JCM Accepts, published online ahead of print on 21 May 2014 J. Clin. Microbiol. doi:10.1128/JCM.00035-14 Copyright © 2014, American Society for Microbiology. All Rights Reserved.
1
Airway Microbiome Dynamics in Exacerbations of Chronic Obstructive
2
Pulmonary Disease
3 4
Yvonne J. Huang1, Sanjay Sethi2,3, Timothy Murphy3, Snehal Nariya1, Homer A.
5
Boushey1, Susan V. Lynch4
6 7
1
8
University of California San Francisco
9
2
Division of Pulmonary/Critical Care/Allergy/Sleep Medicine; Department of Medicine,
Division of Pulmonary/Critical Care/Sleep Medicine; Veterans Western New York
10
Health System, Buffalo, New York
11
3
Department of Medicine, University at Buffalo State University of New York
12
4
Division of Gastroenterology; Department of Medicine, University of California San
13
Francisco
14 15
Corresponding author:
16
Yvonne J. Huang, MD
17
505 Parnassus Avenue, Box 0130
18
San Francisco, CA 94143-0130
19
Email: [email protected]
20
Tel: (415) 476-9456; Fax: (888) 786-7576
21 22 23
24
ABSTRACT
25
Specific bacterial species are implicated in the pathogenesis of exacerbations of chronic
26
obstructive pulmonary disease (COPD). However, recent studies of clinically stable
27
COPD patients have demonstrated a greater diversity of airway microbiota, whose role in
28
acute exacerbations is unclear. In this study temporal changes in the airway microbiome
29
before, at the onset of, and after an acute exacerbation were examined in 60 sputum
30
samples collected from subjects enrolled in a longitudinal study of bacterial infection in
31
COPD. Microbiome composition and predicted functions were examined using 16S
32
rRNA-based culture-independent profiling methods. Shifts in the abundance (>2-fold,
33
p 4 weeks) from an individual patient
92
(3).
93 94
To investigate the exacerbation-related microbiome, subjects and samples were carefully
95
selected for this study to include a diversity of exacerbations, those that were and were
96
not associated with a new strain, and also treatment approaches commonly prescribed in
97
clinical practice (antibiotics only, systemic corticosteroids only, or both). Five time
98
points per subject were examined (Figure 1). The specimens spanned an exacerbation-
99
related time period occurring in a given year between 1999 and 2005 (specific year
100
depending on the subject). Pre- and post-exacerbation samples were those available
101
closest to the exacerbation when the patients were at their baseline respiratory state and
102
on their usual COPD treatment. All subjects were encouraged to seek treatment early for
103
a suspected exacerbation, however a fixed duration of symptoms was not proscribed.
104
However, all exacerbations samples included in this study were obtained prior to
105
initiation of any specific treatment for the exacerbation. Exacerbation events were
106
determined using pre-defined criteria described previously (3), involving evaluation of
107
respiratory symptoms (dyspnea, cough, sputum production, viscosity and purulence) and
108
their change from usual state. Pneumonia was ruled out by chest x-ray if suspected.
109
Decisions about treatments were made by study physicians (Drs. Sethi and Murphy)
110
based on symptoms (especially sputum purulence) and prior to the availability of sputum
111
culture data from the concurrent visit. Antibiotics were prescribed if bacterial infection
112
was thought to be a cause of the exacerbation, whereas steroids only were prescribed if a
113
non-bacterial etiology was suspected.
5
114 115
Sample preparations
116
DNA was extracted utilizing methods previously described (15) and detailed further in
117
the supplemental material. Briefly, universal 16S rRNA bacterial primers (Bact-27F,
118
Bact-1492R) were used to generate amplicons which were processed for hybridization to
119
G2 PhyloChip microarrays (Second Genome; South San Francisco, CA) by methods
120
previously described (15, 16). Each sample was hybridized to an individual array,
121
resulting in 60 microbiome profiles. Bacterial taxa were defined as organisms sharing ≥
122
97% 16S rRNA gene sequence homology and classified according to Hugenholtz
123
taxonomy in the 2011 iteration of the Greengenes database (17).
124 125
Data analyses
126
Raw array data were processed as previously described (18-20), including correction for
127
background and noise, scaling of fluorescence intensities to spiked-in quantitative
128
standards, followed by further data normalization based on the mean intensity of all
129
profiles. Presence of taxa was determined using published methods (20, 21) with slightly
130
modified scoring thresholds applied based on spiked-in quantitative standards. Further
131
information is detailed in the supplemental material. The initial normalized dataset
132
combined for all 60 samples included ~ 4,800 taxa, which was then filtered to include
133
taxa present in at least 10% of samples, yielding a final dataset of ~3,300 taxa for
134
analysis (available in Table S5). Log2-transformed intensities, which correlate with taxa
135
relative abundance (22), were used in all primary analyses which were performed using R
136
version 2.14.1.
6
137 138
Community richness (number of taxa detected), evenness (relative distribution of taxa in
139
a community) and diversity [Shannon and inverse Simpson indices, which consider both
140
richness and evenness in the calculation, as well as Faith’s phylogenetic diversity which
141
additionally incorporates phylogenetic information in this calculation (23)] were
142
calculated using R packages vegan and picante (24, 25). Changes in these metrics were
143
tested using repeated measures ANOVA (RM-ANOVA) and paired t-tests. Bray-Curtis
144
and Unifrac distance measures (26, 27) were calculated and used in ordination analyses
145
(e.g. non-metric multidimensional scaling, NMDS) to assess compositional differences
146
and relationships to clinical factors, including the timing of pre- and post-exacerbation
147
sample collection relative to exacerbation onset date, by distance matrix-based
148
PERMANOVA (28). Taxon-level analyses to identify specific compositional changes
149
between time points were performed using linear models based on moderated t-statistics
150
and a paired test design in R package limma (29). Adjustment for multiple comparisons
151
was applied using the Benjamini-Hochberg method or q-values (30). Changes of at least
152
2-fold (log2 difference > 1.0; unadjusted p 2-fold. Overall, these
230
observations suggest relative stability in airway microbiota composition during a
231
clinically stable period prior to AECOPD in this cohort.
232 233
At exacerbation, it was predominantly members of the Proteobacteria such as
234
Moraxellaceae, Pasteurellaceae, Pseudomonadaceae, and Enterobacteriaceae that were
235
enriched, when compared to Pre2 (Table 2). These enriched taxa included other
236
potential respiratory pathogens in addition to those typically associated with AECOPD.
237
In contrast, taxa that were decreased in abundance at exacerbation relative to Pre2, were
238
predominantly Actinobacteria, Clostridia and Bacteroidia (Table 2). Actinobacteria
239
comprise a large group of metabolically diverse organisms that are prolific producers of
240
secondary metabolites, including those with antimicrobial activity (35), while clade IV
241
and XIVA Clostridia are known inducers of anti-inflammatory T-regulatory cells (36). To
242
speculate, these observations suggest the possibility that members of the microbiome that
243
are depleted at the time of exacerbation may otherwise help maintain community and
244
host immune homeostasis and potentially counteract detrimental effects of respiratory
245
pathogenic species.
246 247
The most dramatic changes in community composition were observed after treatment for
248
exacerbation (Exac to Post1). This was supported by a significant difference in the mean
249
Unifrac distance of communities between Exac and Post1 compared to that for
250
communities between Pre2 and Exac, reflecting a significant change in phylogenetic
11
251
make-up of the community (Fig. S1; p < 0.01). Among those taxa exhibiting significant
252
alterations in relative abundance,, the vast majority decreased in abundance, were
253
primarily members of the Proteobacteria, and included organisms both associated and not
254
traditionally associated with COPD (p < 0.05, q-value < 0.12; Table S1).
255 256
Striking differences, however, were seen in the effects of different treatments on
257
community composition (Fig. 3B). Treatment with antibiotics alone reduced the
258
abundance of many taxa (mostly Proteobacteria), a number of which exhibited further
259
reductions in relative abundance at the second post-treatment time point (Post1 to Post2).
260
The latter supports our data indicating a relationship between treatment type and
261
community composition in post-treatment samples and provides evidence for the
262
prolonged suppressive effects of antibiotics on some microbiota members. For subjects
263
who received any antibiotics (either alone or with steroids), a similar pattern of reduction
264
in microbiota members at Post1 was observed. However, in contrast to those treated with
265
antibiotics-only, those treated with both antibiotics and steroids showed a significant
266
increase in abundance of primarily Proteobacteria from Post1 to Post2. This was
267
reflected also in an increase in phylogenetic diversity at Post2 compared to Post1 for this
268
group (p < 0.01) and suggests that recovery of airway community diversity following
269
antibiotic treatment could be influenced by concomitant corticosteroid treatment.
