 75 years of age with a majority on highly active antiretroviral therapy (HAART) at the time of assessment
[Table 1]. The total 16S concentration in each subject
following background negative control subtraction was
0.42 ± 1.39 16S/ng of DNA (mean ± standard deviation).
The Shannon Diversity was 2.13 ± 0.54, OTU richness
was 37.52 ± 11.83, and evenness was 0.59 ± 0.13 for this
patient population (mean ± standard deviation). The
Page 3 of 10
distribution of Shannon Diversity, OTU richness, and
evenness can be found in the Additional file 1: Table S1.
On a cursory overview, the phyla distribution seems to
be quite different than the experimental background
negative controls. However, there was only a significant
difference in the relative abundance of the Actinobacteria phylum, following Bonferroni correction, between the HIV group and background negative controls
(p = 0.003) [Fig. 1a]. This would suggest that apart from
the Actinobacteria phylum the other phyla distributions
are similar to the background negative controls. A total
of 23.8 % of HIV subjects contained OTUs that aligned
to Tropheryma.
Airway microbiome comparisons between those with and
without COPD by spirometry
There was no difference in Shannon Diversity, evenness,
and OTU richness between HIV patients with and
without COPD (p > 0.05). The diagnosis of COPD had
no influence on the phyla observed (p > 0.05) [Fig. 1b].
However, in COPD, there was a trend towards greater
abundance of the Actinobacteria and Proteobacteria
phyla. Using a Bray-Curtis dissimilarity matrix and
Non-Metric Multidimensional Scaling (NMDS) with
PERMANOVA, we found no significant difference in the
bacterial community composition between those with
and without COPD (PERMANOVA = 0.10) [Fig. 2a].
However, analysis of specific OTUs in relation to COPD
status revealed 3 OTUs that were able to discriminate
HIV patients with and without COPD [Fig. 2b]: OTU4
(Pasteurellaceae), OTU15 (Brachybacterium), and
OTU38 (Yersinia). In COPD samples, there was a paucity of OTU4 and OTU15, and a slight enrichment of
OTU38 [Fig. 2b]. Ribosomal database classifier [24, 25]
revealed that OTU4 contained sequences of bacteria in
the genus for Haemophilus
Airway microbiome comparisons based on CT presence of
emphysema or bronchiectasis
We did not detect a significant difference in the bacterial
community composition based on severe emphysema
status that was detected on CT scans (PERMANOVA =
0.06) [Fig. 2c]. However, there were two OTUs that
discriminated samples from those with and without
emphysema: OTU4 (Pasteurellaceae-Haemophilus) and
OTU30 (Pedobacter). There was no difference in bacterial community composition in relation to emphysema
distribution (whether centrilobular or paraseptal) or
across respiratory bronchiolitis severity (both PERMANOVA > 0.10) (data not shown).
In those with bronchiectasis on CT, the bacterial community composition was also not significantly different from
those without any bronchiectasis (PERMANOVA = 0.04)
Sze et al. BMC Pulmonary Medicine (2016):4
Page 4 of 10
Table 1 An overview of clinical traits of HIV infected patients sampled in this study
Age
Vital
Status
Current VL
Current CD4
Bronchoscopy
Indication
Smoking
Status
PackYears
Current
HAART
CT
Emphysema
CT
Bronchiectasis
FEV1 (L)
FEV1/FVC
(%)
60-69
Alive
<40
400–499
Cancer
Current
30
Yes
Yes
Yes
1.53
33.04
50–59
Alive
1000–9999
100–199
Pneumonia
Current
39
No
Yes
No
1.11
90.00
70–79
Alive
