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Alveolar microbiota profile in patients with human pulmonary tuberculosis and interstitial pneumonia
Journal Pre-proof Alveolar microbiota profile in patients with human pulmonary tuberculosis and interstitial pneumonia Joel Armando Vázquez-Pérez, Concepción Ortega Carrillo, Marco Antonio Iñiguez García, Ivan Romero-Espinoza, José Eduardo Márquez García, Luisa I. Falcón, Martha Torres, María Teresa Herrera PII:
S0882-4010(19)30962-3
DOI:
https://doi.org/10.1016/j.micpath.2019.103851
Reference:
YMPAT 103851
To appear in:
Microbial Pathogenesis
Received Date: 31 May 2019 Revised Date:
19 September 2019
Accepted Date: 5 November 2019
Please cite this article as: Vázquez-Pérez JA, Carrillo ConcepcióOrtega, Iñiguez García MA, RomeroEspinoza I, Márquez García JoséEduardo, Falcón LI, Torres M, Herrera MaríTeresa, Alveolar microbiota profile in patients with human pulmonary tuberculosis and interstitial pneumonia, Microbial Pathogenesis (2019), doi: https://doi.org/10.1016/j.micpath.2019.103851. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Alveolar microbiota profile in patients with human pulmonary tuberculosis and
2
interstitial pneumonia
3 4
Joel Armando Vázquez-Pérez1 *, Concepción Ortega Carrillo2, Marco Antonio Iñiguez
5
García2, Ivan Romero-Espinoza1, José Eduardo Márquez García3, Luisa I. Falcón5,
6
Martha Torres3 and María Teresa Herrera4 *
7 8
1 Departamento de Virología, 2 Servicio de Broncoscopía, 3 Subdirección de
9
Investigación Biomédica, 4 Departamento de Investigación en Microbiología, Instituto
10
Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, 5 Laboratorio de
11
Ecología Bacteriana, Instituto de Ecología, Universidad Nacional Autónoma de México,
12
PCTY Yucatán
13 14 15 16
* Both authors contributed equally to this work
17 18
Corresponding author: María Teresa Herrera Barrios
19
Address: Departamento de Investigación en Microbiología, Instituto Nacional de
20
Enfermedades Respiratorias Ismael Cosío Villegas
21
Calzada de Tlalpan 4502, Col. Sección XVI, Ciudad de México, México, CP 14080
22
Phone: (52) 5487 17 00 Extension 5117, Fax: (52) 55 5487 1734.
23
Email: [email protected]
1
24
Abstract
25
Background. The presence of the human lung microbiota has been demonstrated in
26
patients with different lung diseases, mainly in sputum samples. However, for study of
27
the alveolar microbiota, a bronchoalveolar lavage (BAL) sample represents the lower
28
respiratory tract (LRT) environment. It is currently unknown whether there is a specific
29
alveolar microbiota profile in human lung diseases, such as pulmonary tuberculosis (TB)
30
and interstitial pneumonia (IP).
31
Methods. BAL samples from six active TB patients, six IP patients and ten healthy
32
volunteers were used for DNA extraction followed by amplification of the complete
33
bacterial 16S ribosomal RNA gene (16S rDNA). The 16S rDNA was sequenced with a
34
MiSeq Desktop Sequencer, and the data were analysed by QIIME software for
35
taxonomic assignment.
36
Results. The alveolar microbiota in TB and IP patients and healthy volunteers was
37
characterized by six dominant phyla, Firmicutes, Proteobacteria, Bacteroidetes,
38
Actinobacteria, Fusobacteria and Cyanobacteria. A significant reduction in the
39
abundance of Firmicutes was observed in IP patients. In TB and IP patients, the
40
diversity of the alveolar microbiota was diminished, characterized by a significant
41
reduction in the abundance of the Streptococcus genus and associated with increased
42
Mycobacterium abundance in TB patients and diminished Acinetobacter abundance in
43
IP patients with respect to their abundances in healthy volunteers. However, an
44
important difference was observed between TB and IP patients: the Fusobacterium
45
abundance was significantly reduced in TB patients. Exclusive genera that were less
2
46
abundant in patients than in healthy volunteers were characterized for each study
47
group.
48
Conclusions. This study shows that the alveolar microbiota profile in BAL samples
49
from TB and IP patients, representing infectious and non-infectious lung diseases,
50
respectively, is characterized by decreased diversity.
51 52 53 54 55 56 57 58 59 60 61 62 63 64
Keywords: Bronchoalveolar lavage, alveolar microbiota, pulmonary tuberculosis,
65
interstitial pneumonia, 16S rRNA, sequencing
66 67 68
3
69 70 71
Background
72
In the human body, bacterial communities are present in different sites, such as the
73
skin, vagina, oral cavity, gut and lung, and it has been estimated that 90% of the total
74
cells in the human body are bacteria 1. The gut, vagina, oral cavity and skin are the
75
most studied sites, producing important data on the human microbiota.
76
With respect to the lung microbiota, a few years ago, the lung was considered a “sterile”
77
compartment due to the limitations of traditional microbiological methods for isolating
78
and identifying all bacterial populations, with the lower respiratory tract (LRT) being
79
poorly studied.
80
However, with next-generation sequencing tools, it became possible to identify lung
81
bacterial populations in healthy volunteers and patients with different pulmonary
82
diseases
83
studies have focused on whether changes in the lung microbiota are associated with
84
specific respiratory diseases.
85
The study of the lung microbiota began in healthy volunteers and has demonstrated the
86
presence of a resident microbiota with similar taxa along the respiratory tract with a
87
decreased bacterial burden compared with that in the oral cavity
88
communities in the LRT are similar to those in the upper respiratory tract (URT) since
89
they are the result of the balance of dynamic bacterial immigration due to micro-
90
aspiration of gastric content, mucosa dispersion from oronasal cavities and air
91
inhalation, elimination by mucociliary clearance, coughing, immunity and the relative
2–6
. Because lung diseases represent a health problem worldwide, recent
2,7
. The bacterial
4
8,9
92
bacterial reproduction rates
. However, when this balance is altered in the lung by
93
different factors, such as temperature, oxygen tension, pH, nutrient availability,
94
concentration, activation of inflammatory cells and bacterial competition, important
95
changes have altered the microbiota composition and been associated with lung
96
diseases 9,10.
97
Regarding respiratory illness, several studies have investigated and described the lung
98
microbiota in patients with infectious diseases, such as pneumonia and tuberculosis
99
(TB), and non-infectious diseases, such as asthma, cystic fibrosis, smoking, chronic
100
obstructive pulmonary disease (COPD) and interstitial pneumonia (IP), with respect to
101
that in healthy volunteers to find a microbiota profile related to specific diseases 3–5,11,12.
102
In TB, sputum samples from patients have been used to characterize the lung
103
microbiota in Colombian, Chinese and Indian populations which was compared with
104
those from control group individuals
105
findings that mainly result from different characteristics of patients (with or without
106
treatment), control group selection or the kind of respiratory samples analysed (saliva or
107
thorax secretion). Additionally, differences can be due to the sequencing platform used,
108
16S rDNA variable regions selected (V1-V9) for the analysis and number of sequences
109
obtained per sample 16,17.
110
Sputum samples were mostly used for respiratory samples because they are easily
111
obtained through a non-invasive procedure; however, they are not representative of the
112
LRT and have potential contamination from the oral cavity microbiota.
11,13–15
. However, there are some controversial
5
113
For microbiota studies of the LRT, bronchoalveolar lavage (BAL) is the ideal sample
114
because it is more representative of the site than a sputum sample; however, it is
115
necessary to perform an invasive procedure to obtain a BAL sample18–20.
116
Until now, there has been only one report describing the alveolar microbiota in BAL
117
samples from TB patients after anti-TB treatment. In this study, the abundance of the
118
Mycobacterium and Porphyromonas genera was increased in TB lesions, while the
119
Cupriavidus genus was reported as dominant and specific in TB patients 21.
120
Additionally, there are several reports describing the microbiota in BAL samples from
121
individuals with non-infectious diseases, such as idiopathic pulmonary fibrosis, which
122
revealed decreased alveolar microbiota diversity in association with progression of the
123
diseases characterized by dominant phyla such as Firmicutes, Proteobacteria,
124
Bacteroidetes and Actinobacteria; thus, the potential utility of the microbiota profile can
125
be used for prognosis of this pathology 22,23.
126
In contrast, BAL samples from patients with other pulmonary diseases, such as
127
idiopathic IP, non-idiopathic IP, sarcoidosis, and Pneumocystis pneumonia, and from
128
healthy volunteers showed a predominance of the Prevotellaceae, Streptococcaceae
129
and Acidaminococcaceae families. Additionally, individuals in these categories have
130
similar α-diversity and β-diversity without any significant difference in microbiota
131
between the study groups 5.
132
To describe the alveolar microbiota composition involved in lung diseases and compare
133
infectious and non-infectious respiratory diseases, we analysed BAL samples from
134
active TB patients, IP patients and healthy volunteers. Additionally, to improve the
6
135
accuracy in taxonomy assignments, we analysed all 16S rRNA regions with high
136
sequencing depth.
137 138
Materials and Methods
139
Ethics statement
140
The Science and Ethical Committee of the Instituto Nacional de Enfermedades
141
Respiratorias Ismael Cosío Villegas in México City revised and approved this protocol.
142
Volunteers were invited to participate in the study, and they provided written informed
143
consent with authorization for the use of samples for future research.
144 145
Participants
146
Pulmonary TB patients (TB) (n=6), interstitial pneumonia (IP) patients (n=6) and healthy
147
volunteers (n=10) who were willing to volunteer to undergo bronchoalveolar lavage
148
(BAL) for diagnosis purposes were enrolled in the Pneumology Service at the Instituto
149
Nacional de Enfermedades Respiratorias in México City. The pulmonary TB patients
150
who met the following requirements were enrolled: radiographic findings, clinical
151
symptoms compatible with TB, negative human immunodeficiency virus 1 (HIV-1)
152
serology (n=6) and positive acid-fast bacilli (AFB) in the sputum (n=5) and BAL (n=1).
153
TB was confirmed later to be sputum culture-positive for drug-sensitive M. tuberculosis
154
(n=5), and BAL from one patient had positive GeneXpert/TB without resistance. All
155
patients had TB Class 3 according to American Thoracic Society 24. BAL was performed
156
before anti-TB treatment was started (Figure 1). The IP patients have a history of onset
157
with dyspnoea on large efforts, which progressed over time to dyspnoea on small
158
efforts. One of the symptoms referred to was a non-productive cough. Patients were
7
159
hospitalized and had routine laboratory studies, with negative HIV-1 and negative AFB
160
results in sputum. None of them received antibiotic treatment prior to bronchoscopy,
161
and no indication was recorded in their hospital clinical report. To establish the
162
diagnosis, BAL was performed. The BAL cultures for bacteria, fungi and mycobacteria
163
were negative. Additionally, the xTAG Respiratory Viral Panel FAST/FAST v2 (Luminex,
164
Corp., Austin, TX) by RT-qPCR was used to discard viral infections. Healthy volunteers
165
were HIV-1-negative without radiographic and clinical evidence of respiratory diseases
166
and with no contact with TB patients and were tuberculin skin test (TST) positive (n=3)
167
and TST negative (n=3) without any antibiotic treatment. The BAL cultures for bacteria,
168
fungi and mycobacteria were negative. (Figure 1)
169 170 171
Sample collection
172
Prior to bronchoscopy procedures, all volunteers underwent an oral wash with 0.12%
173
chlorhexidine gluconate solution for one minute (min) 3, and a Williams oral cannula was
174
used during bronchoscopies to prevent BAL sample contamination with the oral
175
microbiota. Briefly, after local anaesthesia of the upper airways with 2% lidocaine
176
solution and additional instillation of 1% lidocaine in the lower airways, a flexible
177
fibreoptic bronchoscope (P30, Olympus BF, New Hyde Park, NY) was wedged
178
consecutively into segment of the radiographically affected site in the TB patients and
179
into the middle lobe or the lingula in IP patients and healthy volunteers
180
sample was centrifuged, and the BAL fluid was stored at -80°C in a bank.