270 271
In contrast to antibiotics, treatment with corticosteroids alone led predominantly to
272
enrichment for many taxa, mainly Proteobacteria but also Bacteroidetes and Firmicutes
273
members (Fig. 3B). Large increases were observed especially for Enterobacteriaceae (up
12
274
to 16-fold), Lachnospiraceae, Burkholderiaceae, and Neisseriaceae (Table S2). This was
275
mirrored by a significantly higher mean 16S rRNA copy number, a proxy for bacterial
276
burden, in this group compared to the other treatment groups (Figure S2; RM-ANOVA p
277
< 0.02; steroids only vs. Abx only group, p 0.5, BH-adjusted p < 0.05), e.g. Pasteurellaceae,
290
Enterobacteriaceae, and Pseudomonadaceae (Figure 4). Most belong to the larger class
291
of γ-Proteobacteria, as well as other classes of Proteobacteria. In contrast, taxa
292
negatively correlated with H. influenzae were primarily members of more
293
phylogenetically distant groups. Similar correlation patterns were observed for P.
294
aeruginosa and M. catarrhalis (data not shown). These observations suggest that
295
enrichment of phylogenetically related organisms in a pathogen-rich airway milieu may
13
296
represent an important factor in microbiome dynamics associated with the pathogenesis
297
of exacerbations.
298 299
Quantitative PCR and sequencing validation studies
300
Total 16S rRNA gene copy numbers ranged from 1.9 x 105 to 1.8 x 108 (Figure S2).
301
While these did not differ significantly over time, type of treatment for exacerbation was
302
a significant factor in observed differences (RM-ANOVA, p = 0.02) with the group
303
treated with corticosteroids alone demonstrating higher mean total 16S rRNA copies
304
(steroids-only vs. Abx-only; p 2-fold were
330
used as PICRUSt inputs. The results predicted a number of KEGG gene orthologs (KOs;
331
Kyoto Encyclopedia of Genes and Genomes, release 67.1) associated with known KEGG
332
pathways, as enriched or depleted within the bacterial community at these time points
333
(Figure 5). Predicted functions enriched at exacerbation included pathways involved in
334
viral and bacterial infection (e.g. influenza A, S. aureus infection), as well as those
335
involved in apoptosis and p53 signaling. Conversely, functions predicted to be depleted
336
involved pathways associated with response to viral infection (e.g. RIG-I like receptor
337
signaling), and pathways involved in flavonoid and steroid biosynthesis [known anti-
338
inflammatory mediators (38)], as well as betalain and indole alkaloid production [known
339
anti-microbial properties (39, 40)]. These observations suggest that loss of community
340
functions associated with maintenance of microbial homeostasis and regulation of host
341
immune response are associated with AECOPD. This is further supported by the
15
342
observation that after treatment, reserve trends were observed. For example, predicted
343
KOs that were enriched for after exacerbation included those involved in betalain, indole
344
alkaloid, and flavonol biosynthesis pathways. In addition, KOs involved in macrolide
345
biosynthesis also were predicted to be enriched post-treatment. Thus microbiome
346
enrichment for taxa that encode these anti-inflammatory functional capacities may
347
contribute to recovery from exacerbation.
348 349
DISCUSSION
350
In this study we extensively examined dynamics of the bacterial airway microbiome in
351
the setting of AECOPD. In addition to assessment of compositional changes by multiple
352
methods, we further explored the predicted functional capacities encoded by microbiota
353
identified as potentially important based on observed compositional shifts. Thus this
354
study represents the most comprehensive effort to date to evaluate the exacerbation-
355
associated microbiome in COPD and serves as a foundation for approaches that could be
356
applied to study larger numbers of patients. Collectively, our findings demonstrate that
357
the COPD airway microbiome is rich in both bacterial species and functional capacity,
358
and that identifiable changes in this microbiome are associated with the development,
359
treatment, and resolution of AECOPD.
360 361
An important limitation of our study is that the intensive microbiome analyses performed
362
on sixty samples derived from only a small number of COPD patients. Offsetting this
363
limitation is the longitudinal nature of the study, providing us with valid pre-exacerbation
364
samples, as well as exacerbation samples prior to any specific treatment for exacerbation.
16
365
Future studies involving larger patient numbers will be necessary, especially given the
366
heterogeneity of COPD and interest in identifying best treatment approaches for different
367
phenotypes (41). Despite the cohort size, we were able to capture some of the
368
heterogeneity associated with exacerbations by intentionally including a proportionally
369
even number of subjects treated for exacerbation using regimens prescribed in clinical
370
practice. In this context, we were able to identify microbiome characteristics
371
differentially associated with features of exacerbations in these subjects. Follow-up
372
studies involving larger numbers of carefully phenotyped patients would be of interest.
373 374
Several specific findings from our study are important to emphasize and have potential
375
clinical implications to be further investigated. An important overall observation from a
376
microbial community perspective is that many bacterial groups, not limited to typical
377
COPD pathogens, were amongst those contributing to the most salient compositional
378
changes observed. These dynamics, involving Proteobacteria taxa in addition to those
379
representative of H. influenzae, M. catarrhalis and P. aeruginosa, suggest potentially
380
important contributions of related microbiota to the etiopathogenesis of AECOPD.
381
Independent analyses confirmed that the abundance of these typical COPD pathogens
382
was highly correlated with the presence of related bacterial phylotypes. While array
383
cross-hybridization at the taxon level is a possible factor in these analyses, it unlikely
384
accounts for the number of distinct groups, characterized at family-level and higher,
385
found to be correlated with these species. Our observations are consistent with previous
386
demonstrations that organisms in an ecosystem can promote enrichment for related
387
species in a ‘like will to like’ phenomenon, as observed for gut microbiota and
17
388
susceptibility to pathogen colonization (37). Moreover, quorum-sensing mechanisms
389
between organisms can influence bacterial pathogenicity or resistance to antimicrobials
390
(42, 43). Overall, findings from this and other studies (44) indicate that the context of the
391
microbial community is important to consider in efforts to better understand the
392
pathogenesis of AECOPD.
393 394
Another important observation is that inter-subject variability in microbial community
395
changes at exacerbation were seen. Similar observations were made in a study using a
396
human model of viral-induced COPD exacerbation (44). In our study, compositional
397
changes at exacerbation in general did not involve very large shifts, although this was not
398
true of all subjects. This is similar to what has been described in exacerbations of cystic
399
fibrosis and non-CF bronchiectasis (45, 46). This suggests that AECOPD could also
400
result from cumulative effects of smaller scale changes in community composition that,
401
based on PICRUSt functional predictions, lead not only to an increase in pathogen-
402
elicited inflammation but also loss of potentially key protective functions in the
403
microbiome. Moreover, organisms do not need to be highly prevalent in a community to
404
have important effects on disease pathogenesis, as has been shown for periodontitis (47).
405
However, two of our subjects (#56 and #111) did exhibit a greater compositional shift at
406
exacerbation based on NMDS analysis (data not shown). Although new bacterial strains
407
were identified in these patients, others also with new strains detected did not
408
demonstrate large compositional shifts at exacerbation. Collectively, these observations
409
suggest there is likely inter-individual heterogeneity in the AECOPD-associated
410
microbiome.
18
411 412
Finally, our observations regarding the effects of COPD treatments on microbiota
413
composition have potentially important clinical implications. Inhaled corticosteroids are
414
commonly used in the management of COPD and other airway diseases, and our results
415
suggest that ICS use may alter the microbiome. Whether this has implications for
416
susceptibility to exacerbations or their outcome will require additional study. Also,
417
treatments for exacerbation led to significant changes in community composition, some
418
of which may last beyond the usual timeframe of clinical recovery from exacerbations.
419
In particular, oral steroid therapy alone led to community enrichment for many
420
microbiota. This suggests that steroids, even administered systemically, may promote
421
microbial colonization in the airways. Overall, the long-term effects of such treatments
422
on the microbiome in COPD, particularly with recurrent administrations, is unknown.
423 424
Strengths of this study include the systematically collected temporal samples examined,
425
encompassing key periods before, at, and after AECOPD. Furthermore, we extended
426
upon compositional analyses to explore the functional capacity encoded by key
427
microbiome members. Weaknesses include the small number of subjects, the focus on
428
only bacteria in examining the microbiome, and also use of sputum as a representative
429
specimen. Despite the potential for saliva admixture, sputum represents the only
430
practical approach for repeated airway sampling of COPD patients, particularly during
431
exacerbations when bronchoscopy may be unsafe. Important microbial insights have been
432
gleaned from analysis of sputum in many studies of COPD, including several from this
433
cohort (3, 4, 9, 44).
19
434 435
In summary, results of this study indicate that the context of the airway microbiome
436
needs to be considered in efforts to improve understanding of the pathogenesis of
437
AECOPD. Given the heterogeneity of COPD, it will be important to study larger
438
numbers of subjects to identify microbiome determinants associated with specific COPD
439
phenotypes and associated exacerbations.