<40
500–599
Cancer
Current
130
Yes
Yes
No
2.99
70.55
50–59
Alive
<40
600–699
Cancer
Current
12.5
Yes
Yes
No
3.35
56.63
50–59
Alive
40–1000
500–599
Cancer
Current
37.5
Yes
Yes
No
2.71
64.18
70–79
Alive
10000–99999
200–299
Cancer
Current
30
No
No
No
N/A
N/A
50–59
Alive
<40
900–999
Cancer
Current
15
Yes
No
No
3.33
70.95
50–59
Alive
10000–99999
200–299
Pneumonia
Current
19.5
No
Yes
No
N/A
N/A
60–69
Alive
<40
700–799
Cancer
Past
20
Yes
Yes
No
2.54
59.86
60–69
Alive
<40
800–899
Cancer
Past
3
Yes
Yes
No
2.87
71.44
60–69
Alive
40–999
200–299
Cancer
Past
45
Yes
No
No
2.41
76.80
60–69
Alive
<40
≥1000
Bronchiectasis
Past
12
Yes
No
Yes
3.06
70.57
60–69
Alive
<40
300–399
Pneumonia
Past
75
Yes
No
Yes
2.47
85
40–49
Alive
<40
100–199
Cancer
Current
30
Yes
Yes
No
2.41
51.56
40–49
Alive
10000–99999
100–199
Pneumonia
Current
115
Yes
Yes
Yes
2
78.28
50–59
Alive
<40
400–499
Cancer
Past
90
Yes
Yes
No
3.33
75.76
70–79
Deceased
<40
400–499
Cancer
Past
20
Yes
Yes
Yes
2.75
69.09
60–69
Deceased
<40
100–199
Cancer
Past
4
Yes
No
Yes
2.45
72.86
60–69
Deceased
<40
100–199
Cancer
None
0
Yes
Yes
No
N/A
N/A
50–59
Deceased
<40
100–199
Pneumonia
Current
N/A
Yes
Yes
No
N/A
N/A
40–49
Deceased
≥100000
<100
Cancer
None
0
No
Yes
No
N/A
N/A
[Fig. 2e]. Two OTUs were important for this discrimination: OTU1 (Prevotella), and OTU38 (Yersinia).
The impact of acute lung infection on airway microbiome
and host responses
We found no significant difference in the bacterial community composition between those with and without
pneumonia (PERMANOVA = 0.30) probably because the
bronchoscopic samples were obtained from the lung
contralateral to the site of active infection. We also
found no significant differences in bacterial community
composition between those with CD4 counts above or
below 200 (PERMANOVA = 0.84), across smoking status
(current, past, or never smokers; PERMANOVA = 0.37),
or whether Tropheryma was detected or not (PERMANOVA = 0.16) [Additional file 1: Figure S1–S5].
Significant pathways in WGCNA modules that correlated
with the bacterial microbiome
The power measurement of 6 was used to create the
gene co-expression network and a single sample was
excluded since it was an extreme outlier [21, 22]
[Additional file 1: Figures S6 and S7]. This sample was
considered an outlier since on the hierarchical clustering
of the gene expression data it formed its own unique
branch on the tree versus all other samples [Additional
file 1: Figure S7]. DAVID was used to assess the most
relevant pathways involved in the WGCNA modules
that correlated with the bacterial microbiome. In total
there were 14/23 (60.8 %) gene expression modules that
correlated with at least one measure of the bacterial
microbiome [Table 2]. Most interesting were the
immune pathways identified by the Tan, Red, Pink, and
Green Yellow modules and the cilia pathways represented by the Green module [Table 2].
WGCNA of alpha diversity and phyla with gene
expression
Our analysis revealed a number of gene expression modules that correlated with the Firmicutes phylum. It was
the only group that had significant correlations with any
modules after FDR correction (two negative and two
positive correlations). The negatively correlated modules
were Green (FDR = 0.037, p = 4 × 10-4), Midnight Blue
(FDR = 0.037, p = 8 × 10-4). The positively correlated
modules were, Brown (FDR = 0.037, p = 8 × 10-4), and
Blue (FDR = 0.037, p = 5 × 10-4) [Fig. 3 and Additional
file 1: Figure S9].