18
. The BAL
181
8
182
Alveolar sample concentration and DNA extraction
183
Ten millilitres of BAL fluid were concentrated by centrifugation at 4000 ×g for 20 min at
184
room temperature in a 50 ml Amicon Ultra-15 10K centrifugal filter tube (Merck Millipore
185
Ltd., Tullagreen, Ireland). The concentrated sample was transferred into a micro-
186
centrifuge tube, and DNA extraction was performed using a QIAmp Cador Pathogen
187
Mini Kit (Qiagen Co., Hilden, Germany) according the manufacturer’s instructions.
188
Briefly, buffer VXL containing carrier RNA and proteinase K was added to the
189
concentrated sample, followed by incubation at 56°C for 15 min at 1000 rpm in a
190
Thermo-Shaker (BioSan, USA).
191 192
Control samples
193
To ensure that the results presented in this study did not come from laboratory
194
contamination, we obtained samples from the laboratory bench, PCR cabinet and the
195
bronchoscope before the bronchoscopy procedure. All samples were processed with
196
the same DNA extraction protocol. A PCR negative control was included in all
197
amplifications (including all reagents except DNA).
198 199
Whole 16S ribosomal RNA amplification
200
The bacterial 16S rRNA gene (16S rDNA) was amplified using a primer set described
201
previously,
202
TACGGYTACCTTGTTACGACTT-3’)
203
primer, enzyme and the reagents of the Expand High Fidelity PCR System (Roche
204
Diagnostics, Indianapolis, IN) for a 50 µl reaction volume. The amplification reaction
27F (5’-AGAGTTTGATYMTGGCTCAG-3’)
and 1492R (reverse:
5’-
25
; 100 ng of DNA was mixed with 10 µM each
9
205
was performed in a Verity thermocycler (Applied Biosystems, San Jose, CA) with the
206
reaction at 95°C for 3 min followed by 35 cycles (9 5°C for 30 sec, 55°C for 30 sec and
207
72°C for 1.5 min) and a final extension at 72°C for 5 min.
208
Then, all the PCR product samples were mixed with loading buffer and Gel Red
209
(Biotium Inc., Fremont, CA) for staining and separated by electrophoresis in a 1.5%
210
agarose gel including a 500 bp DNA ladder molecular marker (Thermo Scientific,
211
Carlsbad, CA). The gel was observed under UV light in a Chemidoc MP Imaging
212
System (Bio-Rad Laboratories, Inc. Grand Junction, CO), and the amplified fragment
213
was purified using a QIAquick Gel Extraction Kit according to the manufacturer’s
214
instructions (Qiagen Co., Strasse, Germany). The purified fragments were stored at -
215
20°C and used for sequencing.
216 217
16S ribosomal library construction
218
Nextera XT libraries from whole 16S rDNA of each BAL sample were prepared following
219
the manufacturer’s protocol (Illumina Inc., San Diego, CA). Briefly, samples were
220
adjusted to 0.2 ng/µL DNA material per library using a Qubit dsDNA HS Assay Kit to
221
measure the DNA concentration (Invitrogen/Thermo Scientific, Eugene, OR) and then
222
fragmented and tagged via tagmentation. Each sample was indexed with a unique
223
combination of i5 and i7 sequences, and the size fragments of each library were verified
224
with a High Sensitivity DNA kit (Agilent Technologies Inc., Waldbronn, Germany) in a
225
Bioanalyzer 2100 (Agilent Technologies Inc.) followed by AMPure XP bead cleanup
226
(Beckman Coulter, Brea, CA). The library was normalized and loaded in a flow cell (2 x
10
227
250, Illumina Inc.), and sequencing was performed in a MiSeq Desktop Sequencer
228
(Illumina, Inc.) for paired-end reads 250 bp in length.
229 230
Sequence analysis and taxonomic assignment
231
Demultiplexing was performed with the default quality-filtering parameters, using R1
232
reads of each sample with a minimum quality score of 30. Sequences were then
233
analysed with QIIME version 1.8.0
234
Operational taxonomic unit (OTU) picking was performed with a closed reference
235
method by aligning the sequences without assembling to a reference in the Greengenes
236
13.8 database. OTUs were picked based on 97% sequence identity, and chimeric
237
sequences were removed using usearch61 (identify_chimeric_seqs_py). After picking
238
for OTUs, we obtained a total of 7,251,033 reads (ranging from 49,251 to 465,896
239
reads) with an average of 268,556 reads per sample and an average size of 190 nt.
240
OTUs were grouped at different levels of taxonomy classification (phylum, class, order,
241
family and genus) and normalized at each level to obtain the relative abundance using
242
the summarize_taxa_through_plots.py script. Phylogenetic trees were created using
243
FastTree2 under QIIME’s default parameters and were used for the calculation of
244
α−diversity and β-diversity metrics.
26
using the pick_open_reference_otus.py workflow.
245 246
α−Diversity analysis
247
To evaluate the diversity contained within groups, we employed rarefaction plots.
248
Rarefaction analysis was performed over 10,000 to 40,000 reads in depth with 10
249
subsampling times at each depth. Rarefaction curves were generated using the QIIME
11
250
workflow (alpha_rarefaction.py) for three diversity matrices: phylogenetic distance (PD),
251
Chao1 and observed OTUs. OTU-based α-diversity was estimated by each matrix. Non-
252
parametric tests were used to compare the statistical significance of the rarefaction
253
curves for TB, IP and healthy groups implemented in the QIIME function
254
(compare_alpha_diversity.py), as the data distribution was not normal. Phylogenetic
255
distance, the number of observed OTUs and Chao1 index at the rarefaction of 10,000
256
and 40,000 reads were compared.
257 258
β−Diversity analysis and distance comparison
259
To determine the amount of diversity shared between two communities, we employed
260
UniFrac
261
(jackknifed_beta_diversity.py) in QIIME to create distance matrices and rarefied
262
UPGMA trees and generate principal coordinates plots. Principal coordinates analysis
263
(PCoA) was applied to summarize UniFrac distance matrices and generate biplots.
264
Statistical analysis
265
Multiple comparisons tests regarding age, sex and TST were performed by ANOVA and
266
Dunn’s test, and their results were compared with proportions from the Z test. The non-
267
parametric Mann-Whitney U test was used for comparative analysis between phyla and
268
genera in our study groups. To compare the distances between groups, we created
269
distance comparison plots, and to determine if there was significance between
270
distances, we performed a two-sided Student´s two-sample t-test. A p value≤0.05 was
271
considered significant.
metrics.
β-Diversity
was
calculated
using
the
jack-knife
workflow
272
12
273
Results
274
Clinical data for patients and controls
275
BAL samples were collected from twenty-two adults: six active (TB) patients, six
276
interstitial pneumonia (IP) patients and ten healthy volunteers. The clinical and
277
demographic characteristics of the study participants are summarized in Table 1.
278 279
Microbiota composition
280
An average of 268,556 reads per sample from patients and healthy volunteers was
281
obtained. After analysis of relative abundance, we observed that the alveolar microbiota
282
was characterized by six dominant phyla: Firmicutes, Proteobacteria and Bacteroidetes,
283
followed by Actinobacteria, Fusobacteria and Cyanobacteria. These phyla were present
284
in our study groups but in different abundances (Figure 2b and Table 2). Firmicutes was
285
a phylum with higher relative abundance in healthy volunteers and TB patients than in
286
IP patients (44.6%, 35.4% and 27.2%, respectively). Moreover, Bacteroidetes was a
287
phylum with a non-significant higher relative abundance in IP patients (39.1%) than in
288
healthy volunteers (24.3%) and TB patients (21.9%).
289
Additionally, other phyla were present in the alveolar microbiota with less relative
290
abundance, and they are included in Table 2. Interestingly, we observed that phylum
291
richness was reduced in TB and IP patients in comparison with healthy volunteers
292
(TB=9, IP=10 and healthy=13). In Figure 2a, we show the relative abundance of the
293
phylum profile corresponding to each individual in the study groups.
294
Then, we focused on the phyla with a relative abundance equal to or greater than 0.1%,
295
and we observed a significant decrease in the abundance of Firmicutes in IP patients
13
296
compared with that in healthy volunteers (p<0.05). Although the decrease in Firmicutes
297
abundance was not significant in TB patients, we observed that the relative abundance
298
was lower than that in healthy volunteers (Figure 2b). Additionally, we observed that
299
Proteobacteria abundance was increased in TB and IP patients in comparison to
300
healthy volunteers, while Bacteroidetes abundance was increased in IP patients in
301
comparison with TB patients and healthy volunteers, but it was not significant.
302
We identified differences in microbial taxa between TB patients, IP patients and healthy
303
volunteers. First, the IP patients showed an inverted ratio for the relative abundances of
304
Firmicutes and Bacteroidetes compared with that of the TB patients and healthy
305
volunteers (Firmicutes: IP: 27.2% vs. TB: 35.4% and healthy 44.6%; Bacteroidetes: IP:
306
39.1% vs. TB: 21.9% and healthy 24.3%). Second, in the TB and IP patients, the
307
Proteobacteria phylum was enriched, while in healthy volunteers, its abundance was
308
reduced (TB=28.0% vs. IP=24.7% and healthy=13.4%).
309
Because changes in the lung microbiota are associated with diseases, we explored the
310
impact of the diseases on the alveolar microbiota profile at the genus level. In Figure 3a,
311
we show the relative abundances of genera in each individual volunteer. Comparison of
312
the relative abundances of genera between the study groups allowed us to identify
313
differences in the alveolar microbiota profile.
314
We analysed the twelve genera with a high relative abundance, and we found that the
315
Streptococcus abundance was significantly reduced in TB and IP patients (7.34% and
316
9.98%, respectively) in comparison with healthy volunteers (27.76%) (p<0.05), while
317
Prevotella and Veillonella were enriched in IP patients in comparison to TB patients and
318
healthy volunteers (IP: 28.38% and 9.89% vs. TB: 12.16% and 5.85% and healthy:
14
319
17.14% and 6.44%, respectively). We observed that the Fusobacterium abundance was
320
significantly reduced in TB patients (p<0.05) compared to IP patients but not healthy
321
volunteers (Figure 3b, Table 1 supplementary material).
322
In addition, we found that in comparison with healthy volunteers, TB patients exhibited a
323
significantly increased relative abundance of Mycobacterium, and IP patients exhibited
324
a significantly reduced Acinetobacter abundance (p<0.05).