440 441
ACKNOWLEDGEMENTS
442
This work was supported by a University of California Tobacco-related Disease Research
443
Program grant (17FT-0040) and a National Institutes of Health grant HL105572 to Y.J.H,
444
and an American Lung Association award to S.V.L.
445 446
The authors gratefully acknowledge Ali Faruqi for guidance in performing PICRUSt
447
analysis of microarray-identified taxa, and to Emily Cope for technical assistance with
448
Illumina MiSeq sequencing and guidance in the sequencing data analysis.
449 450 451 452 453 454 455 456
20
457
REFERENCES
458
1. Global strategy for the diagnosis, management and prevention of chronic obstructive
459
pulmonary disease. 2013. Available from http://www.goldcopd.org/. Accessed 1/6/2014.
460
2. Sethi S, Murphy TF. 2008. Infection in the pathogenesis and course of chronic
461
obstructive pulmonary disease. N. Engl. J. Med. 359:2355-2365.
462
3. Sethi S, Evans N, Grant BJ, Murphy TF. 2002. New strains of bacteria and
463
exacerbations of chronic obstructive pulmonary disease. N. Engl. J. Med. 347:465-471.
464
4. Murphy TF, Brauer AL, Eschberger K, Lobbins P, Grove L, Cai X, Sethi S.
465
Pseudomonas aeruginosa in chronic obstructive pulmonary disease. 2008. Am. J. Respir.
466
Crit. Care Med. 177:853-860.
467
5. Huang YJ, Kim E, Cox MJ, Brodie EL, Brown R, Wiener-Kronish JP, Lynch SV. 2010.
468
A persistent and diverse airway microbiota present during chronic obstructive pulmonary
469
disease exacerbations. OMICS. 14:9-59.
470
6. Erb-Downward JR, Thompson DL, Han MK, Freeman CM, McCloskey L, Schmidt
471
LA, Young VB, Toews GB, Curtis JL, Sundaram B, Martinez FJ, Huffnagle GB. 2011.
472
Analysis of the lung microbiome in the "healthy" smoker and in COPD. PLoS One.
473
6:e16384. doi:10.1371/journal.pone.0016384
474
7. Pragman AA, Kim HB, Reilly CS, Wendt C, Isaacson RE. 2012. The lung microbiome
475
in moderate and severe chronic obstructive pulmonary disease. PLoS One. 7:e47305.
476
doi:10.1371/journal.pone.0047305.
477
8. Cabrera-Rubio R, Garcia-Núñez M, Setó L, Antó JM, Moya A, Monsó E, Mira A.
478
2012. Microbiome diversity in the bronchial tracts of patients with chronic obstructive
479
pulmonary disease. J. Clin. Microbiol. 50:3562-8.
21
480
9. Papi A, Bellettato CM, Braccioni F, Romagnoli M, Casolari P, Caramori G, Fabbri LM,
481
Johnston SL. 2006. Infections and airway inflammation in chronic obstructive pulmonary
482
disease severe exacerbations. Am. J. Respir. Crit. Care Med. 173:1114-1121.
483
10. Rosell A, Monso E, Soler N, Torres F, Angrill J, Riise G, Zalacain R, Morera J,
484
Torres A. 2005. Microbiologic determinants of exacerbation in chronic obstructive
485
pulmonary disease. Arch. Intern. Med. 165:891-897.
486
11. Sze MA, Dimitriu PA, Hayashi S, Elliott WM, McDonough JE, Gosselink JV,
487
Cooper J, Sin DD, Mohn WW, Hogg JC. 2012. The lung tissue microbiome in chronic
488
obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 185:1073-80.
489
12. Duan K, Dammel C, Stein J, Rabin H, Surette MG. 2003. Modulation of
490
Pseudomonas aeruginosa gene expression by host microflora through interspecies
491
communication. Mol. Microbiol. 50:1477-1491.
492
13. Endt K, Stecher B, Chaffron S, Slack E, Tchitchek N, Benecke A, Van Maele L,
493
Sirard JC, Mueller AJ, Heikenwalder M, Macpherson AJ, Strugnell R, von Mering C,
494
Hardt WD. 2010. The microbiota mediates pathogen clearance from the gut lumen after
495
non-typhoidal Salmonella diarrhea. PLoS Pathog. 6:e1001097.
496
doi:10.1371/journal.ppat.1001097.
497
14. Sethi S, Wrona C, Eschberger K, Lobbins P, Cai X, Murphy TF. 2008. Inflammatory
498
profile of new bacterial strain exacerbations of chronic obstructive pulmonary disease.
499
Am. J. Respir. Crit. Care Med. 177:491-497.
500
15. Huang YJ, Nelson CE, Brodie EL, Desantis TZ, Baek MS, Liu J, Woyke T, Allgaier
501
M, Bristow J, Wiener-Kronish JP, Sutherland ER, King TS, Icitovic N, Martin RJ,
502
Calhoun WJ, Castro M, Denlinger LC, Dimango E, Kraft M, Peters SP, Wasserman SI,
22
503
Wechsler ME, Boushey HA, Lynch SV. 2011. Airway microbiota and bronchial
504
hyperresponsiveness in patients with suboptimally controlled asthma. J. Allergy Clin.
505
Immunol. 127:372-381.
506
16. Flanagan JL, Brodie EL, Weng L, Lynch SV, Garcia O, Brown R, Hugenholtz P,
507
DeSantis TZ, Andersen GL, Wiener-Kronish JP, Bristow J. 2007. Loss of bacterial
508
diversity during antibiotic treatment of intubated patients colonized with Pseudomonas
509
aeruginosa. J. Clin. Microbiol. 45:1954-1962.
510
17. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A,
511
Andersen GL, Knight R, Hugenholtz P. 2012. An improved Greengenes taxonomy with
512
explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J.
513
6:610-8.
514
18. Brodie EL, Desantis TZ, Joyner DC, Baek SM, Larsen JT, Andersen GL, Hazen TC,
515
Richardson PM, Herman DJ, Tokunaga TK, Wan JM, Firestone MK. 2006. Application
516
of a high-density oligonucleotide microarray approach to study bacterial population
517
dynamics during uranium reduction and reoxidation. Appl. Environ. Microbiol. 72:6288-
518
6298.
519
19. DeSantis TZ, Brodie EL, Moberg JP, Zubieta IX, Piceno YM, Andersen GL. 2007.
520
High-density universal 16S rRNA microarray analysis reveals broader diversity than
521
typical clone library when sampling the environment. Microb Ecol. 53:371-383.
522
20. Hazen TC, Dubinsky EA, DeSantis TZ, Andersen GL, Piceno YM, Singh N, Jansson
523
JK, Probst A, Borglin SE, Fortney JL, Stringfellow WT, Bill M, Conrad ME, Tom LM,
524
Chavarria KL, Alusi TR, Lamendella R, Joyner DC, Spier C, Baelum J, Auer M, Zemla
23
525
ML, Chakraborty R, Sonnenthal EL, D'haeseleer P, Holman HY, Osman S, Lu Z, Van
526
Nostrand JD, Deng Y, Zhou J, Mason OU. 2010. Deep-sea oil plume enriches indigenous
527
oil-degrading bacteria. Science. 330:204-8.
528
21. Handley KM, Wrighton KC, Piceno YM, Andersen GL, DeSantis TZ, Williams KH,
529
Wilkins MJ, N'Guessan AL, Peacock A, Bargar J, Long PE, Banfield JF. 2012. High-
530
density PhyloChip profiling of stimulated aquifer microbial communities reveals a
531
complex response to acetate amendment. FEMS Microbiol. Ecol. 81:188-204.
532
22. DeSantis TZ, Stone CE, Murray SR, Moberg JP, Andersen GL. 2005. Rapid
533
quantification and taxonomic classification of environmental DNA from both prokaryotic
534
and eukaryotic origins using a microarray. FEMS Microbiol. Lett. 245:271-8.
535
23. Faith DP. Conservation evaluation and phylogenetic diversity. 1992. Biol. Conserv.
536
61:1–10.
537
24. Oksanen J, Kindt R, Legendre P, O'Hara B, Simpson GL, Solymos P, Henry M,
538
Stevens H, Wagner H. 2008.Vegan: Community ecology package. R package version
539
1.16-1.
540
25. Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD,
541
Blomberg SP, Webb CO. 2010. Picante: R tools for integrating phylogenies and ecology.
542
Bioinformatics. 26:1463-1464.
543
26. Gotelli N, Ellison A. 2004. A primer of ecological statistics. Sinauer Associates, Inc.
544
Sunderland, MA.
545
27. Lozupone C, Knight R. 2005. UniFrac: a new phylogenetic method for comparing
546
microbial communities. Appl. Environ. Microbiol. 71: 8228–8235.