When looking at those that had a p value under 0.05
but not an FDR under 0.05 there were additional
Sze et al. BMC Pulmonary Medicine (2016):4
Page 5 of 10
Fig. 1 Breakdown of major phyla. a Comparison between HIV patient samples (n = 21) and background negative controls (n = 4). There was a
significant difference in the Actinobacteria phylum between HIV and background negative controls (p = 0.003). There were also slightly more
Proteobacteria in the HIV group than in the background negative controls (p > 0.05). b Comparison between HIV patients with (n = 6) and
without COPD (n = 10). No difference between the different phyla was observed (p > 0.05)
correlations that have been summarized in Fig. 3. Briefly,
the Tan module may be negatively correlated with both
Shannon Diversity and evenness (FDR = 0.283, p = 0.02
and FDR = 0.368, p = 0.03 respectively) [Fig. 3a]. Evenness may also be correlated with the Midnight Blue
module (FDR = 0.184, p = 0.009) [Fig. 3a]. The Proteobacteria phylum may also be positively correlated with
the Magenta module (FDR = 0.283, p = 0.02) and the
Turquoise module (FDR = 0.368, p = 0.03) [Fig. 3b].
WGCNA of the important OTUs and gene expression
No modules and OTUs found to be predictive by
random forest for COPD, severe emphysema, or
bronchiectasis were found above an FDR of 0.05. are described in the Additional file 1: Figure S10. However,
some correlations between modules and these OTUs occurred with a p-value under 0.05. This included OTU4
[Fig. 3c] which was negatively correlated with the Grey
(FDR = 0.941, p = 0.04), Green (FDR = 0.941, p = 0.04),
and Green Yellow (FDR = p = 0.03) modules. It was also
positively correlated with the Blue (p = 0.004) and Dark
Green (p = 0.04) modules.
Discussion
The interplay between the microbiome and host gene
expression is increasingly recognized as a key element of
health and disease. Our study extensively examined the
relationship between the bacterial microbiome and host
gene expression from bronchial epithelial cells taken
from the same small airways of patients infected with
HIV. We found that the small airway microbiome of
HIV-infected patients demonstrated only modest differences in the global bacterial community composition
compared with background negative controls [Fig. 1].
However, we did not find any significant differences in
the global airway bacterial composition between those
Sze et al. BMC Pulmonary Medicine (2016):4
Page 6 of 10
Fig. 2 Bacterial community composition and COPD status, severe emphysema, and bronchiectasis. a Non-metric multidimensional scaling analysis of
individuals with and without COPD, PERMANOVA = 0.10. b Boxplot of the relative abundance of each of the discriminative OTUs for COPD status.
c Non-metric multidimensional scaling analysis of individuals with and without severe emphysema, PERMANOVA = 0.06. d Boxplot of the relative
abundance of each of the discriminative OTUs for severe emphysema. e Non-metric multidimensional scaling analysis of individuals with and without
bronchiectasis, PERMANOVA = 0.04. f Boxplot of the relative abundance of each of the discriminative OTUs for bronchiectasis
with and without COPD, between those with elevated or
reduced CD4 counts, between those with bronchiectasis,
or between those with and without emphysema on CT
scans [Fig. 2]. However, when we investigated individual
OTUs using an unocrrected PERMANOVA threshold of
0.10 or below, we discovered OTU signatures that were
distinct for those with COPD (measured by spirometry),
severe emphysema (detected on CT), and bronchiectasis.
Spirometry-based COPD was associated with OTU4,
OTU15, and OTU38, severe emphysema was associated
with OTU4 and OTU30, while bronchiectasis was
associated with OTU1 and OTU38. More importantly,
we found that measures of the airway microbiome including alpha diversity measures, phyla, and OTUs, were
significantly related to distinct host response in the same
airway as captured by gene expression modules. Many of
these modules involved immune and inflammatory
responses, cell signaling, and cilia pathways suggesting
immunomodulatory role of the airway microbiota in the
host’s ability to process and remove irritants and aeropathogens. Additional work will be needed to validate
this hypothesis.