325
In TB patients, the relative abundances of Lactobacillus (6.6%), Acinetobacter (7.61%),
326
Mycobacterium (6.45%) and Staphylococcus (1.93%) were higher than those in IP and
327
healthy volunteers. Our data showed reduced diversity of genera in TB and IP patients
328
in comparison with that in healthy volunteers (TB=44, IP= 50 and healthy=67,
329
respectively, Table 1 supplementary material, Figure 4).
330
Interestingly, we observed exclusive genera for each study group; for example, seven
331
genera were observed for the TB group (Table 3a), with Lactococcus and Leuconostoc
332
being the most abundant (1.14 and 0.94%), and six genera were observed for the IP
333
group (Table 3b), including Enterobacter, Paludibacter, Erwinia, Brevibacterium,
334
Citrobacter and Pantoea, all with less than 0.1% relative abundance, while twenty
335
genera were observed in the healthy group (Table 3c), with Peptoniphilus exhibiting
336
0.43% relative abundance and the other genera representing less than 0.1%.
337
However, there were other genera shared between the study groups; for example, the
338
TB group shared Butyrivibrio, Mycoplasma and Coprococcus with the IP group (Table
339
2a, Supplementary material) and shared five genera with the healthy group, including
340
Mycobacterium, Rothia, Acholeplasma, Corynebacterium and Moryella (Table 2b,
341
Supplementary material), while the IP and healthy groups shared eleven genera (Table
15
342
2c, Supplementary material). There were twenty-nine genera shared among the three
343
study groups (Table 2d, Supplementary material).
344 345
Diversity analysis
346
The diversity and richness of the 16S rDNA in each BAL sample were evaluated using
347
different α-diversity metrics. The means of the Chao1, observed OTU richness and PD
348
whole-tree diversity metrics in the TB group were lower (16,520, 10,530 and 564.74,
349
respectively) than those in the IP and healthy groups (Table 4). Additionally, the mean
350
Simpson and Shannon diversity index, which accounted for richness and evenness,
351
was higher in the TB group than in the IP (Shannon index, p<0.05) and healthy groups.
352
After conducting a rarefaction analysis, we found that a sufficient sequencing depth was
353
evident for each sample type, as illustrated by the rarefaction curves (Figure 4a, 4b and
354
4c).
355
UniFrac-based principal coordinates analysis (PCoA) revealed that TB and IP patients
356
and healthy volunteers did not cluster in clearly different groups. PCoA plots (Figure 5)
357
showed that all samples were divided into two clusters: cluster 1 contained 66.7% of IP
358
samples (IP1, IP3, IP4 and IP5), 50% of healthy samples (H1, H3, H4, H6 and H10) and
359
33.3% of TB samples (TB3 and TB6), while cluster 2 contained 66.7% of TB samples
360
(TB1, TB2, TB4 and TB5) and 50% of healthy samples (H2, H5, H7, H8 and H9). Two
361
samples (IP2 and IP6) and the two control samples did not belong to any cluster.
362
Moreover, when we compared the distances between the groups, there was no
363
statistically significant difference.
364
16
365 366 367 368
Discussion
369
The microbiota has important effects on the human body, and its early acquisition in
370
infants by breast milk represents a pivotal moment in modulating and developing the
371
immune system 27.
372
In the gut, Firmicutes and Bacteroidetes produce acetate, propionate and butyrate,
373
which act as energy sources and are involved in vital procedures such as stimulation of
374
growth, differentiation and mucin production of epithelial cells, lipogenesis, physiology,
375
homeostasis and development of the gut immune response 28,29.
376
Human health is characterized by a balance between the microbiota, metabolites and
377
the immune response, and an imbalance in these factors is associated with some
378
diseases and induces changes in the microbiota composition, although in some cases,
379
individual genetic factors are associated with and promote diseases 29.
380
Until now, the lung microbiota has been studied in mainly sputum samples, followed by
381
different secretions (alveolar, respiratory, nasal, oro-pharyngeal, pharyngeal), and
382
bronchial aspirates and saliva in pulmonary infections (TB and pneumonia) and non-
383
infectious diseases (asthma, cystic fibrosis, smoking, COPD and IP)17. BAL samples are
384
an alternative in studies describing the lower respiratory tract (LRT) microbiota,
385
although there is a limitation because it is difficult to obtain these samples and there is
386
the probability of sample contamination from the oral microbiota.
17
387
Unlike previous studies of lung diseases (TB, IP, COPD and cystic fibrosis) where one
388
or two variable regions from 16S rDNA have been sequenced with fewer than 50,000
389
reads per sample 4–6,11,14,15,21,23,30, we sequenced the complete 16S rDNA from BAL and
390
obtained an average of 268,556 reads per sample to obtain increased accuracy in
391
taxonomy assignments with a high sequencing depth.
392
Our results demonstrated that the alveolar microbiota profile in BAL samples from TB
393
and IP patients is characterized by a loss of microbiota diversity and also showed a
394
“bacterial core” composed of mainly Firmicutes, Proteobacteria, Bacteroidetes,
395
Actinobacteria, Fusobacteria and Cyanobacteria in the alveolar microbiota from TB and
396
IP patients and healthy volunteers, consistent with previous reports 22,31–33. The
397
abundance of these taxa in the oral microbiota has been described, but the bacterial
398
burden in the LRT is lower than that in the oral cavity 2,34,35.
399
Interestingly, we found a diminished relative abundance of Firmicutes in BAL samples
400
from IP and TB patients with respect to that of healthy volunteers, and these results are
401
in accordance with previous reports of sputum samples from Chinese pulmonary TB
402
patients
403
and Actinobacteria abundances were increased 15. These differences may be due to the
404
type of sample, study population, geographic region, diet, environmental conditions and
405
customs. Although there was no statistical significance, it was important to note the
406
increase in Proteobacteria abundance in TB and IP patients, and these changes could
407
be a consequence of M. tuberculosis infection or associated with inflammation in IP
408
patients.
14
but are contradictory to results from Indian TB patients, for whom Firmicutes
18
409
At the genus level, we found a decrease in the relative abundance of Streptococcus in
410
both TB and IP patients, indicating that microbiota changes were independent of
411
disease aetiology (infectious or not infectious). In particular, the microbiota profile in TB
412
patients was characterized by a Streptococcus abundance reduction associated with an
413
increase in Mycobacterium, Lactobacillus and Acinetobacter abundances. This profile
414
could be determined by the growth of M. tuberculosis and probably by the influence of
415
the immune response characterized by the production of pro-inflammatory cytokines,
416
such as IFN-γ and IL-12, because the inflammatory environment influences microbial
417
communities
418
release its virulence factors, ESAT-6 and CFP-10, causing a reduced macrophage
419
response by suppressing nitric oxide (NO) and reactive oxygen species (ROS)
420
production
421
were reduced, and Prevotella and Veillonella abundances increased. The difference
422
observed in microbiota profiles between TB and IP patients was a reduced
423
Fusobacterium abundance in TB patients. Additionally, it is important to note that
424
differences between patients were not determined by one genus because changes in
425
others genus were observed.
426
In addition, exclusive genera were found in each group; for example, in TB, seven
427
predominant genera were observed: Lactococcus, Leuconostoc, Streptomyces,
428
Nocardioides,
429
observation did not match with a previous report on BAL samples from a Chinese
430
population with TB in which Cupriavidus was a dominant and exclusive genus in TB
431
patients. However, this difference is most likely due to anti-TB treatment prior to BAL in
18
. In this environment, M. tuberculosis can compete for nutrients and
36
. Regarding IP patients, Streptococcus and Acinetobacter abundances
Desulfovibrio,
Rhodococcus
and
Sphaerochaeta.
However,
this
19
432
patients included in this study because antibiotics alter the microbiota diversity
433
composition
434
treatment prior to obtaining the BAL sample; thus, our findings were not affected by
435
antibiotic treatment, although IP patients received different drugs before the BAL
436
procedure. It is important to mention that the age of healthy volunteers was not paired
437
with the age of TB and IP patients, which is a limitation of our study.
438
Furthermore, IP patients had six exclusive genera (Enterobacter, Paludibacter,
439
Erwinia, Brevibacterium, Citrobacter and Pantoea), while healthy volunteers had twenty
440
exclusive genera, with a very low relative abundance for both groups (<0.1%).
441
Our results support a different profile in the alveolar microbiota associated with disease,
442
which is related to the fact that the aetiologies demonstrated in active TB and IP are
443
unique, sharing a lower bacterial diversity than healthy volunteers.
444
Similar results were shown in BAL samples from moderate and severe COPD and
445
idiopathic pulmonary fibrosis patients, with low microbiota diversity with predominant
446
Bacteroidetes, Firmicutes, Proteobacteria and Fusobacteria phyla and the presence of
447
Pseudomonas, Streptococcus, Prevotella, Fusobacterium and Veillonella genera 23,30.
448
The IPs are a group of heterogeneous non-neoplastic lung diseases that may be
449
idiopathic or associated with an infectious agent, including viruses
450
implicated in the pathogenesis of these diseases. Evidence suggests that increased
451
bacterial burden, including the abundance of potentially pathogenic bacteria, may drive
452
disease progression in idiopathic pulmonary fibrosis 41.
453
In addition, previous reports suggest that in cases of pulmonary fibrosis, there is
454
significantly decreased bacterial diversity and an increased chance to harbour
6,21,37,38
. Regarding antibiotics, our patients (TB and IP) did not receive
39
and bacteria
40
,
20
455
potentially pathogenic Haemophilus, Neisseria and Streptococcus spp. Additionally, in
456
exacerbated patients, there is a relatively high abundance of Proteobacteria spp.,
457
including an abundance of such potential pathogens 42.
458
In this context, our data support these findings because we found decreased diversity in
459
IP patients
460
Citrobacter and Pantoea, and pathogenic bacteria such as Neisseria.
461
The acknowledgement of differences of the lung microbiota profile in TB and IP patients
462
could improve the selection of an alternative treatment focused on restoring a healthy
463
lung microbiota profile.
464
For example, a healthy microbiota in the gut is composed of mainly anaerobes of the
465
Bacteroidetes and Firmicutes phyla that maintain mucosal immunity and provide
466
colonization resistance against other pathogens. By contrast, the inflammatory bowel
467
diseases (IBDs) Crohn's disease and ulcerative colitis are characterized by recurrent
468
gut inflammation, and the microbiota contains an abundance of the Proteobacteria
469
phylum that is associated with triggers of IBDs
470
with faecal microbiota transplant is a therapeutic alternative applied to reset the health
471
of the microbiota and reduce inflammation
472
patients with Clostridium difficile infections with promising results 46.
473
Our results describe the alveolar microbiota present in two chronic respiratory diseases,
474
and the results contribute to elucidating the difference between infectious (TB) and non-
475
infectious (IP) pulmonary diseases. Both groups of patients showed dysbiosis with
476
decreased diversity but a unique microbiota profile; however, one limitation of our study
477
is the small number of individuals included, and further studies including a large number
with predominant Proteobacteria, including Enterobacter, Erwinia,
43
. Manipulation of the gut microbiota
44,45
. This alternative has been used in
21
478
of samples are needed to confirm these findings. As with faecal microbiota
479
transplantation in the gastroenteric compartment, therapies or nutritional schemes could
480
be most likely applied to modify the dysbiosis in lung compartments to reset the health
481
of the alveolar microbiota. Additionally, in this study, we did not evaluate the lung virome
482
and mycobiota and are important to consider in future studies because they are likely
483
involved in the alveolar environment and have interactions with and influence lung
484
dynamics. Knowledge of the alveolar microbiota in lung diseases might restore the
485
microbiota to a profile associated with health.