24
547
28. Anderson, M.J. 2001. A new method for non-parametric multivariate analysis of
548
variance. Austral Ecology. 26:32–46.
549
29. Smyth, G. K. 2004. Linear models and empirical Bayes methods for assessing
550
differential expression in microarray experiments. Statistical Applications in Genetics
551
and Molecular Biology. 3(1):Article3.
552
30. Storey JD, Tibshirani R. 2003. Statistical significance for genomewide studies. Proc.
553
Natl. Acad. Sci. U S A. 100:9440-5.
554
31. Sunagawa S, DeSantis TZ, Piceno YM, Brodie EL, DeSalvo MK, Voolstra CR, Weil
555
E, Andersen GL, Medina M. 2009. Bacterial diversity and White Plague Disease-
556
associated community changes in the Caribbean coral Montastraea faveolata. ISME J.
557
3:512-21..
558
32. Ivanov II, Atarashi K, Manel N, Brodie EL, Shima T, Karaoz U, Wei D, Goldfarb KC,
559
Santee CA, Lynch SV, Tanoue T, Imaoka A, Itoh K, Takeda K, Umesaki Y, Honda K,
560
Littman DR. 2009. Induction of intestinal Th17 cells by segmented filamentous bacteria.
561
Cell. 139:485-98.
562
33. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA,
563
Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C.
564
2013. Predictive functional profiling of microbial communities using 16S rRNA marker
565
gene sequences. Nat. Biotechnol. 31:814-21.
566
34. Pauwels RA, Buist SA, Calverley PM, Jenkins CR, Hurd SS. 2001. Global Strategy
567
for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary
568
Disease. Am. J. Respir. Crit. Care Med. 163: 1256-1276.
25
569
35. Ventura M, Canchaya C, Tauch A, Chandra G, Fitzgerald GF, Chater KF, van
570
Sinderen D. 2007. Genomics of Actinobacteria: tracing the evolutionary history of an
571
ancient phylum. Microbiol. Mol. Biol. Rev. 71:495-548.
572
36. Atarashi K, Tanoue T, Shima T, Imaoka A, Kuwahara T, Momose Y, Cheng G,
573
Yamasaki S, Saito T, Ohba Y, Taniguchi T, Takeda K, Hori S,Ivanov II, Umesaki Y, Itoh
574
K, Honda K. 2011. Induction of colonic regulatory T cells by indigenous Clostridium
575
species. Science. 331(6015):337-41.
576
37. Stecher B, Chaffron S, Kappeli R, Hapfelmeier S, Freedrich S, Weber TC, Kirundi J,
577
Suar M, McCoy KD, von Mering C, Macpherson AJ, Hardt WD. 2010. Like will to like:
578
Abundances of closely related species can predict susceptibility to intestinal colonization
579
by pathogenic and commensal bacteria. PLoS Pathog. 6:e1000711.
580
38. Cushnie TP, Lamb AJ. 2011. Recent advances in understanding the antibacterial
581
properties of flavonoids. Int. J. Antimicrob. Agents. 38:99-107.
582
39. Vulić JJ, Cebović TN, Canadanović VM, Cetković GS, Djilas SM, Canadanović-
583
Brunet JM, Velićanski AS, Cvetković DD, Tumbas VT. 2013. Antiradical, antimicrobial
584
and cytotoxic activities of commercial beetroot pomace. Food Funct. 4:713-721.
585
40. Zoraghi R, Worrall L, See RH, Strangman W, Popplewell WL, Gong H, Samaai T,
586
Swayze RD, Kaur S, Vuckovic M, Finlay BB, Brunham RC, McMaster WR, Davies-
587
Coleman MT, Strynadka NC, Andersen RJ, Reiner NE. 2011. Methicillin-resistant
588
Staphylococcus aureus (MRSA) pyruvate kinase as a target for bis-indole alkaloids with
589
antibacterial activities. J. Biol. Chem. 286:44716-25.
590
41. Miravitlles M, Soler-Cataluña JJ, Calle M, Soriano JB. 2013. Treatment of COPD by
591
clinical phenotypes: putting old evidence into clinical practice. Eur Respir J. 41:1252-6.
26
592
42. Armbruster CE, Hong W, Pang B, Weimer KE, Juneau RA, Turner J, Swords WE.
593
2010. Indirect pathogenicity of Haemophilus influenzae and Moraxella catarrhalis in
594
polymicrobial otitis media occurs via interspecies quorum signaling. MBio. 1:e00102-10..
595
43. Sibley CD, Duan K, Fischer C, Parkins MD, Storey DG, Rabin HR, Surette MG.
596
2008. Discerning the complexity of community interactions using a Drosophila model of
597
polymicrobial infections. PLoS Pathog. 4:e1000184..
598
44. Molyneaux PL, Mallia P, Cox MJ, Footitt J, Willis-Owen SA, Homola D, Trujillo-
599
Torralbo MB, Elkin S, Kon OM, Cookson WO, Moffatt MF, Johnston SL. 2013.
600
Outgrowth of the bacterial airway microbiome after rhinovirus exacerbation of chronic
601
obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 188:1224-31.
602
45. Zhao J, Schloss PD, Kalikin LM, Carmody LA, Foster BK, Petrosino JF, Cavalcoli
603
JD, Vandevanter DR, Murray S, Li JZ, Young VB, Lipuma JJ. 2012. Decade-long
604
bacterial community dynamics in cystic fibrosis airways. Proc Natl Acad Sci U S A.
605
109:5809-5814.
606
46. Tunney, MM, Einarsson, GG, Wei, L, Drain, M, Klem, ER, Cardwell, C, Ennis, M,
607
Boucher, RC, Wolfgang, MC & Elborn, JS. 2013. Lung microbiota and bacterial
608
abundance in patients with bronchiectasis when clinically stable and during exacerbation.
609
Am. J. Respir. Crit. Care Med. 187:1118-1126.
610
47. Hajishengallis G, Liang S, Payne MA, Hashim A, Jotwani R, Eskan MA, McIntosh
611
ML, Alsam A, Kirkwood KL, Lambris JD, Darveau RP, Curtis MA. 2011. Low-
612
abundance biofilm species orchestrates inflammatory periodontal disease through the
613
commensal microbiota and complement. Cell Host Microbe. 10:497-506.
614
27
Table 1. Clinical characteristics of subjects and exacerbations. All subjects were Caucasian male veterans. Age, lung function and smoking status information were collected during clinically stable state at parent study enrollment. Inhaled corticosteroid use status was current for analyzed time points. Subject ID
Age
FEV1 (%)
Smoking status (pack-yrs)
Inhaled steroid use
Clinical score at Exac
Sputum purulence at Exac
Sputum culture result from Exac sample
Pre-existing or new bacterial strain at Exac
Antibiotic for Exac
3
67
7
65
Systemic steroids for Exac
91
Ex (10)
Yes
20
Purulent
P. aeruginosa
New
Azithromycin
No
63
Ex (105)
No
18
Purulent
H. influenzae
New
Ofloxacin
No
19
51
63
Current (48)
Yes
19
Purulent
M. catarrhalis
New
Azithromycin
No
40
63
56
Current (112)
Yes
14
Mucoid
None
NA
None
Yes
46
71
48
Ex (125)