Table 2 An overview of significant gene expression module pathways
Gene Expression Module
Number of Genes
Pathway Identified
FDR
Tan
201
Lysosome, Immune Response, Plasma Membrane
<5.0 × 10-4
Red
554
Immune Response, Defense Response, Inflammatory
Response, Response to Wounding
<1.0 × 10-12
Midnight Blue
82
Magnesium Ion Binding
<0.05
Green
791
Cilia
<2 × 10-4
Turquoise
6050
Intracellular Organelle, Membrane-Enclosed Lumen
<2 × 10-7
Dark Green
43
None Identified
N/A
Black
452
Cell to Cell Signaling, Cell Membrane
<1.0 × 10-5
Magenta
365
Oxidation/Reduction, Microsomes
<2.0 × 10-2
Pink
427
Immune Response, Immunoglobulion, Antigen Presentation
<1.0 × 10-3
Brown
1274
Glycoprotein, Plasma Membrane, Immune Response
<1.0 × 10-6
Blue
5675
Nucleus, Transcription Regulation, Nuclear Lumen
<2 × 10-4
Grey
5861
Olfactory Transduction
<4.9 × 10-42
Green Yellow
245
Immunoglobulin, Antigen Processing and Presentation
<1 × 10-4
Light Green
63
None Identified
N/A
Sze et al. BMC Pulmonary Medicine (2016):4
Fig. 3 (See legend on next page.)
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Sze et al. BMC Pulmonary Medicine (2016):4
Page 8 of 10
(See figure on previous page.)
Fig. 3 Network of the module correlations with bacterial microbiome measures. a Gene expression modules and alpha diversity measures. b Gene
expression modules and bacterial phyla. c Gene expression modules and important OTUs for COPD, severe emphysema, and bronchiectasis. In
brackets under each module is a brief description of pathways identified by DAVID for genes in the module. Red represents significant positive
correlations while black represents significant negative correlations
Our findings may be consistent with previous studies on
the lung microbiome in HIV, which found Tropheryma
whipplei as a discriminative bacterium in bronchoalveolar
lavage fluid (BALF), occurring in 13.4 % of HIV subjects
versus only 1.3 % of HIV-uninfected subjects [4]. In our
study, which used bronchial brushes rather than BALF, we
demonstrated the presence of Tropheryma in 23.4 % of
the samples. Although these data are in line with the
previous literature, additional molecular studies such as a
qPCR assay targeting a gene specific for the species would
be needed to confirm that the Tropheryma we identified
was indeed T.whipplei. We extend the previous findings
by characterizing the host gene expression response to the
bacterial microbiome. For instance, we found that Shannon Diversity and evenness were negatively correlated
with genes involved with lysosome formation and immune
response. This finding is consistent with the evolving concept that reduction in bacterial diversity is associated with
an elevated risk of clinical infection and increased inflammatory response by the host [26, 27]. It should be noted
that certain organisms independent of their numbers, are
more likely to elicit an inflammatory response compared
with others that are less “pathogenic”. For instance, although in our study we found that bacteria in the Actinobacteria phylum were significantly more abundant in HIV
lungs than in the background negative environmental
controls, these bacteria were not significantly associated
with gene expression modules. In contrast, bacteria in the
Firmicutes phyla (though less abundant compared with
Actinobacteria) were significantly associated with several
different gene expression modules. Firmicutes were negatively related to pathways governing cilium and positively
associated with gene expression modules associated with
immune response and transcription regulation. When we
explored all correlations that had a p-value under 0.05
[Fig. 3] the Proteobacteria phylum was positively associated with gene expression pathways related to oxidation/
reduction and intracellular orgnaelles, whereas the Firmicutes phylum was negatively associated with these pathways. This data is consistent with a previous study which
reported a natural antagonism between the Firmicutes
and Proteobacteria phyla in the oropharynx [28]. These
data are also consistent with the evolving concept that the
lung microbiome is propagated by upper airway seeding
[29, 30]. We speculate that the host immune response is
regulated in the HIV lung by the seeding of certain organisms from the upper airways into the lower airway tract.
We posit that the predominance of Firmicutes phylum
leads to a heightened inflammatory state. Additional studies into the host interactions with the bacterial microbiome within the lung will need to be completed to
confirm this hypothesis.