486 487 488
Conclusions
489
In conclusion, these results showed a decrease in alveolar microbiota diversity in BAL
490
samples from TB and IP patients, which was characterized by a common reduction in
491
Streptococcus and specific genus abundances for each pulmonary disease.
492 493
Additional file
494
File 1. Clinical characteristics and data set, Table S1. Genera in each study group.
495
Table S2. Genera shared between the study groups, Cover letter R1. American
496
Journal Experts certificate.
497 498
Abbreviations
499
TB=pulmonary TB, IP=interstitial pneumonia, BALF=bronchoalveolar lavage, 16S
500
rDNA= 16S ribosomal DNA, OTU=operational taxonomic unit
22
501 502
Ethics statements
503
The Science and Ethical Committee of the Instituto Nacional de Enfermedades
504
Respiratorias Ismael Cosío Villegas in México City revised and approved this protocol
505
with reference number B17-15. All volunteers were invited to participate in the study,
506
and they provided written informed consent.
507 508
Consent for publication
509
Not applicable.
510 511
Availability of data and materials.
512
All data sets were added in an additional file.
513 514
Funding
515
Not applicable.
516 517
Competing interests
518
None of the authors have any competing interests.
519 520
Author contributions
521
MTH and JAVP performed the experiments, analysed the data and wrote the
522
manuscript. COC and MAIG obtained BAL samples by bronchoscopy. IRE and JEMG
523
performed the experiments, obtained the data and performed the analyses. LF reviewed
23
524
the manuscript, and MTH and MT participated in the study design and wrote the
525
manuscript.
526 527
Acknowledgement
528
Thanks to all the volunteers (patients and heathy) included in the study and to Arely
529
Jiménez, Raquel Galicia, Juan Manuel del Angel and Angélica Moncada for their help in
530
the laboratory.
531 532 533 534 535 536
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654 655 656
Figure 1. Flowchart for recruitment of volunteers.
657
A total of 22 volunteers were recruited and eligible for inclusion. TB patients, n=6, IP
658
patients, n=6 and healthy volunteers, n=10 were included in the study. They underwent
659
bronchoscopy for BAL, and BAL fluid samples were used for the study of alveolar
660
microbiota by sequencing.
661
TB=pulmonary TB, IP=interstitial pneumonia, AFB= acid-fast bacilli,
662
BAL=bronchoalveolar lavage
29
663 664
Figure 2. Phylum comparison in BAL samples from pulmonary TB, IP and healthy
665
individuals
666
16S rDNA from BAL samples was sequenced, and the relative abundances of phyla are
667
shown for a) individual volunteers, TB=6, IP=6 and healthy=10. b) The six dominant
668
phyla by group. Bars represent the mean ± SE; *p≤0.05, Mann-Whitney U test.
669
BAL=bronchoalveolar lavage, TB=pulmonary TB, IP=interstitial pneumonia.
670 671
Figure 3. Genus comparison in BAL samples from pulmonary TB, IP and healthy
672
individuals
673
16S rDNA from BAL samples was sequenced, and the relative abundance of the genera
674
is shown for a) individual volunteers, TB=6, IP=6 and healthy=10. b) Twelve dominant
675
genera by group. Bars represent the mean ± SE; *p≤0.05, Mann-Whitney U test.
676
BAL=bronchoalveolar lavage, TB=pulmonary TB, IP=interstitial pneumonia.
677 678
Figure 4. Rarefaction curves for α-diversity.
679
α−Diversity was measured with a depth of 40,000 sequences per sample with three
680
different matrices: a) phylogenetic distance, b) Chao1 and c) observed OTUs.
681
OTU=operational taxonomic unit.
682 683
Figure 5. Principal coordinates analysis (PCoA) plot based on weighted UniFrac
684
distances.
30
685
The scatter plot depicts samples from TB (purple) n=6, IP (green) n= 6, and healthy
686
(orange) n=10 groups. Control samples: C1=bronchoscopy (red) and C2=ARI (acute
687
respiratory infection-influenza positive) (blue).
688
BAL=bronchoalveolar lavage, TB=pulmonary TB, IP=interstitial pneumonia.
31
Table 1. Demographic and clinical characteristics of study groups Clinical variables
TB
Interstitial pneumonia
Healthy
6
6
10
4/2
3/3
6/4
Age (years)
40.2±16.6
64.6±10.5
37.8±11.9*
BMI (Kg/m2)
23.0±4.9
25.8±5.1
25.6±2.1
TST positive (%)
83.3 (5)
nd
50 (5)
Contact with TB patients (%)
16.7 (1)
0 (0)
0 (0)
Diagnosis of pulmonary TB (%)
100 (6)
NA
NA
Class 3 (6)
NA
NA
100 (6)
NA
NA
Hemoptysis (%)
0 (0)
0 (0)
0 (0)
AFB positive (%)
100 (6)
NA
NA
M.tuberculosis positive culture (%)
83.3 (5)
NA
NA
Resistance of M.tuberculosis (%)
0 (0)
NA
NA
Smoke (%)
0 (0)
0 (0)
0 (0)
HIV seropositive (%)
0 (0)
0 (0)
0 (0)
n Sex (Male/Female)
Pulmonary TB classification & Symptom/radiographic findings compatible with pulmonary TB (%)
TB= pulmonary TB, BMI=body mass index, TST= tuberculin skin test, & American Thoracic Society, AFB=acid-fast bacilli, NA= Non-applicable. Values for age and BMI represent mean (± SE, *p<0.05, interstitial pneumonia vs. healthy, Mann\Whitney U test.
Table 2. % Relative abundance of phyla in each study group
Phyla
TB (n=6)
IP (n=6)
Healthy (n=10)
Firmicutes
35.4
27.2
44.6
Proteobacteria
28.0
24.7
13.4
Bacteroidetes
21.9
39.1
24.3
Actinobacteria
9.5
2.8
7.5
Fusobacteria
2.1
3.7
5.4
Cyanobacteria
0.4
0.1
1.8
Spirochaetes
0.3
0.2
0.9
Synergistetes
0.1
<0.1
0.1
Tenericutes
0.1
0.1
0.1
Acidobacteria
0
<0.1
<0.1
Planctomycetes
0
0.0
<0.1
Chlorobi
0
0.0
<0.1
Verrucomicrobia
0
0.0
<0.1
TB=pulmonary TB, IP=interstitial pneumonia
Table 3. Exclusive genera in each group
a TB (n=7)
Relative abundance (%)
Lactococcus
1.14
Leuconostoc
0.94
Streptomyces
< 0.1
Nocardioides
< 0.1
Desulfovibrio
< 0.1
Rhodococcus
< 0.1
Sphaerochaeta
< 0.1
b
IP (n=6)
Relative abundance (%)
Enterobacter
< 0.1
Paludibacter
< 0.1
Erwinia
< 0.1
Brevibacterium
< 0.1
Citrobacter
< 0.1
Pantoea
< 0.1
C Healthy (n=20)
Relative abundance (%)
Peptoniphilus
0.43
BE24
< 0.1
Clostridium
< 0.1
Pedobacter
< 0.1
Conchiformibius
< 0.1
Faecalibacterium
< 0.1
Bacillus Novosphingobium
< 0.1
Variovorax
< 0.1
Anaerovorax
< 0.1
Arthrobacter Bifidobacterium
< 0.1
Microbacterium
< 0.1
Bacteroides
< 0.1
Ochrobactrum
< 0.1
Wautersiella
< 0.1
Kocuria
< 0.1
Candidatus
< 0.1
Solibacter
< 0.1
Oxobacter
< 0.1
TB=pulmonary tuberculosis, IP= interstitial pneumonia
Table 4. Alpha diversity indices between the study groups
Index
TB
IP
Healthy
Chao 1
16,520
24,990
21,543
Observed-OTUs
10,530
13,580
12,551
PD-whole-tree
564.74
741.48
726.62
Simpson
0.989
0.977
0.979
Shannon
8.87
8.18*
8.31
TB=pulmonary TB, IP= interstitial pneumonia, OTUs=operational taxonomic units *p<0.05 IP vs. TB, Mann-Whitney U test
Recruitment*of*volunteers Participants*interview*(n=22) TB*(n=6) (n=5)* Radiographic(and(symptoms( compatible(with(pulmonary(TB( Positive(AFB(in(sputum Without(anti>TB(treatment
IP*(n=6) (n=1)* Radiographic(and(symptoms( compatible(with(pulmonary(TB( Negative(AFB(in(sputum Without(anti>TB(treatment
(n=6) Pulmonary(disease Negative(AFB Without(antibiotic(treatment
Healthy*(n=10) (n=10) Without(evidence(of(respiratory( disease,(without(contact(with(TB( patients((and(without(antibiotic( treatment
Diagnosis
Bronchoalveolar*lavage (BAL) Positive(M.tuberculosis culture(in(sputum Drug(sensitive
Anti>TB(treatment( in(TB(patients(and( clinical(monitoring
Positive(AFB(in(BAL Positive(GenXpert/TB(in(BAL
Bronchoalveolar*cells
Laboratory
Negative(M.tuberculosis and(fungi(culture(in(BAL
BAL*fluid/*Bank*G800C “Alveolar*microbiome”
100
a
Verrucomicrobia
80
Planctomycetes Acidobacteria Tenericutes
60
Synergistetes Spirochaetes Cyanobacteria
40
Fusobacteria Actinobacteria
20
60
IP (n=6)
*
H9 H10
H7 H8
H5 H6
H4
H2 H3
H1
IP6
IP4 IP5
IP2 IP3
IP1
TB5 TB6
TB (n=6)
Healthy (n=10)
Firmicutes Proteobacteria Bacteroidetes Actinobacteria Fusobacteria Cyanobacteria
40
Healthy
IP
TB
Healthy
IP
TB
Healthy
IP
TB
Healthy
IP
TB
Healthy
IP
TB
Healthy
0
IP
20
TB
% Relative abundance
b
TB3 TB4
Proteobacteria Firmicutes TB1
0
Bacteroidetes
TB2
% Relative abundance
Chlorobi
100 80 60 40
40
IP (n=6)
H9 H10
H7 H8
H6
H5
H2 H3 H4
H1
IP 6
IP 4 IP 5
IP 2 IP 3
IP 1
TB5
TB6
TB4
TB1
* TB *(n=6)
Healthy (n=10)
30
Streptococcus Prevotella Veillonella Fusobacterium Mycobacterium Neisseria Porphiromonas
20
Haemophilus Lactobacillus
*
Acinetobacter
10
0
*
TB IP Healthy TB IP Healthy TB IP Healthy TB IP Healthy TB IP Healthy
*
TB IP Healthy TB IP Healthy TB IP Healthy TB IP Healthy
% Relative abundance
TB2 TB3
20 0
b
Others Propionibacterium Staphylococcus Campylobacter Actinobacillus Capnocytophaga [Prevotella] Acinetobacter Lactobacillus Haemophilus Porphyromonas Neisseria Mycobacterium Fusobacterium Veillonella Prevotella Streptococcus
TB IP Healthy TB IP Healthy TB IP Healthy
% Relative abundance
a
[Prevotella] Capnocytophaga
S0882-4010(19)30962-3
DOI:
https://doi.org/10.1016/j.micpath.2019.103851
Reference:
YMPAT 103851
To appear in:
Microbial Pathogenesis
Received Date: 31 May 2019 Revised Date:
19 September 2019
Accepted Date: 5 November 2019
Please cite this article as: Vázquez-Pérez JA, Carrillo ConcepcióOrtega, Iñiguez García MA, RomeroEspinoza I, Márquez García JoséEduardo, Falcón LI, Torres M, Herrera MaríTeresa, Alveolar microbiota profile in patients with human pulmonary tuberculosis and interstitial pneumonia, Microbial Pathogenesis (2019), doi: https://doi.org/10.1016/j.micpath.2019.103851. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Alveolar microbiota profile in patients with human pulmonary tuberculosis and
2
interstitial pneumonia
3 4
Joel Armando Vázquez-Pérez1 *, Concepción Ortega Carrillo2, Marco Antonio Iñiguez
5
García2, Ivan Romero-Espinoza1, José Eduardo Márquez García3, Luisa I. Falcón5,
6
Martha Torres3 and María Teresa Herrera4 *
7 8
1 Departamento de Virología, 2 Servicio de Broncoscopía, 3 Subdirección de
9
Investigación Biomédica, 4 Departamento de Investigación en Microbiología, Instituto
10
Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, 5 Laboratorio de
11
Ecología Bacteriana, Instituto de Ecología, Universidad Nacional Autónoma de México,
12
PCTY Yucatán
13 14 15 16
* Both authors contributed equally to this work
17 18
Corresponding author: María Teresa Herrera Barrios
19
Address: Departamento de Investigación en Microbiología, Instituto Nacional de
20
Enfermedades Respiratorias Ismael Cosío Villegas
21
Calzada de Tlalpan 4502, Col. Sección XVI, Ciudad de México, México, CP 14080
22
Phone: (52) 5487 17 00 Extension 5117, Fax: (52) 55 5487 1734.