No
13
Mucopurulent
M. catarrhalis Klebsiella spp.
Pre-existing NA
None
Yes
48
64
46
Current (45)
Yes
15
Mucoid
None
NA
None
Yes
49
66
61
Ex (100)
Yes
24
Purulent
P. fluorescens
NA
Azithromycin
Yes
56
61
42
Current (135)
Yes
22
Purulent
H. influenzae
New
Ciprofloxacin
Yes
Mucoid
H. influenzae M. catarrhalis S. pneumoniae
Pre-existing Pre-existing Pre-existing
63 67
80 66
51 48
Ex (87) Current (50)
Yes No
13 23
None
Yes
Trimethoprim/ sulfamethoxazole
No
Purulent
S. pneumoniae
New Pre-existing New
Ofloxacin
Yes
New
Gatifloxacin
Yes
74
72
55
Ex (96)
Yes
21
Mucopurulent
H. influenzae, P. aeruginosa
111
68
38
Current (108)
Yes
23
Purulent
M. catarrhalis
Table 2. List of bacterial taxa that demonstrated the largest changes in relative abundance (> 2-fold, p-value < 0.05, q-value < 0.30), at the onset of exacerbation compared to the most recent pre-exacerbation time point (Pre2). The results are based on analysis of all subjects in the study (n = 12 subjects). Taxon (Class/Family/Genus)
prokMSA_ID for taxon representative species
γ-Proteobacteria/Moraxellaceae/Acinetobacter γ-Proteobacteria/Moraxellaceae/Perlucidibaca γ-Proteobacteria/Moraxellaceae/Acinetobacter γ-Proteobacteria/Moraxellaceae/Acinetobacter β-Proteobacteria/Anaplasmataceae/Ehrlichia γ-Proteobacteria/Pasteurellaceae/Actinobacillus α-Proteobacteria/Sphinogomonadaceae/Sphingomonas β-Proteobacteria/Comamonadaceae/Comamonas γ-Proteobacteria/Enterobacteriaceae γ-Proteobacteria/Pseudomonadaceeae/Pseudomonas Bacilli/Bacillaceae/unclassified ε-Proteobacteria/Campylobacteraceae/Campylobacter Bacteroidia/Rikenellaceae/unclassified Clostridia/Clostridiales Family XIII/Mogibacterium Actinobacteria/ Microbacteriaceae/Agromyces Clostridia/Lachnospiraceae/unclassified Actinobacteria/ACK-M1/unclassified Actinobacteria/Micrococcaceae/Rothia Actinobacteria/Nocardiaceae/Rhodococcus
60270 105696 106871 101304 6030 52835 89065 71872 10365 8434 89195 108770 101395 14167 12174 72977 96076 58305 85556
Fold-change (increase or decrease at exacerbation) +2.9 +2.7 +2.4 +2.4 +2.2 +2.1 +2.1 +2.0 +2.0 +2.0 -1.9 -1.9 -1.9 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8
p-value
0.01 0.02 0.04, 0.006 0.02 0.04 0.01 0.03 0.04 0.01 0.04 0.03 0.01 0.002 0.02 0.005 0.01 0.03 0.04
1
Figure legends.
2 3
Figure 1. Time points of samples collected before, at the onset of, and after an acute COPD
4
exacerbation. Line coloring indicates the type of exacerbation treatment the subject received.
5 6
Figure 2. A. Non-metric multidimensional scaling (NMDS) ordination demonstrates that
7
samples from all subjects in the study segregate into distinct clusters (indicated by ellipses) based
8
on the type of treatment prescribed for exacerbation [antibiotics (Abx) only, oral steroids only, or
9
both; n = 4 subjects per treatment group; Bray-Curtis distance-based PERMANOVA, p < 0.05].
10
Ellipses represent 95% confidence interval for the standard error of the distances of samples in
11
each treatment group. B. NMDS plot of only post-exacerbation treatment samples from all
12
subjects demonstrates that the relationship between sample community composition and
13
treatment type remains strong, though not statistically significant (p = 0.08), when the analysis is
14
limited to only these samples. C. NMDS plot of pre-exacerbation samples alone from all subjects
15
shows that chronic use of inhaled corticosteroids may be associated with differences in bacterial
16
community composition (Bray-Curtis distance-based PERMANOVA, p = 0.08).
17 18
Figure 3. A. Volcano plots indicating taxa that are significantly increased (upper right quadrant)
19
or decreased (upper left quadrant) in pair-wise comparisons as shown, using moderated t-test
20
models (R package limma). Results shown are from analyses of all subjects. Dashed lines
21
indicate significant FDR-adjusted p-values and changes in relative abundance of at least 2-fold,
22
or log2 =1. Taxa exhibiting significant changes are colored by phylum-level classification as
23
shown. Note that in addition to highlighted taxa, many other microbiota members exhibit
24
smaller scale changes in abundance, which cumulatively may contribute importantly to
25
microbiome community function, B. Changes in taxa relative abundance from Exac to Post1 and
26
Post1 to Post2, segregated by type of exacerbation treatment (antibiotics only, systemic
27
corticosteroids only, or both; n = 4 subjects in each treatment group).
28 29
Figure 4. Correlations between the relative abundance of H. influenzae (a member of the
30
Pasteurellaceae/GammaProteobacteria) and that of all other identified taxa. The analysis was
31
performed using data from all 60 samples in this study. Significant positive and negative
32
correlations are shown (Pearson R > 0.5, Benjamini-Hochberg adjusted p < 0.05). Positive
33
correlations (red) occur predominantly with members of Pasteurellaceae or closely related
34
bacterial families and classes of Proteobacteria. Negative correlations (blue) occur with bacterial
35
families and classes that phylogenetically are more distant to H. influenzae/Pasteurellaeae. Tree
36
branches are color-coded by bacterial class (legend).
37 38
Figure 5. KEGG pathways associated with predicted gene functions (KOs) encoded by
39
metagenomes of bacterial taxa that either were increased (red) or decreased (blue) ≥ 2-fold in
40
relative abundance at the time of and after exacerbations. Among the microbiome-related
41
functions identified were those involved in promotion of inflammation by pathogens (*) and
42
production of anti-microbial and anti-inflammatory compounds (**), functions that were
43
differentially represented at exacerbation compared to post-exacerbation.
Exacerbation onset subj 111 subj 74 subj 56
Exacerbation treatment group
subj 49 subj 63
Antibiotics only Corticosteroids only Both antibiotics & corticosteroids
subj 48 subj 46 subj 40 subj 67 subj 19 subj 7 subj 3
-150
-100
-50
0
50
Days relative to exacerbation onset
Figure 1.
100
150
0.05
111Ex
Both
63Ex 56Ex
111Pre1 111Post1
46Post2 63Post2 63Pre1 63Post1
0.00
NMDS2
63Pre2
111Pre2
40Pre1
-0.05
Steroid only
46Pre2 7Post1 111Post2 48Pre1 67Post2 56Post1 7Pre1 49Post1 7Ex 74Pre1 48Ex 67Ex 74Post1 67Pre1 40Pre2 74Ex 48Post2 67Post1 74Post2 56Pre2 1 7Post2 48Post 46Post1 56Pre1 48Pre2 49Pre2 7Pre2 56Post2 74Pre2 46Ex 67Pre2 40Post2 46Pre1 49Pre1 49Post2 40Post1 40Ex 3Pre1 3Ex 3Post1 3Post2 49Ex19Pre2 19Post2 19Pre1 19Post1 3Pre2 Abx only 19Ex
-0.05
0.00 NMDS1
Figure 2A.
0.05
0.06 0.04
Steroid only
0.00
NMDS2
0.02
40Post1 40Post2 56Post2 48Post2 48Post1 49Post2 63Post1
49Post1 3Post1
-0.02
63Post2
7Post2 74Post2 19Post1 7Post1 46Post1 74Post1 67Post2
56Post1 67Post1
Both
111Post2 19Post2
111Post1
3Post2
Abx only
-0.06
-0.04
46Post2
-0.05
0.00
0.05 NMDS1
Figure 2B.
Figure 2C.
Clostridiaceae/Lachnospiraceae (Clostridia)
Classes (tree branches): Gammaproteobacteria Verrucomicrobia e Chlamydiae Aquificae gut clone group Catabacter Bacilli Fusobacteria Mollicutes Symbiobacteria Deltaproteobacteria Betaproteobacteria Alphaproteobacteria Acidobacteria NC10−2 Spirochaetes Sphingobacteria Bacteroidetes Flavobacteria Epsilonproteobacteria mgA−1 TM7−3 Cyanobacteria OP9 Actinobacteria
Bacillaceae
Staphylococcus spp.
Lactobacillus spp.
Streptococcaceae (Lactobacillales)
Enterobacteriaceae Helicobacter Campylobacter (Epsilonproteobacteria)
0.01
Pasteurellaceae (includes genus Haemophilus)
Abundance Higher
Lower Positive correlation Negative correlation
Deltaproteobacteria
Pseudomonadaceae Moraxellaceae Oxalobacteraceae (Betaproteobacteria)
Figure 4.
KEGG pathways related to biologic functions represented by predicted KOs Apoptosis Basal transcription factors Betalain biosynthesis** Biosynthesis of 12-,14-, and 16-membered macrolides** Circadian rhythm-plant Cytochrome P450 Epithelial cell signaling in H. pylori infection* Ether lipid metabolism Flavone and flavonol biosynthesis** Hypertrophic cardiomyopathy Indole alkaloid biosynthesis** Influenza A* p53 signaling pathway Renin-angiotensin system RIG-I-like receptor signaling pathway Staphylococcus aureus infection* Steroid biosynthesis Toxoplasmosis Transcription related proteins Viral myocarditis
Figure 5.
Pre2 to Exac
Exac to Post1
1
Airway Microbiome Dynamics in Exacerbations of Chronic Obstructive
2
Pulmonary Disease
3 4
Yvonne J. Huang1, Sanjay Sethi2,3, Timothy Murphy3, Snehal Nariya1, Homer A.