Most intriguingly, we found that OTU4 (Pasteurellaceae-Haemophilus) was predictive of both COPD (by
spirometry) and severe emphysema (by CT). Previous
studies suggest that Haemophilus influenzae is an important pathogen in COPD [31] and a recent study using
whole lung tissue has shown that this organism is found
in control subjects but not in patients with GOLD 4 severity [7]. Consistent with this observation, in our study
Haemophilus spp was found in airways of patients without COPD by spirometry and without significant emphysema on CT scan [Figs. 2 and 3], although there were no
correlations under and FDR < 0.05 when exploring OTU
correlations with gene expression modules. Intriguingly,
those correlations that were under a p value of 0.05
showed that OTU4 negatively correlated with both pathways involved with cilia and antigen processing and
presentation [Fig. 3]. This raises the tantalizing hypothesis that up-regulation of immune genes which activate
the adaptive immune processes may enable processing
and removal of Haemophilus spp in the airways. However, this result would need to be validated in a study
with more power to asses this relationship. Upregulation of genes involved in cilia may have a similar
effect. We speculate that COPD airways have altered immune and/or cilia function that may prevent effective
clearance of Haemophilus spp. Additional work will be
needed to validate this hypothesis.
There are several limitations to this study. First, the
findings pertain exclusively to HIV-infected patients. Thus
it is possible that these OTUs may not help to distinguish
COPD, severe emphysema, or bronchiectasis in HIVuninfected patients. However, a recent study suggests that
the bacterial microbiome between HIV-uninfected
patients and HIV patients on successful antiretroviral
therapy may be similar [5]. Second, no oral wash was
performed prior to the bronchoscopy. This could have led
to minor contaminations of the bronchial brush samples.
However, it was reassuring that the findings of the present
study were consistent with others that used mouth rinsing
procedures [6, 29, 32]. Thirdly, all patients enrolled in this
study had a clinical indication for bronchoscopy. While
great care was taken to sample epithelial cells from uninfected regions of the lung, and far away from nodules or
masses, our results could be confounded by these
Sze et al. BMC Pulmonary Medicine (2016):4
underlying conditions. However, we did not find the bacterial species identified by routine clinical culture in those
patients with a diagnosis of pneumonia in the analysis of
16S, which would support that sampling was indeed from
the unaffected portions of the lung. This would explain
the fact that there was no difference in the bacterial community composition between those with and without
pneumonia. Another limitation is that we were unable to
validate the microarray expression results with RT-PCR
due to the large size of many of the modules. Thus it is
possible that some of the genes within the modules are
not accurate. However, a module is based on more than
one gene and in order for a module to be wrong the majority of gene expression values within it would have to be
incorrect. Finally, it is possible that some of the gene expression could be accounted for by infiltrating immune
cells that were taken along with the epithelial cells during
sampling. We cannot conclusively rule this possibility out
but samples in this study were obtained away from locations with signs of clear inflammation. Future studies in
which the bronchial epithelial cell microbiome is assessed
in asymptomatic HIV-infected individuals would help to
clarify the relationships between the microbiome and host
response, and in certain pulmonary phenotypes.
Overall, this study provides a preliminary investigation
into the host gene expression interaction with the bacterial microbiome in the small airways of HIV infected
individuals. It supports the hypothesis that diversity and
evenness of the community are important in modulating
inflammatory responses of the host. This study also
shows how bacteria in some phyla and OTUs may be
important in disease pathogenesis by modifying either
the host response and/or ecological niche areas. Our
work supports the possibility that specific interactions
between the bacterial microbiome and host cells within
the airways of the lung occur and may be associated
with distinct disease phenotypes; these findings would
require additional studies for validation.
Conclusions
In summary this study demonstrates that the bacterial
microbiome and host gene expression may interact with
one another in individuals with HIV infection. It identifies
pathways, such as the mucocillary transport system, as
important in the interaction between host and bacterial
microbiome. However, more research into this specific
area needs to be accomplished to confirm these results
and observations.
Page 9 of 10
Figure S3. NMDS of HIV patients based on smoking status. Figure S4.
NMDS of HIV patients based on whether Emphysema was present on CT
scan. Figure S5. NMDS of HIV patients based on whether Tropheryma
was present. Figure S6. Power threshold calculation for WGCNA. Figure S7.