23
Email: [email protected]
1
24
Abstract
25
Background. The presence of the human lung microbiota has been demonstrated in
26
patients with different lung diseases, mainly in sputum samples. However, for study of
27
the alveolar microbiota, a bronchoalveolar lavage (BAL) sample represents the lower
28
respiratory tract (LRT) environment. It is currently unknown whether there is a specific
29
alveolar microbiota profile in human lung diseases, such as pulmonary tuberculosis (TB)
30
and interstitial pneumonia (IP).
31
Methods. BAL samples from six active TB patients, six IP patients and ten healthy
32
volunteers were used for DNA extraction followed by amplification of the complete
33
bacterial 16S ribosomal RNA gene (16S rDNA). The 16S rDNA was sequenced with a
34
MiSeq Desktop Sequencer, and the data were analysed by QIIME software for
35
taxonomic assignment.
36
Results. The alveolar microbiota in TB and IP patients and healthy volunteers was
37
characterized by six dominant phyla, Firmicutes, Proteobacteria, Bacteroidetes,
38
Actinobacteria, Fusobacteria and Cyanobacteria. A significant reduction in the
39
abundance of Firmicutes was observed in IP patients. In TB and IP patients, the
40
diversity of the alveolar microbiota was diminished, characterized by a significant
41
reduction in the abundance of the Streptococcus genus and associated with increased
42
Mycobacterium abundance in TB patients and diminished Acinetobacter abundance in
43
IP patients with respect to their abundances in healthy volunteers. However, an
44
important difference was observed between TB and IP patients: the Fusobacterium
45
abundance was significantly reduced in TB patients. Exclusive genera that were less
2
46
abundant in patients than in healthy volunteers were characterized for each study
47
group.
48
Conclusions. This study shows that the alveolar microbiota profile in BAL samples
49
from TB and IP patients, representing infectious and non-infectious lung diseases,
50
respectively, is characterized by decreased diversity.
51 52 53 54 55 56 57 58 59 60 61 62 63 64
Keywords: Bronchoalveolar lavage, alveolar microbiota, pulmonary tuberculosis,
65
interstitial pneumonia, 16S rRNA, sequencing
66 67 68
3
69 70 71
Background
72
In the human body, bacterial communities are present in different sites, such as the
73
skin, vagina, oral cavity, gut and lung, and it has been estimated that 90% of the total
74
cells in the human body are bacteria 1. The gut, vagina, oral cavity and skin are the
75
most studied sites, producing important data on the human microbiota.
76
With respect to the lung microbiota, a few years ago, the lung was considered a “sterile”
77
compartment due to the limitations of traditional microbiological methods for isolating
78
and identifying all bacterial populations, with the lower respiratory tract (LRT) being
79
poorly studied.
80
However, with next-generation sequencing tools, it became possible to identify lung
81
bacterial populations in healthy volunteers and patients with different pulmonary
82
diseases
83
studies have focused on whether changes in the lung microbiota are associated with
84
specific respiratory diseases.
85
The study of the lung microbiota began in healthy volunteers and has demonstrated the
86
presence of a resident microbiota with similar taxa along the respiratory tract with a
87
decreased bacterial burden compared with that in the oral cavity
88
communities in the LRT are similar to those in the upper respiratory tract (URT) since
89
they are the result of the balance of dynamic bacterial immigration due to micro-
90
aspiration of gastric content, mucosa dispersion from oronasal cavities and air
91
inhalation, elimination by mucociliary clearance, coughing, immunity and the relative
2–6
. Because lung diseases represent a health problem worldwide, recent
2,7
. The bacterial
4
8,9
92
bacterial reproduction rates
. However, when this balance is altered in the lung by
93
different factors, such as temperature, oxygen tension, pH, nutrient availability,
94
concentration, activation of inflammatory cells and bacterial competition, important
95
changes have altered the microbiota composition and been associated with lung
96
diseases 9,10.
97
Regarding respiratory illness, several studies have investigated and described the lung
98
microbiota in patients with infectious diseases, such as pneumonia and tuberculosis
99
(TB), and non-infectious diseases, such as asthma, cystic fibrosis, smoking, chronic
100
obstructive pulmonary disease (COPD) and interstitial pneumonia (IP), with respect to
101
that in healthy volunteers to find a microbiota profile related to specific diseases 3–5,11,12.
102
In TB, sputum samples from patients have been used to characterize the lung
103
microbiota in Colombian, Chinese and Indian populations which was compared with
104
those from control group individuals
105
findings that mainly result from different characteristics of patients (with or without
106
treatment), control group selection or the kind of respiratory samples analysed (saliva or
107
thorax secretion). Additionally, differences can be due to the sequencing platform used,
108
16S rDNA variable regions selected (V1-V9) for the analysis and number of sequences
109
obtained per sample 16,17.
110
Sputum samples were mostly used for respiratory samples because they are easily
111
obtained through a non-invasive procedure; however, they are not representative of the
112
LRT and have potential contamination from the oral cavity microbiota.
11,13–15
. However, there are some controversial
5
113
For microbiota studies of the LRT, bronchoalveolar lavage (BAL) is the ideal sample
114
because it is more representative of the site than a sputum sample; however, it is
115
necessary to perform an invasive procedure to obtain a BAL sample18–20.
116
Until now, there has been only one report describing the alveolar microbiota in BAL
117
samples from TB patients after anti-TB treatment. In this study, the abundance of the
118
Mycobacterium and Porphyromonas genera was increased in TB lesions, while the
119
Cupriavidus genus was reported as dominant and specific in TB patients 21.
120
Additionally, there are several reports describing the microbiota in BAL samples from
121
individuals with non-infectious diseases, such as idiopathic pulmonary fibrosis, which
122
revealed decreased alveolar microbiota diversity in association with progression of the
123
diseases characterized by dominant phyla such as Firmicutes, Proteobacteria,
124
Bacteroidetes and Actinobacteria; thus, the potential utility of the microbiota profile can
125
be used for prognosis of this pathology 22,23.
126
In contrast, BAL samples from patients with other pulmonary diseases, such as
127
idiopathic IP, non-idiopathic IP, sarcoidosis, and Pneumocystis pneumonia, and from
128
healthy volunteers showed a predominance of the Prevotellaceae, Streptococcaceae
129
and Acidaminococcaceae families. Additionally, individuals in these categories have
130
similar α-diversity and β-diversity without any significant difference in microbiota
131
between the study groups 5.
132
To describe the alveolar microbiota composition involved in lung diseases and compare
133
infectious and non-infectious respiratory diseases, we analysed BAL samples from
134
active TB patients, IP patients and healthy volunteers. Additionally, to improve the
6
135
accuracy in taxonomy assignments, we analysed all 16S rRNA regions with high
136
sequencing depth.
137 138
Materials and Methods
139
Ethics statement
140
The Science and Ethical Committee of the Instituto Nacional de Enfermedades
141
Respiratorias Ismael Cosío Villegas in México City revised and approved this protocol.
142
Volunteers were invited to participate in the study, and they provided written informed
143
consent with authorization for the use of samples for future research.
144 145
Participants
146
Pulmonary TB patients (TB) (n=6), interstitial pneumonia (IP) patients (n=6) and healthy
147
volunteers (n=10) who were willing to volunteer to undergo bronchoalveolar lavage
148
(BAL) for diagnosis purposes were enrolled in the Pneumology Service at the Instituto
149
Nacional de Enfermedades Respiratorias in México City. The pulmonary TB patients
150
who met the following requirements were enrolled: radiographic findings, clinical
151
symptoms compatible with TB, negative human immunodeficiency virus 1 (HIV-1)
152
serology (n=6) and positive acid-fast bacilli (AFB) in the sputum (n=5) and BAL (n=1).
153
TB was confirmed later to be sputum culture-positive for drug-sensitive M. tuberculosis
154
(n=5), and BAL from one patient had positive GeneXpert/TB without resistance. All
155
patients had TB Class 3 according to American Thoracic Society 24. BAL was performed
156
before anti-TB treatment was started (Figure 1). The IP patients have a history of onset
157
with dyspnoea on large efforts, which progressed over time to dyspnoea on small
158
efforts. One of the symptoms referred to was a non-productive cough. Patients were
7
159
hospitalized and had routine laboratory studies, with negative HIV-1 and negative AFB
160
results in sputum. None of them received antibiotic treatment prior to bronchoscopy,
161
and no indication was recorded in their hospital clinical report. To establish the
162
diagnosis, BAL was performed. The BAL cultures for bacteria, fungi and mycobacteria
163
were negative. Additionally, the xTAG Respiratory Viral Panel FAST/FAST v2 (Luminex,
164
Corp., Austin, TX) by RT-qPCR was used to discard viral infections. Healthy volunteers
165
were HIV-1-negative without radiographic and clinical evidence of respiratory diseases
166
and with no contact with TB patients and were tuberculin skin test (TST) positive (n=3)
167
and TST negative (n=3) without any antibiotic treatment. The BAL cultures for bacteria,
168
fungi and mycobacteria were negative. (Figure 1)
169 170 171
Sample collection
172
Prior to bronchoscopy procedures, all volunteers underwent an oral wash with 0.12%
173
chlorhexidine gluconate solution for one minute (min) 3, and a Williams oral cannula was
174
used during bronchoscopies to prevent BAL sample contamination with the oral
175
microbiota. Briefly, after local anaesthesia of the upper airways with 2% lidocaine
176
solution and additional instillation of 1% lidocaine in the lower airways, a flexible
177
fibreoptic bronchoscope (P30, Olympus BF, New Hyde Park, NY) was wedged
178
consecutively into segment of the radiographically affected site in the TB patients and
179
into the middle lobe or the lingula in IP patients and healthy volunteers
180
sample was centrifuged, and the BAL fluid was stored at -80°C in a bank.