5
Boushey1, Susan V. Lynch4
6 7
1
8
University of California San Francisco
9
2
Division of Pulmonary/Critical Care/Allergy/Sleep Medicine; Department of Medicine,
Division of Pulmonary/Critical Care/Sleep Medicine; Veterans Western New York
10
Health System, Buffalo, New York
11
3
Department of Medicine, University at Buffalo State University of New York
12
4
Division of Gastroenterology; Department of Medicine, University of California San
13
Francisco
14 15
Corresponding author:
16
Yvonne J. Huang, MD
17
505 Parnassus Avenue, Box 0130
18
San Francisco, CA 94143-0130
19
Email: [email protected]
20
Tel: (415) 476-9456; Fax: (888) 786-7576
21 22 23
24
ABSTRACT
25
Specific bacterial species are implicated in the pathogenesis of exacerbations of chronic
26
obstructive pulmonary disease (COPD). However, recent studies of clinically stable
27
COPD patients have demonstrated a greater diversity of airway microbiota, whose role in
28
acute exacerbations is unclear. In this study temporal changes in the airway microbiome
29
before, at the onset of, and after an acute exacerbation were examined in 60 sputum
30
samples collected from subjects enrolled in a longitudinal study of bacterial infection in
31
COPD. Microbiome composition and predicted functions were examined using 16S
32
rRNA-based culture-independent profiling methods. Shifts in the abundance (>2-fold,
33
p 4 weeks) from an individual patient
92
(3).
93 94
To investigate the exacerbation-related microbiome, subjects and samples were carefully
95
selected for this study to include a diversity of exacerbations, those that were and were
96
not associated with a new strain, and also treatment approaches commonly prescribed in
97
clinical practice (antibiotics only, systemic corticosteroids only, or both). Five time
98
points per subject were examined (Figure 1). The specimens spanned an exacerbation-
99
related time period occurring in a given year between 1999 and 2005 (specific year
100
depending on the subject). Pre- and post-exacerbation samples were those available
101
closest to the exacerbation when the patients were at their baseline respiratory state and
102
on their usual COPD treatment. All subjects were encouraged to seek treatment early for
103
a suspected exacerbation, however a fixed duration of symptoms was not proscribed.
104
However, all exacerbations samples included in this study were obtained prior to
105
initiation of any specific treatment for the exacerbation. Exacerbation events were
106
determined using pre-defined criteria described previously (3), involving evaluation of
107
respiratory symptoms (dyspnea, cough, sputum production, viscosity and purulence) and
108
their change from usual state. Pneumonia was ruled out by chest x-ray if suspected.
109
Decisions about treatments were made by study physicians (Drs. Sethi and Murphy)
110
based on symptoms (especially sputum purulence) and prior to the availability of sputum
111
culture data from the concurrent visit. Antibiotics were prescribed if bacterial infection
112
was thought to be a cause of the exacerbation, whereas steroids only were prescribed if a
113
non-bacterial etiology was suspected.
5
114 115
Sample preparations
116
DNA was extracted utilizing methods previously described (15) and detailed further in
117
the supplemental material. Briefly, universal 16S rRNA bacterial primers (Bact-27F,
118
Bact-1492R) were used to generate amplicons which were processed for hybridization to
119
G2 PhyloChip microarrays (Second Genome; South San Francisco, CA) by methods
120
previously described (15, 16). Each sample was hybridized to an individual array,
121
resulting in 60 microbiome profiles. Bacterial taxa were defined as organisms sharing ≥
122
97% 16S rRNA gene sequence homology and classified according to Hugenholtz
123
taxonomy in the 2011 iteration of the Greengenes database (17).
124 125
Data analyses
126
Raw array data were processed as previously described (18-20), including correction for
127
background and noise, scaling of fluorescence intensities to spiked-in quantitative
128
standards, followed by further data normalization based on the mean intensity of all
129
profiles. Presence of taxa was determined using published methods (20, 21) with slightly
130
modified scoring thresholds applied based on spiked-in quantitative standards. Further
131
information is detailed in the supplemental material. The initial normalized dataset
132
combined for all 60 samples included ~ 4,800 taxa, which was then filtered to include
133
taxa present in at least 10% of samples, yielding a final dataset of ~3,300 taxa for
134
analysis (available in Table S5). Log2-transformed intensities, which correlate with taxa
135
relative abundance (22), were used in all primary analyses which were performed using R
136
version 2.14.1.
6
137 138
Community richness (number of taxa detected), evenness (relative distribution of taxa in
139
a community) and diversity [Shannon and inverse Simpson indices, which consider both
140
richness and evenness in the calculation, as well as Faith’s phylogenetic diversity which
141
additionally incorporates phylogenetic information in this calculation (23)] were
142
calculated using R packages vegan and picante (24, 25). Changes in these metrics were
143
tested using repeated measures ANOVA (RM-ANOVA) and paired t-tests. Bray-Curtis
144
and Unifrac distance measures (26, 27) were calculated and used in ordination analyses
145
(e.g. non-metric multidimensional scaling, NMDS) to assess compositional differences
146
and relationships to clinical factors, including the timing of pre- and post-exacerbation
147
sample collection relative to exacerbation onset date, by distance matrix-based
148
PERMANOVA (28). Taxon-level analyses to identify specific compositional changes
149
between time points were performed using linear models based on moderated t-statistics
150
and a paired test design in R package limma (29). Adjustment for multiple comparisons
151
was applied using the Benjamini-Hochberg method or q-values (30). Changes of at least
152
2-fold (log2 difference > 1.0; unadjusted p 2-fold. Overall, these
230
observations suggest relative stability in airway microbiota composition during a
231
clinically stable period prior to AECOPD in this cohort.
232 233
At exacerbation, it was predominantly members of the Proteobacteria such as
234
Moraxellaceae, Pasteurellaceae, Pseudomonadaceae, and Enterobacteriaceae that were
235
enriched, when compared to Pre2 (Table 2). These enriched taxa included other
236
potential respiratory pathogens in addition to those typically associated with AECOPD.
237
In contrast, taxa that were decreased in abundance at exacerbation relative to Pre2, were
238
predominantly Actinobacteria, Clostridia and Bacteroidia (Table 2). Actinobacteria
239
comprise a large group of metabolically diverse organisms that are prolific producers of
240
secondary metabolites, including those with antimicrobial activity (35), while clade IV
241
and XIVA Clostridia are known inducers of anti-inflammatory T-regulatory cells (36). To
242
speculate, these observations suggest the possibility that members of the microbiome that
243
are depleted at the time of exacerbation may otherwise help maintain community and
244
host immune homeostasis and potentially counteract detrimental effects of respiratory
245
pathogenic species.
246 247
The most dramatic changes in community composition were observed after treatment for
248
exacerbation (Exac to Post1). This was supported by a significant difference in the mean
249
Unifrac distance of communities between Exac and Post1 compared to that for
250
communities between Pre2 and Exac, reflecting a significant change in phylogenetic
11
251
make-up of the community (Fig. S1; p < 0.01). Among those taxa exhibiting significant
252
alterations in relative abundance,, the vast majority decreased in abundance, were
253
primarily members of the Proteobacteria, and included organisms both associated and not
254
traditionally associated with COPD (p < 0.05, q-value < 0.12; Table S1).
255 256
Striking differences, however, were seen in the effects of different treatments on
257
community composition (Fig. 3B). Treatment with antibiotics alone reduced the
258
abundance of many taxa (mostly Proteobacteria), a number of which exhibited further
259
reductions in relative abundance at the second post-treatment time point (Post1 to Post2).
260
The latter supports our data indicating a relationship between treatment type and
261
community composition in post-treatment samples and provides evidence for the
262
prolonged suppressive effects of antibiotics on some microbiota members. For subjects
263
who received any antibiotics (either alone or with steroids), a similar pattern of reduction
264
in microbiota members at Post1 was observed. However, in contrast to those treated with
265
antibiotics-only, those treated with both antibiotics and steroids showed a significant
266
increase in abundance of primarily Proteobacteria from Post1 to Post2. This was
267
reflected also in an increase in phylogenetic diversity at Post2 compared to Post1 for this
268
group (p < 0.01) and suggests that recovery of airway community diversity following
269
antibiotic treatment could be influenced by concomitant corticosteroid treatment.
270 271
In contrast to antibiotics, treatment with corticosteroids alone led predominantly to
272
enrichment for many taxa, mainly Proteobacteria but also Bacteroidetes and Firmicutes
273
members (Fig. 3B). Large increases were observed especially for Enterobacteriaceae (up
12
274
to 16-fold), Lachnospiraceae, Burkholderiaceae, and Neisseriaceae (Table S2). This was
275
mirrored by a significantly higher mean 16S rRNA copy number, a proxy for bacterial
276
burden, in this group compared to the other treatment groups (Figure S2; RM-ANOVA p
277
< 0.02; steroids only vs. Abx only group, p 0.5, BH-adjusted p < 0.05), e.g. Pasteurellaceae,
290
Enterobacteriaceae, and Pseudomonadaceae (Figure 4). Most belong to the larger class
291
of γ-Proteobacteria, as well as other classes of Proteobacteria. In contrast, taxa
292
negatively correlated with H. influenzae were primarily members of more
293
phylogenetically distant groups. Similar correlation patterns were observed for P.