Outlier detection for WGCNA. A single sample was removed (BIDC25) since it
clustered quite differently than the rest of the data set. Figure S8. Heatmap
showing that BIDC25 is also very different than other samples versus traits of
interest. Figure S9. Module trait relationship with alpha diversity measures
and phyla. Green squares represent negative correlations while red squares
represent positive correlations. The tow numbers displayed in each square is
the R-value correlation and P-value (number in brackets) respectively. Figure
S10. Module trait relationship with the important OTUs identified. Green
squares represent negative correlations while red squares represent positive
correlations. The two numbers displayed in each square is the
R-value correlations and P-value (number in brackets respectively).
(DOC 430 kb)
Abbreviations
16S: 16 Svedberg; BC: British Columbia; BIDC: Internal naming system for
patient samples; COPD: Chronic Obstructive Pulmonary Disease;
CT: Computed Tomography; DAVID: Database for Annotation Visualization
and Integrated Discovery; ddPCR: Droplet digital Polymerase Chain Reaction;
FDR: False discovery rate; FEV1: Forced expiratory volume in 1 second;
FVC: Forced vital capacity; HAART: Highly active antiretroviral therapy;
HIV: Human immunodeficiency virus; NCBI: Nationa Center for Biotechnology
Information; Neg: Negative; NMDS: Non-metric multidimensional scaling;
OTU: Operational taxonomic unit; PCR: Polymerase chain reaction;
PERMANOVA: Permutation analysis of variance; RMA: Robust multi-array
average; rRNA: Ribosomal ribonucleic acid; SPH: Saint Paul’s Hospital;
SRP: Sequence read project; UBC: University of British Columbia;
WGCNA: Weighted gene co-expression network analysis
Acknowledgements
None.
Funding
Funding was provided by the Canadian Institutes of Health Research grant
number 342422 and the BC Lung Association.
Availability of data and materials
Data for this study have been provided at http://www.ncbi.nlm.nih.gov/
Traces/study/ (search for SRP068430).
Authors’ contributions
Study conception and design: MAS, JML, EAV, SL, WL, DDS, SFPM. Data
acquisition: SX, JML, EAV, TS, AM, MH, SG, SS, CN, DM, CH, JL, SL, WL, JSM,
DDS, SFPM. Data analysis: MAS, JML, EAV. Manuscript writing: MAS, JML.
Manuscript editing: MAS, SX, JML, EAC, MH, SG, DM, CH, JL, SL, WL, JSM, DDS,
SFPM. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Consent to publish was not obtained, therefore key baseline demographic
information was summarized to prevent identification of subjects.
Ethics approval and consent to participate
All patients provided written informed consent to participate. The study was
approved by the University of British Columbia Providence Health Care
ethics board (Protocol H14-03267).
Author details
Centre for Heart Lung Innovation, St. Paul’s Hospital & Department of
Medicine, University of British Columbia, Rm 166 – 1081 Burrard St.,
Vancouver, BC V6Z 1Y6, Canada. 2Department of Integrative Oncology, BC
Cancer Research Centre, Vancouver, BC, Canada. 3Division of Respiratory
Medicine, St. Paul’s Hospital, University of British Columbia, Vancouver, BC,
Canada. 4AIDS Research Program, St. Paul’s Hospital, Vancouver, BC, Canada.
5
Department of Family Medicine, Faculty of Medicine, University of British
1
Additional file
Additional file 1: Table S1. Alpha diversity measures used in the
analysis. Figure S1. NMDS of HIV patients based on pneumonia status.
Figure S2. NMDS of HIV patients based on CD4 counts below 200.
Sze et al. BMC Pulmonary Medicine (2016):4
Columbia, Vancouver, BC, Canada. 6Division of HIV/AIDS, Department of
Medicine, University of British Columbia, Vancouver, BC, Canada. 7Faculty of
Pharmaceutical Sciences, Pharmaceutical Sciences Building, University of
British Columbia, Vancouver, BC, Canada. 8Department of Radiology and
Diagnostic Imaging, St. Paul’s Hospital, Vancouver, BC, Canada. 9British
Columbia Centre for Excellence in HIV/AIDS, St. Paul’s Hospital, Vancouver,
BC, Canada.
Received: 20 April 2016 Accepted: 27 October 2016
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