18
. The BAL
181
8
182
Alveolar sample concentration and DNA extraction
183
Ten millilitres of BAL fluid were concentrated by centrifugation at 4000 ×g for 20 min at
184
room temperature in a 50 ml Amicon Ultra-15 10K centrifugal filter tube (Merck Millipore
185
Ltd., Tullagreen, Ireland). The concentrated sample was transferred into a micro-
186
centrifuge tube, and DNA extraction was performed using a QIAmp Cador Pathogen
187
Mini Kit (Qiagen Co., Hilden, Germany) according the manufacturer’s instructions.
188
Briefly, buffer VXL containing carrier RNA and proteinase K was added to the
189
concentrated sample, followed by incubation at 56°C for 15 min at 1000 rpm in a
190
Thermo-Shaker (BioSan, USA).
191 192
Control samples
193
To ensure that the results presented in this study did not come from laboratory
194
contamination, we obtained samples from the laboratory bench, PCR cabinet and the
195
bronchoscope before the bronchoscopy procedure. All samples were processed with
196
the same DNA extraction protocol. A PCR negative control was included in all
197
amplifications (including all reagents except DNA).
198 199
Whole 16S ribosomal RNA amplification
200
The bacterial 16S rRNA gene (16S rDNA) was amplified using a primer set described
201
previously,
202
TACGGYTACCTTGTTACGACTT-3’)
203
primer, enzyme and the reagents of the Expand High Fidelity PCR System (Roche
204
Diagnostics, Indianapolis, IN) for a 50 µl reaction volume. The amplification reaction
27F (5’-AGAGTTTGATYMTGGCTCAG-3’)
and 1492R (reverse:
5’-
25
; 100 ng of DNA was mixed with 10 µM each
9
205
was performed in a Verity thermocycler (Applied Biosystems, San Jose, CA) with the
206
reaction at 95°C for 3 min followed by 35 cycles (9 5°C for 30 sec, 55°C for 30 sec and
207
72°C for 1.5 min) and a final extension at 72°C for 5 min.
208
Then, all the PCR product samples were mixed with loading buffer and Gel Red
209
(Biotium Inc., Fremont, CA) for staining and separated by electrophoresis in a 1.5%
210
agarose gel including a 500 bp DNA ladder molecular marker (Thermo Scientific,
211
Carlsbad, CA). The gel was observed under UV light in a Chemidoc MP Imaging
212
System (Bio-Rad Laboratories, Inc. Grand Junction, CO), and the amplified fragment
213
was purified using a QIAquick Gel Extraction Kit according to the manufacturer’s
214
instructions (Qiagen Co., Strasse, Germany). The purified fragments were stored at -
215
20°C and used for sequencing.
216 217
16S ribosomal library construction
218
Nextera XT libraries from whole 16S rDNA of each BAL sample were prepared following
219
the manufacturer’s protocol (Illumina Inc., San Diego, CA). Briefly, samples were
220
adjusted to 0.2 ng/µL DNA material per library using a Qubit dsDNA HS Assay Kit to
221
measure the DNA concentration (Invitrogen/Thermo Scientific, Eugene, OR) and then
222
fragmented and tagged via tagmentation. Each sample was indexed with a unique
223
combination of i5 and i7 sequences, and the size fragments of each library were verified
224
with a High Sensitivity DNA kit (Agilent Technologies Inc., Waldbronn, Germany) in a
225
Bioanalyzer 2100 (Agilent Technologies Inc.) followed by AMPure XP bead cleanup
226
(Beckman Coulter, Brea, CA). The library was normalized and loaded in a flow cell (2 x
10
227
250, Illumina Inc.), and sequencing was performed in a MiSeq Desktop Sequencer
228
(Illumina, Inc.) for paired-end reads 250 bp in length.
229 230
Sequence analysis and taxonomic assignment
231
Demultiplexing was performed with the default quality-filtering parameters, using R1
232
reads of each sample with a minimum quality score of 30. Sequences were then
233
analysed with QIIME version 1.8.0
234
Operational taxonomic unit (OTU) picking was performed with a closed reference
235
method by aligning the sequences without assembling to a reference in the Greengenes
236
13.8 database. OTUs were picked based on 97% sequence identity, and chimeric
237
sequences were removed using usearch61 (identify_chimeric_seqs_py). After picking
238
for OTUs, we obtained a total of 7,251,033 reads (ranging from 49,251 to 465,896
239
reads) with an average of 268,556 reads per sample and an average size of 190 nt.
240
OTUs were grouped at different levels of taxonomy classification (phylum, class, order,
241
family and genus) and normalized at each level to obtain the relative abundance using
242
the summarize_taxa_through_plots.py script. Phylogenetic trees were created using
243
FastTree2 under QIIME’s default parameters and were used for the calculation of
244
α−diversity and β-diversity metrics.
26
using the pick_open_reference_otus.py workflow.
245 246
α−Diversity analysis
247
To evaluate the diversity contained within groups, we employed rarefaction plots.
248
Rarefaction analysis was performed over 10,000 to 40,000 reads in depth with 10
249
subsampling times at each depth. Rarefaction curves were generated using the QIIME
11
250
workflow (alpha_rarefaction.py) for three diversity matrices: phylogenetic distance (PD),
251
Chao1 and observed OTUs. OTU-based α-diversity was estimated by each matrix. Non-
252
parametric tests were used to compare the statistical significance of the rarefaction
253
curves for TB, IP and healthy groups implemented in the QIIME function
254
(compare_alpha_diversity.py), as the data distribution was not normal. Phylogenetic
255
distance, the number of observed OTUs and Chao1 index at the rarefaction of 10,000
256
and 40,000 reads were compared.
257 258
β−Diversity analysis and distance comparison
259
To determine the amount of diversity shared between two communities, we employed
260
UniFrac
261
(jackknifed_beta_diversity.py) in QIIME to create distance matrices and rarefied
262
UPGMA trees and generate principal coordinates plots. Principal coordinates analysis
263
(PCoA) was applied to summarize UniFrac distance matrices and generate biplots.
264
Statistical analysis
265
Multiple comparisons tests regarding age, sex and TST were performed by ANOVA and
266
Dunn’s test, and their results were compared with proportions from the Z test. The non-
267
parametric Mann-Whitney U test was used for comparative analysis between phyla and
268
genera in our study groups. To compare the distances between groups, we created
269
distance comparison plots, and to determine if there was significance between
270
distances, we performed a two-sided Student´s two-sample t-test. A p value≤0.05 was
271
considered significant.
metrics.
β-Diversity
was
calculated
using
the
jack-knife
workflow
272
12
273
Results
274
Clinical data for patients and controls
275
BAL samples were collected from twenty-two adults: six active (TB) patients, six
276
interstitial pneumonia (IP) patients and ten healthy volunteers. The clinical and
277
demographic characteristics of the study participants are summarized in Table 1.
278 279
Microbiota composition
280
An average of 268,556 reads per sample from patients and healthy volunteers was
281
obtained. After analysis of relative abundance, we observed that the alveolar microbiota
282
was characterized by six dominant phyla: Firmicutes, Proteobacteria and Bacteroidetes,
283
followed by Actinobacteria, Fusobacteria and Cyanobacteria. These phyla were present
284
in our study groups but in different abundances (Figure 2b and Table 2). Firmicutes was
285
a phylum with higher relative abundance in healthy volunteers and TB patients than in
286
IP patients (44.6%, 35.4% and 27.2%, respectively). Moreover, Bacteroidetes was a
287
phylum with a non-significant higher relative abundance in IP patients (39.1%) than in
288
healthy volunteers (24.3%) and TB patients (21.9%).
289
Additionally, other phyla were present in the alveolar microbiota with less relative
290
abundance, and they are included in Table 2. Interestingly, we observed that phylum
291
richness was reduced in TB and IP patients in comparison with healthy volunteers
292
(TB=9, IP=10 and healthy=13). In Figure 2a, we show the relative abundance of the
293
phylum profile corresponding to each individual in the study groups.
294
Then, we focused on the phyla with a relative abundance equal to or greater than 0.1%,
295
and we observed a significant decrease in the abundance of Firmicutes in IP patients
13
296
compared with that in healthy volunteers (p<0.05). Although the decrease in Firmicutes
297
abundance was not significant in TB patients, we observed that the relative abundance
298
was lower than that in healthy volunteers (Figure 2b). Additionally, we observed that
299
Proteobacteria abundance was increased in TB and IP patients in comparison to
300
healthy volunteers, while Bacteroidetes abundance was increased in IP patients in
301
comparison with TB patients and healthy volunteers, but it was not significant.
302
We identified differences in microbial taxa between TB patients, IP patients and healthy
303
volunteers. First, the IP patients showed an inverted ratio for the relative abundances of
304
Firmicutes and Bacteroidetes compared with that of the TB patients and healthy
305
volunteers (Firmicutes: IP: 27.2% vs. TB: 35.4% and healthy 44.6%; Bacteroidetes: IP:
306
39.1% vs. TB: 21.9% and healthy 24.3%). Second, in the TB and IP patients, the
307
Proteobacteria phylum was enriched, while in healthy volunteers, its abundance was
308
reduced (TB=28.0% vs. IP=24.7% and healthy=13.4%).
309
Because changes in the lung microbiota are associated with diseases, we explored the
310
impact of the diseases on the alveolar microbiota profile at the genus level. In Figure 3a,
311
we show the relative abundances of genera in each individual volunteer. Comparison of
312
the relative abundances of genera between the study groups allowed us to identify
313
differences in the alveolar microbiota profile.
314
We analysed the twelve genera with a high relative abundance, and we found that the
315
Streptococcus abundance was significantly reduced in TB and IP patients (7.34% and
316
9.98%, respectively) in comparison with healthy volunteers (27.76%) (p<0.05), while
317
Prevotella and Veillonella were enriched in IP patients in comparison to TB patients and
318
healthy volunteers (IP: 28.38% and 9.89% vs. TB: 12.16% and 5.85% and healthy:
14
319
17.14% and 6.44%, respectively). We observed that the Fusobacterium abundance was
320
significantly reduced in TB patients (p<0.05) compared to IP patients but not healthy
321
volunteers (Figure 3b, Table 1 supplementary material).
322
In addition, we found that in comparison with healthy volunteers, TB patients exhibited a
323
significantly increased relative abundance of Mycobacterium, and IP patients exhibited
324
a significantly reduced Acinetobacter abundance (p<0.05).
325
In TB patients, the relative abundances of Lactobacillus (6.6%), Acinetobacter (7.61%),
326
Mycobacterium (6.45%) and Staphylococcus (1.93%) were higher than those in IP and
327
healthy volunteers. Our data showed reduced diversity of genera in TB and IP patients
328
in comparison with that in healthy volunteers (TB=44, IP= 50 and healthy=67,
329
respectively, Table 1 supplementary material, Figure 4).