294
aeruginosa and M. catarrhalis (data not shown). These observations suggest that
295
enrichment of phylogenetically related organisms in a pathogen-rich airway milieu may
13
296
represent an important factor in microbiome dynamics associated with the pathogenesis
297
of exacerbations.
298 299
Quantitative PCR and sequencing validation studies
300
Total 16S rRNA gene copy numbers ranged from 1.9 x 105 to 1.8 x 108 (Figure S2).
301
While these did not differ significantly over time, type of treatment for exacerbation was
302
a significant factor in observed differences (RM-ANOVA, p = 0.02) with the group
303
treated with corticosteroids alone demonstrating higher mean total 16S rRNA copies
304
(steroids-only vs. Abx-only; p 2-fold were
330
used as PICRUSt inputs. The results predicted a number of KEGG gene orthologs (KOs;
331
Kyoto Encyclopedia of Genes and Genomes, release 67.1) associated with known KEGG
332
pathways, as enriched or depleted within the bacterial community at these time points
333
(Figure 5). Predicted functions enriched at exacerbation included pathways involved in
334
viral and bacterial infection (e.g. influenza A, S. aureus infection), as well as those
335
involved in apoptosis and p53 signaling. Conversely, functions predicted to be depleted
336
involved pathways associated with response to viral infection (e.g. RIG-I like receptor
337
signaling), and pathways involved in flavonoid and steroid biosynthesis [known anti-
338
inflammatory mediators (38)], as well as betalain and indole alkaloid production [known
339
anti-microbial properties (39, 40)]. These observations suggest that loss of community
340
functions associated with maintenance of microbial homeostasis and regulation of host
341
immune response are associated with AECOPD. This is further supported by the
15
342
observation that after treatment, reserve trends were observed. For example, predicted
343
KOs that were enriched for after exacerbation included those involved in betalain, indole
344
alkaloid, and flavonol biosynthesis pathways. In addition, KOs involved in macrolide
345
biosynthesis also were predicted to be enriched post-treatment. Thus microbiome
346
enrichment for taxa that encode these anti-inflammatory functional capacities may
347
contribute to recovery from exacerbation.
348 349
DISCUSSION
350
In this study we extensively examined dynamics of the bacterial airway microbiome in
351
the setting of AECOPD. In addition to assessment of compositional changes by multiple
352
methods, we further explored the predicted functional capacities encoded by microbiota
353
identified as potentially important based on observed compositional shifts. Thus this
354
study represents the most comprehensive effort to date to evaluate the exacerbation-
355
associated microbiome in COPD and serves as a foundation for approaches that could be
356
applied to study larger numbers of patients. Collectively, our findings demonstrate that
357
the COPD airway microbiome is rich in both bacterial species and functional capacity,
358
and that identifiable changes in this microbiome are associated with the development,
359
treatment, and resolution of AECOPD.
360 361
An important limitation of our study is that the intensive microbiome analyses performed
362
on sixty samples derived from only a small number of COPD patients. Offsetting this
363
limitation is the longitudinal nature of the study, providing us with valid pre-exacerbation
364
samples, as well as exacerbation samples prior to any specific treatment for exacerbation.
16
365
Future studies involving larger patient numbers will be necessary, especially given the
366
heterogeneity of COPD and interest in identifying best treatment approaches for different
367
phenotypes (41). Despite the cohort size, we were able to capture some of the
368
heterogeneity associated with exacerbations by intentionally including a proportionally
369
even number of subjects treated for exacerbation using regimens prescribed in clinical
370
practice. In this context, we were able to identify microbiome characteristics
371
differentially associated with features of exacerbations in these subjects. Follow-up
372
studies involving larger numbers of carefully phenotyped patients would be of interest.
373 374
Several specific findings from our study are important to emphasize and have potential
375
clinical implications to be further investigated. An important overall observation from a
376
microbial community perspective is that many bacterial groups, not limited to typical
377
COPD pathogens, were amongst those contributing to the most salient compositional
378
changes observed. These dynamics, involving Proteobacteria taxa in addition to those
379
representative of H. influenzae, M. catarrhalis and P. aeruginosa, suggest potentially
380
important contributions of related microbiota to the etiopathogenesis of AECOPD.
381
Independent analyses confirmed that the abundance of these typical COPD pathogens
382
was highly correlated with the presence of related bacterial phylotypes. While array
383
cross-hybridization at the taxon level is a possible factor in these analyses, it unlikely
384
accounts for the number of distinct groups, characterized at family-level and higher,
385
found to be correlated with these species. Our observations are consistent with previous
386
demonstrations that organisms in an ecosystem can promote enrichment for related
387
species in a ‘like will to like’ phenomenon, as observed for gut microbiota and
17
388
susceptibility to pathogen colonization (37). Moreover, quorum-sensing mechanisms
389
between organisms can influence bacterial pathogenicity or resistance to antimicrobials
390
(42, 43). Overall, findings from this and other studies (44) indicate that the context of the
391
microbial community is important to consider in efforts to better understand the
392
pathogenesis of AECOPD.
393 394
Another important observation is that inter-subject variability in microbial community
395
changes at exacerbation were seen. Similar observations were made in a study using a
396
human model of viral-induced COPD exacerbation (44). In our study, compositional
397
changes at exacerbation in general did not involve very large shifts, although this was not
398
true of all subjects. This is similar to what has been described in exacerbations of cystic
399
fibrosis and non-CF bronchiectasis (45, 46). This suggests that AECOPD could also
400
result from cumulative effects of smaller scale changes in community composition that,
401
based on PICRUSt functional predictions, lead not only to an increase in pathogen-
402
elicited inflammation but also loss of potentially key protective functions in the
403
microbiome. Moreover, organisms do not need to be highly prevalent in a community to
404
have important effects on disease pathogenesis, as has been shown for periodontitis (47).
405
However, two of our subjects (#56 and #111) did exhibit a greater compositional shift at
406
exacerbation based on NMDS analysis (data not shown). Although new bacterial strains
407
were identified in these patients, others also with new strains detected did not
408
demonstrate large compositional shifts at exacerbation. Collectively, these observations
409
suggest there is likely inter-individual heterogeneity in the AECOPD-associated
410
microbiome.
18
411 412
Finally, our observations regarding the effects of COPD treatments on microbiota
413
composition have potentially important clinical implications. Inhaled corticosteroids are
414
commonly used in the management of COPD and other airway diseases, and our results
415
suggest that ICS use may alter the microbiome. Whether this has implications for
416
susceptibility to exacerbations or their outcome will require additional study. Also,
417
treatments for exacerbation led to significant changes in community composition, some
418
of which may last beyond the usual timeframe of clinical recovery from exacerbations.
419
In particular, oral steroid therapy alone led to community enrichment for many
420
microbiota. This suggests that steroids, even administered systemically, may promote
421
microbial colonization in the airways. Overall, the long-term effects of such treatments
422
on the microbiome in COPD, particularly with recurrent administrations, is unknown.
423 424
Strengths of this study include the systematically collected temporal samples examined,
425
encompassing key periods before, at, and after AECOPD. Furthermore, we extended
426
upon compositional analyses to explore the functional capacity encoded by key
427
microbiome members. Weaknesses include the small number of subjects, the focus on
428
only bacteria in examining the microbiome, and also use of sputum as a representative
429
specimen. Despite the potential for saliva admixture, sputum represents the only
430
practical approach for repeated airway sampling of COPD patients, particularly during
431
exacerbations when bronchoscopy may be unsafe. Important microbial insights have been
432
gleaned from analysis of sputum in many studies of COPD, including several from this
433
cohort (3, 4, 9, 44).
19
434 435
In summary, results of this study indicate that the context of the airway microbiome
436
needs to be considered in efforts to improve understanding of the pathogenesis of
437
AECOPD. Given the heterogeneity of COPD, it will be important to study larger
438
numbers of subjects to identify microbiome determinants associated with specific COPD
439
phenotypes and associated exacerbations.
440 441
ACKNOWLEDGEMENTS
442
This work was supported by a University of California Tobacco-related Disease Research
443
Program grant (17FT-0040) and a National Institutes of Health grant HL105572 to Y.J.H,
444
and an American Lung Association award to S.V.L.
445 446
The authors gratefully acknowledge Ali Faruqi for guidance in performing PICRUSt
447
analysis of microarray-identified taxa, and to Emily Cope for technical assistance with
448
Illumina MiSeq sequencing and guidance in the sequencing data analysis.