330
Interestingly, we observed exclusive genera for each study group; for example, seven
331
genera were observed for the TB group (Table 3a), with Lactococcus and Leuconostoc
332
being the most abundant (1.14 and 0.94%), and six genera were observed for the IP
333
group (Table 3b), including Enterobacter, Paludibacter, Erwinia, Brevibacterium,
334
Citrobacter and Pantoea, all with less than 0.1% relative abundance, while twenty
335
genera were observed in the healthy group (Table 3c), with Peptoniphilus exhibiting
336
0.43% relative abundance and the other genera representing less than 0.1%.
337
However, there were other genera shared between the study groups; for example, the
338
TB group shared Butyrivibrio, Mycoplasma and Coprococcus with the IP group (Table
339
2a, Supplementary material) and shared five genera with the healthy group, including
340
Mycobacterium, Rothia, Acholeplasma, Corynebacterium and Moryella (Table 2b,
341
Supplementary material), while the IP and healthy groups shared eleven genera (Table
15
342
2c, Supplementary material). There were twenty-nine genera shared among the three
343
study groups (Table 2d, Supplementary material).
344 345
Diversity analysis
346
The diversity and richness of the 16S rDNA in each BAL sample were evaluated using
347
different α-diversity metrics. The means of the Chao1, observed OTU richness and PD
348
whole-tree diversity metrics in the TB group were lower (16,520, 10,530 and 564.74,
349
respectively) than those in the IP and healthy groups (Table 4). Additionally, the mean
350
Simpson and Shannon diversity index, which accounted for richness and evenness,
351
was higher in the TB group than in the IP (Shannon index, p<0.05) and healthy groups.
352
After conducting a rarefaction analysis, we found that a sufficient sequencing depth was
353
evident for each sample type, as illustrated by the rarefaction curves (Figure 4a, 4b and
354
4c).
355
UniFrac-based principal coordinates analysis (PCoA) revealed that TB and IP patients
356
and healthy volunteers did not cluster in clearly different groups. PCoA plots (Figure 5)
357
showed that all samples were divided into two clusters: cluster 1 contained 66.7% of IP
358
samples (IP1, IP3, IP4 and IP5), 50% of healthy samples (H1, H3, H4, H6 and H10) and
359
33.3% of TB samples (TB3 and TB6), while cluster 2 contained 66.7% of TB samples
360
(TB1, TB2, TB4 and TB5) and 50% of healthy samples (H2, H5, H7, H8 and H9). Two
361
samples (IP2 and IP6) and the two control samples did not belong to any cluster.
362
Moreover, when we compared the distances between the groups, there was no
363
statistically significant difference.
364
16
365 366 367 368
Discussion
369
The microbiota has important effects on the human body, and its early acquisition in
370
infants by breast milk represents a pivotal moment in modulating and developing the
371
immune system 27.
372
In the gut, Firmicutes and Bacteroidetes produce acetate, propionate and butyrate,
373
which act as energy sources and are involved in vital procedures such as stimulation of
374
growth, differentiation and mucin production of epithelial cells, lipogenesis, physiology,
375
homeostasis and development of the gut immune response 28,29.
376
Human health is characterized by a balance between the microbiota, metabolites and
377
the immune response, and an imbalance in these factors is associated with some
378
diseases and induces changes in the microbiota composition, although in some cases,
379
individual genetic factors are associated with and promote diseases 29.
380
Until now, the lung microbiota has been studied in mainly sputum samples, followed by
381
different secretions (alveolar, respiratory, nasal, oro-pharyngeal, pharyngeal), and
382
bronchial aspirates and saliva in pulmonary infections (TB and pneumonia) and non-
383
infectious diseases (asthma, cystic fibrosis, smoking, COPD and IP)17. BAL samples are
384
an alternative in studies describing the lower respiratory tract (LRT) microbiota,
385
although there is a limitation because it is difficult to obtain these samples and there is
386
the probability of sample contamination from the oral microbiota.
17
387
Unlike previous studies of lung diseases (TB, IP, COPD and cystic fibrosis) where one
388
or two variable regions from 16S rDNA have been sequenced with fewer than 50,000
389
reads per sample 4–6,11,14,15,21,23,30, we sequenced the complete 16S rDNA from BAL and
390
obtained an average of 268,556 reads per sample to obtain increased accuracy in
391
taxonomy assignments with a high sequencing depth.
392
Our results demonstrated that the alveolar microbiota profile in BAL samples from TB
393
and IP patients is characterized by a loss of microbiota diversity and also showed a
394
“bacterial core” composed of mainly Firmicutes, Proteobacteria, Bacteroidetes,
395
Actinobacteria, Fusobacteria and Cyanobacteria in the alveolar microbiota from TB and
396
IP patients and healthy volunteers, consistent with previous reports 22,31–33. The
397
abundance of these taxa in the oral microbiota has been described, but the bacterial
398
burden in the LRT is lower than that in the oral cavity 2,34,35.
399
Interestingly, we found a diminished relative abundance of Firmicutes in BAL samples
400
from IP and TB patients with respect to that of healthy volunteers, and these results are
401
in accordance with previous reports of sputum samples from Chinese pulmonary TB
402
patients
403
and Actinobacteria abundances were increased 15. These differences may be due to the
404
type of sample, study population, geographic region, diet, environmental conditions and
405
customs. Although there was no statistical significance, it was important to note the
406
increase in Proteobacteria abundance in TB and IP patients, and these changes could
407
be a consequence of M. tuberculosis infection or associated with inflammation in IP
408
patients.
14
but are contradictory to results from Indian TB patients, for whom Firmicutes
18
409
At the genus level, we found a decrease in the relative abundance of Streptococcus in
410
both TB and IP patients, indicating that microbiota changes were independent of
411
disease aetiology (infectious or not infectious). In particular, the microbiota profile in TB
412
patients was characterized by a Streptococcus abundance reduction associated with an
413
increase in Mycobacterium, Lactobacillus and Acinetobacter abundances. This profile
414
could be determined by the growth of M. tuberculosis and probably by the influence of
415
the immune response characterized by the production of pro-inflammatory cytokines,
416
such as IFN-γ and IL-12, because the inflammatory environment influences microbial
417
communities
418
release its virulence factors, ESAT-6 and CFP-10, causing a reduced macrophage
419
response by suppressing nitric oxide (NO) and reactive oxygen species (ROS)
420
production
421
were reduced, and Prevotella and Veillonella abundances increased. The difference
422
observed in microbiota profiles between TB and IP patients was a reduced
423
Fusobacterium abundance in TB patients. Additionally, it is important to note that
424
differences between patients were not determined by one genus because changes in
425
others genus were observed.
426
In addition, exclusive genera were found in each group; for example, in TB, seven
427
predominant genera were observed: Lactococcus, Leuconostoc, Streptomyces,
428
Nocardioides,
429
observation did not match with a previous report on BAL samples from a Chinese
430
population with TB in which Cupriavidus was a dominant and exclusive genus in TB
431
patients. However, this difference is most likely due to anti-TB treatment prior to BAL in
18
. In this environment, M. tuberculosis can compete for nutrients and
36
. Regarding IP patients, Streptococcus and Acinetobacter abundances
Desulfovibrio,
Rhodococcus
and
Sphaerochaeta.
However,
this
19
432
patients included in this study because antibiotics alter the microbiota diversity
433
composition
434
treatment prior to obtaining the BAL sample; thus, our findings were not affected by
435
antibiotic treatment, although IP patients received different drugs before the BAL
436
procedure. It is important to mention that the age of healthy volunteers was not paired
437
with the age of TB and IP patients, which is a limitation of our study.
438
Furthermore, IP patients had six exclusive genera (Enterobacter, Paludibacter,
439
Erwinia, Brevibacterium, Citrobacter and Pantoea), while healthy volunteers had twenty
440
exclusive genera, with a very low relative abundance for both groups (<0.1%).
441
Our results support a different profile in the alveolar microbiota associated with disease,
442
which is related to the fact that the aetiologies demonstrated in active TB and IP are
443
unique, sharing a lower bacterial diversity than healthy volunteers.
444
Similar results were shown in BAL samples from moderate and severe COPD and
445
idiopathic pulmonary fibrosis patients, with low microbiota diversity with predominant
446
Bacteroidetes, Firmicutes, Proteobacteria and Fusobacteria phyla and the presence of
447
Pseudomonas, Streptococcus, Prevotella, Fusobacterium and Veillonella genera 23,30.
448
The IPs are a group of heterogeneous non-neoplastic lung diseases that may be
449
idiopathic or associated with an infectious agent, including viruses
450
implicated in the pathogenesis of these diseases. Evidence suggests that increased
451
bacterial burden, including the abundance of potentially pathogenic bacteria, may drive
452
disease progression in idiopathic pulmonary fibrosis 41.
453
In addition, previous reports suggest that in cases of pulmonary fibrosis, there is
454
significantly decreased bacterial diversity and an increased chance to harbour
6,21,37,38
. Regarding antibiotics, our patients (TB and IP) did not receive
39
and bacteria
40
,
20
455
potentially pathogenic Haemophilus, Neisseria and Streptococcus spp. Additionally, in
456
exacerbated patients, there is a relatively high abundance of Proteobacteria spp.,
457
including an abundance of such potential pathogens 42.
458
In this context, our data support these findings because we found decreased diversity in
459
IP patients
460
Citrobacter and Pantoea, and pathogenic bacteria such as Neisseria.
461
The acknowledgement of differences of the lung microbiota profile in TB and IP patients
462
could improve the selection of an alternative treatment focused on restoring a healthy
463
lung microbiota profile.
464
For example, a healthy microbiota in the gut is composed of mainly anaerobes of the
465
Bacteroidetes and Firmicutes phyla that maintain mucosal immunity and provide
466
colonization resistance against other pathogens. By contrast, the inflammatory bowel
467
diseases (IBDs) Crohn's disease and ulcerative colitis are characterized by recurrent
468
gut inflammation, and the microbiota contains an abundance of the Proteobacteria
469
phylum that is associated with triggers of IBDs
470
with faecal microbiota transplant is a therapeutic alternative applied to reset the health
471
of the microbiota and reduce inflammation
472
patients with Clostridium difficile infections with promising results 46.
473
Our results describe the alveolar microbiota present in two chronic respiratory diseases,
474
and the results contribute to elucidating the difference between infectious (TB) and non-
475
infectious (IP) pulmonary diseases. Both groups of patients showed dysbiosis with
476
decreased diversity but a unique microbiota profile; however, one limitation of our study
477
is the small number of individuals included, and further studies including a large number
with predominant Proteobacteria, including Enterobacter, Erwinia,
43
. Manipulation of the gut microbiota
44,45
. This alternative has been used in
21
478
of samples are needed to confirm these findings. As with faecal microbiota
479
transplantation in the gastroenteric compartment, therapies or nutritional schemes could
480
be most likely applied to modify the dysbiosis in lung compartments to reset the health
481
of the alveolar microbiota. Additionally, in this study, we did not evaluate the lung virome
482
and mycobiota and are important to consider in future studies because they are likely
483
involved in the alveolar environment and have interactions with and influence lung
484
dynamics. Knowledge of the alveolar microbiota in lung diseases might restore the
485
microbiota to a profile associated with health.
486 487 488
Conclusions
489
In conclusion, these results showed a decrease in alveolar microbiota diversity in BAL
490
samples from TB and IP patients, which was characterized by a common reduction in
491
Streptococcus and specific genus abundances for each pulmonary disease.
492 493
Additional file
494
File 1. Clinical characteristics and data set, Table S1. Genera in each study group.
495
Table S2. Genera shared between the study groups, Cover letter R1. American
496
Journal Experts certificate.
497 498
Abbreviations
499
TB=pulmonary TB, IP=interstitial pneumonia, BALF=bronchoalveolar lavage, 16S
500
rDNA= 16S ribosomal DNA, OTU=operational taxonomic unit
22
501 502
Ethics statements
503
The Science and Ethical Committee of the Instituto Nacional de Enfermedades
504
Respiratorias Ismael Cosío Villegas in México City revised and approved this protocol
505
with reference number B17-15. All volunteers were invited to participate in the study,
506
and they provided written informed consent.
507 508
Consent for publication
509
Not applicable.
510 511
Availability of data and materials.
512
All data sets were added in an additional file.
513 514
Funding
515
Not applicable.
516 517
Competing interests
518
None of the authors have any competing interests.
519 520
Author contributions
521
MTH and JAVP performed the experiments, analysed the data and wrote the
522
manuscript. COC and MAIG obtained BAL samples by bronchoscopy. IRE and JEMG
523
performed the experiments, obtained the data and performed the analyses. LF reviewed
23
524
the manuscript, and MTH and MT participated in the study design and wrote the
525
manuscript.
526 527
Acknowledgement
528
Thanks to all the volunteers (patients and heathy) included in the study and to Arely
529
Jiménez, Raquel Galicia, Juan Manuel del Angel and Angélica Moncada for their help in
530
the laboratory.
531 532 533 534 535 536
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654 655 656
Figure 1. Flowchart for recruitment of volunteers.
657
A total of 22 volunteers were recruited and eligible for inclusion. TB patients, n=6, IP
658
patients, n=6 and healthy volunteers, n=10 were included in the study. They underwent
659
bronchoscopy for BAL, and BAL fluid samples were used for the study of alveolar
660
microbiota by sequencing.
661
TB=pulmonary TB, IP=interstitial pneumonia, AFB= acid-fast bacilli,
662
BAL=bronchoalveolar lavage
29
663 664
Figure 2. Phylum comparison in BAL samples from pulmonary TB, IP and healthy
665
individuals
666
16S rDNA from BAL samples was sequenced, and the relative abundances of phyla are
667
shown for a) individual volunteers, TB=6, IP=6 and healthy=10. b) The six dominant
668
phyla by group. Bars represent the mean ± SE; *p≤0.05, Mann-Whitney U test.
669
BAL=bronchoalveolar lavage, TB=pulmonary TB, IP=interstitial pneumonia.
670 671
Figure 3. Genus comparison in BAL samples from pulmonary TB, IP and healthy
672
individuals
673
16S rDNA from BAL samples was sequenced, and the relative abundance of the genera
674
is shown for a) individual volunteers, TB=6, IP=6 and healthy=10. b) Twelve dominant
675
genera by group. Bars represent the mean ± SE; *p≤0.05, Mann-Whitney U test.
676
BAL=bronchoalveolar lavage, TB=pulmonary TB, IP=interstitial pneumonia.
677 678
Figure 4. Rarefaction curves for α-diversity.
679
α−Diversity was measured with a depth of 40,000 sequences per sample with three
680
different matrices: a) phylogenetic distance, b) Chao1 and c) observed OTUs.
681
OTU=operational taxonomic unit.
682 683
Figure 5. Principal coordinates analysis (PCoA) plot based on weighted UniFrac
684
distances.
30
685
The scatter plot depicts samples from TB (purple) n=6, IP (green) n= 6, and healthy
686
(orange) n=10 groups. Control samples: C1=bronchoscopy (red) and C2=ARI (acute
687
respiratory infection-influenza positive) (blue).
688
BAL=bronchoalveolar lavage, TB=pulmonary TB, IP=interstitial pneumonia.
31
Table 1. Demographic and clinical characteristics of study groups Clinical variables
TB
Interstitial pneumonia
Healthy
6
6
10
4/2
3/3
6/4
Age (years)
40.2±16.6
64.6±10.5
37.8±11.9*
BMI (Kg/m2)
23.0±4.9
25.8±5.1
25.6±2.1
TST positive (%)
83.3 (5)
nd
50 (5)
Contact with TB patients (%)
16.7 (1)
0 (0)
0 (0)
Diagnosis of pulmonary TB (%)
100 (6)
NA
NA
Class 3 (6)
NA
NA
100 (6)
NA
NA
Hemoptysis (%)
0 (0)
0 (0)
0 (0)
AFB positive (%)
100 (6)
NA
NA
M.tuberculosis positive culture (%)
83.3 (5)
NA
NA
Resistance of M.tuberculosis (%)
0 (0)
NA
NA
Smoke (%)
0 (0)
0 (0)
0 (0)
HIV seropositive (%)
0 (0)
0 (0)
0 (0)
n Sex (Male/Female)
Pulmonary TB classification & Symptom/radiographic findings compatible with pulmonary TB (%)
TB= pulmonary TB, BMI=body mass index, TST= tuberculin skin test, & American Thoracic Society, AFB=acid-fast bacilli, NA= Non-applicable. Values for age and BMI represent mean (± SE, *p<0.05, interstitial pneumonia vs. healthy, Mann\Whitney U test.
Table 2. % Relative abundance of phyla in each study group
Phyla
TB (n=6)
IP (n=6)
Healthy (n=10)
Firmicutes
35.4
27.2
44.6
Proteobacteria
28.0
24.7
13.4
Bacteroidetes
21.9
39.1
24.3
Actinobacteria
9.5
2.8
7.5
Fusobacteria
2.1
3.7
5.4
Cyanobacteria
0.4
0.1
1.8
Spirochaetes
0.3
0.2
0.9
Synergistetes
0.1
<0.1
0.1
Tenericutes
0.1
0.1
0.1
Acidobacteria
0
<0.1
<0.1
Planctomycetes
0
0.0
<0.1
Chlorobi
0
0.0
<0.1
Verrucomicrobia
0
0.0
<0.1
TB=pulmonary TB, IP=interstitial pneumonia
Table 3. Exclusive genera in each group
a TB (n=7)
Relative abundance (%)
Lactococcus
1.14
Leuconostoc
0.94
Streptomyces
< 0.1
Nocardioides
< 0.1
Desulfovibrio
< 0.1
Rhodococcus
< 0.1
Sphaerochaeta
< 0.1
b
IP (n=6)
Relative abundance (%)
Enterobacter
< 0.1
Paludibacter
< 0.1
Erwinia
< 0.1
Brevibacterium
< 0.1
Citrobacter
< 0.1
Pantoea
< 0.1
C Healthy (n=20)
Relative abundance (%)
Peptoniphilus
0.43
BE24
< 0.1
Clostridium
< 0.1
Pedobacter
< 0.1
Conchiformibius
< 0.1
Faecalibacterium
< 0.1
Bacillus Novosphingobium
< 0.1
Variovorax
< 0.1
Anaerovorax
< 0.1
Arthrobacter Bifidobacterium
< 0.1
Microbacterium
< 0.1
Bacteroides
< 0.1
Ochrobactrum
< 0.1
Wautersiella
< 0.1
Kocuria
< 0.1
Candidatus
< 0.1
Solibacter
< 0.1
Oxobacter
< 0.1
TB=pulmonary tuberculosis, IP= interstitial pneumonia
Table 4. Alpha diversity indices between the study groups
Index
TB
IP
Healthy
Chao 1
16,520
24,990
21,543
Observed-OTUs
10,530
13,580
12,551
PD-whole-tree
564.74
741.48
726.62
Simpson
0.989
0.977
0.979
Shannon
8.87
8.18*
8.31
TB=pulmonary TB, IP= interstitial pneumonia, OTUs=operational taxonomic units *p<0.05 IP vs. TB, Mann-Whitney U test
Recruitment*of*volunteers Participants*interview*(n=22) TB*(n=6) (n=5)* Radiographic(and(symptoms( compatible(with(pulmonary(TB( Positive(AFB(in(sputum Without(anti>TB(treatment
IP*(n=6) (n=1)* Radiographic(and(symptoms( compatible(with(pulmonary(TB( Negative(AFB(in(sputum Without(anti>TB(treatment
(n=6) Pulmonary(disease Negative(AFB Without(antibiotic(treatment
Healthy*(n=10) (n=10) Without(evidence(of(respiratory( disease,(without(contact(with(TB( patients((and(without(antibiotic( treatment
Diagnosis
Bronchoalveolar*lavage (BAL) Positive(M.tuberculosis culture(in(sputum Drug(sensitive
Anti>TB(treatment( in(TB(patients(and( clinical(monitoring
Positive(AFB(in(BAL Positive(GenXpert/TB(in(BAL
Bronchoalveolar*cells
Laboratory
Negative(M.tuberculosis and(fungi(culture(in(BAL
BAL*fluid/*Bank*G800C “Alveolar*microbiome”
100
a
Verrucomicrobia
80
Planctomycetes Acidobacteria Tenericutes
60
Synergistetes Spirochaetes Cyanobacteria
40
Fusobacteria Actinobacteria
20
60
IP (n=6)
*
H9 H10
H7 H8
H5 H6
H4
H2 H3
H1
IP6
IP4 IP5
IP2 IP3
IP1
TB5 TB6
TB (n=6)
Healthy (n=10)
Firmicutes Proteobacteria Bacteroidetes Actinobacteria Fusobacteria Cyanobacteria
40
Healthy
IP
TB
Healthy
IP
TB
Healthy
IP
TB
Healthy
IP
TB
Healthy
IP
TB
Healthy
0
IP
20
TB
% Relative abundance
b
TB3 TB4
Proteobacteria Firmicutes TB1
0
Bacteroidetes
TB2
% Relative abundance
Chlorobi
100 80 60 40
40
IP (n=6)
H9 H10
H7 H8
H6
H5
H2 H3 H4
H1
IP 6
IP 4 IP 5
IP 2 IP 3
IP 1
TB5
TB6
TB4
TB1
* TB *(n=6)
Healthy (n=10)
30
Streptococcus Prevotella Veillonella Fusobacterium Mycobacterium Neisseria Porphiromonas
20
Haemophilus Lactobacillus
*
Acinetobacter
10
0
*
TB IP Healthy TB IP Healthy TB IP Healthy TB IP Healthy TB IP Healthy
*
TB IP Healthy TB IP Healthy TB IP Healthy TB IP Healthy
% Relative abundance
TB2 TB3
20 0
b
Others Propionibacterium Staphylococcus Campylobacter Actinobacillus Capnocytophaga [Prevotella] Acinetobacter Lactobacillus Haemophilus Porphyromonas Neisseria Mycobacterium Fusobacterium Veillonella Prevotella Streptococcus
TB IP Healthy TB IP Healthy TB IP Healthy
% Relative abundance
a
[Prevotella] Capnocytophaga