449 450 451 452 453 454 455 456
20
457
REFERENCES
458
1. Global strategy for the diagnosis, management and prevention of chronic obstructive
459
pulmonary disease. 2013. Available from http://www.goldcopd.org/. Accessed 1/6/2014.
460
2. Sethi S, Murphy TF. 2008. Infection in the pathogenesis and course of chronic
461
obstructive pulmonary disease. N. Engl. J. Med. 359:2355-2365.
462
3. Sethi S, Evans N, Grant BJ, Murphy TF. 2002. New strains of bacteria and
463
exacerbations of chronic obstructive pulmonary disease. N. Engl. J. Med. 347:465-471.
464
4. Murphy TF, Brauer AL, Eschberger K, Lobbins P, Grove L, Cai X, Sethi S.
465
Pseudomonas aeruginosa in chronic obstructive pulmonary disease. 2008. Am. J. Respir.
466
Crit. Care Med. 177:853-860.
467
5. Huang YJ, Kim E, Cox MJ, Brodie EL, Brown R, Wiener-Kronish JP, Lynch SV. 2010.
468
A persistent and diverse airway microbiota present during chronic obstructive pulmonary
469
disease exacerbations. OMICS. 14:9-59.
470
6. Erb-Downward JR, Thompson DL, Han MK, Freeman CM, McCloskey L, Schmidt
471
LA, Young VB, Toews GB, Curtis JL, Sundaram B, Martinez FJ, Huffnagle GB. 2011.
472
Analysis of the lung microbiome in the "healthy" smoker and in COPD. PLoS One.
473
6:e16384. doi:10.1371/journal.pone.0016384
474
7. Pragman AA, Kim HB, Reilly CS, Wendt C, Isaacson RE. 2012. The lung microbiome
475
in moderate and severe chronic obstructive pulmonary disease. PLoS One. 7:e47305.
476
doi:10.1371/journal.pone.0047305.
477
8. Cabrera-Rubio R, Garcia-Núñez M, Setó L, Antó JM, Moya A, Monsó E, Mira A.
478
2012. Microbiome diversity in the bronchial tracts of patients with chronic obstructive
479
pulmonary disease. J. Clin. Microbiol. 50:3562-8.
21
480
9. Papi A, Bellettato CM, Braccioni F, Romagnoli M, Casolari P, Caramori G, Fabbri LM,
481
Johnston SL. 2006. Infections and airway inflammation in chronic obstructive pulmonary
482
disease severe exacerbations. Am. J. Respir. Crit. Care Med. 173:1114-1121.
483
10. Rosell A, Monso E, Soler N, Torres F, Angrill J, Riise G, Zalacain R, Morera J,
484
Torres A. 2005. Microbiologic determinants of exacerbation in chronic obstructive
485
pulmonary disease. Arch. Intern. Med. 165:891-897.
486
11. Sze MA, Dimitriu PA, Hayashi S, Elliott WM, McDonough JE, Gosselink JV,
487
Cooper J, Sin DD, Mohn WW, Hogg JC. 2012. The lung tissue microbiome in chronic
488
obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 185:1073-80.
489
12. Duan K, Dammel C, Stein J, Rabin H, Surette MG. 2003. Modulation of
490
Pseudomonas aeruginosa gene expression by host microflora through interspecies
491
communication. Mol. Microbiol. 50:1477-1491.
492
13. Endt K, Stecher B, Chaffron S, Slack E, Tchitchek N, Benecke A, Van Maele L,
493
Sirard JC, Mueller AJ, Heikenwalder M, Macpherson AJ, Strugnell R, von Mering C,
494
Hardt WD. 2010. The microbiota mediates pathogen clearance from the gut lumen after
495
non-typhoidal Salmonella diarrhea. PLoS Pathog. 6:e1001097.
496
doi:10.1371/journal.ppat.1001097.
497
14. Sethi S, Wrona C, Eschberger K, Lobbins P, Cai X, Murphy TF. 2008. Inflammatory
498
profile of new bacterial strain exacerbations of chronic obstructive pulmonary disease.
499
Am. J. Respir. Crit. Care Med. 177:491-497.
500
15. Huang YJ, Nelson CE, Brodie EL, Desantis TZ, Baek MS, Liu J, Woyke T, Allgaier
501
M, Bristow J, Wiener-Kronish JP, Sutherland ER, King TS, Icitovic N, Martin RJ,
502
Calhoun WJ, Castro M, Denlinger LC, Dimango E, Kraft M, Peters SP, Wasserman SI,
22
503
Wechsler ME, Boushey HA, Lynch SV. 2011. Airway microbiota and bronchial
504
hyperresponsiveness in patients with suboptimally controlled asthma. J. Allergy Clin.
505
Immunol. 127:372-381.
506
16. Flanagan JL, Brodie EL, Weng L, Lynch SV, Garcia O, Brown R, Hugenholtz P,
507
DeSantis TZ, Andersen GL, Wiener-Kronish JP, Bristow J. 2007. Loss of bacterial
508
diversity during antibiotic treatment of intubated patients colonized with Pseudomonas
509
aeruginosa. J. Clin. Microbiol. 45:1954-1962.
510
17. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A,
511
Andersen GL, Knight R, Hugenholtz P. 2012. An improved Greengenes taxonomy with
512
explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J.
513
6:610-8.
514
18. Brodie EL, Desantis TZ, Joyner DC, Baek SM, Larsen JT, Andersen GL, Hazen TC,
515
Richardson PM, Herman DJ, Tokunaga TK, Wan JM, Firestone MK. 2006. Application
516
of a high-density oligonucleotide microarray approach to study bacterial population
517
dynamics during uranium reduction and reoxidation. Appl. Environ. Microbiol. 72:6288-
518
6298.
519
19. DeSantis TZ, Brodie EL, Moberg JP, Zubieta IX, Piceno YM, Andersen GL. 2007.
520
High-density universal 16S rRNA microarray analysis reveals broader diversity than
521
typical clone library when sampling the environment. Microb Ecol. 53:371-383.
522
20. Hazen TC, Dubinsky EA, DeSantis TZ, Andersen GL, Piceno YM, Singh N, Jansson
523
JK, Probst A, Borglin SE, Fortney JL, Stringfellow WT, Bill M, Conrad ME, Tom LM,
524
Chavarria KL, Alusi TR, Lamendella R, Joyner DC, Spier C, Baelum J, Auer M, Zemla
23
525
ML, Chakraborty R, Sonnenthal EL, D'haeseleer P, Holman HY, Osman S, Lu Z, Van
526
Nostrand JD, Deng Y, Zhou J, Mason OU. 2010. Deep-sea oil plume enriches indigenous
527
oil-degrading bacteria. Science. 330:204-8.
528
21. Handley KM, Wrighton KC, Piceno YM, Andersen GL, DeSantis TZ, Williams KH,
529
Wilkins MJ, N'Guessan AL, Peacock A, Bargar J, Long PE, Banfield JF. 2012. High-
530
density PhyloChip profiling of stimulated aquifer microbial communities reveals a
531
complex response to acetate amendment. FEMS Microbiol. Ecol. 81:188-204.
532
22. DeSantis TZ, Stone CE, Murray SR, Moberg JP, Andersen GL. 2005. Rapid
533
quantification and taxonomic classification of environmental DNA from both prokaryotic
534
and eukaryotic origins using a microarray. FEMS Microbiol. Lett. 245:271-8.
535
23. Faith DP. Conservation evaluation and phylogenetic diversity. 1992. Biol. Conserv.
536
61:1–10.
537
24. Oksanen J, Kindt R, Legendre P, O'Hara B, Simpson GL, Solymos P, Henry M,
538
Stevens H, Wagner H. 2008.Vegan: Community ecology package. R package version
539
1.16-1.
540
25. Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD,
541
Blomberg SP, Webb CO. 2010. Picante: R tools for integrating phylogenies and ecology.
542
Bioinformatics. 26:1463-1464.
543
26. Gotelli N, Ellison A. 2004. A primer of ecological statistics. Sinauer Associates, Inc.
544
Sunderland, MA.
545
27. Lozupone C, Knight R. 2005. UniFrac: a new phylogenetic method for comparing
546
microbial communities. Appl. Environ. Microbiol. 71: 8228–8235.
24
547
28. Anderson, M.J. 2001. A new method for non-parametric multivariate analysis of
548
variance. Austral Ecology. 26:32–46.
549
29. Smyth, G. K. 2004. Linear models and empirical Bayes methods for assessing
550
differential expression in microarray experiments. Statistical Applications in Genetics
551
and Molecular Biology. 3(1):Article3.
552
30. Storey JD, Tibshirani R. 2003. Statistical significance for genomewide studies. Proc.
553
Natl. Acad. Sci. U S A. 100:9440-5.
554
31. Sunagawa S, DeSantis TZ, Piceno YM, Brodie EL, DeSalvo MK, Voolstra CR, Weil
555
E, Andersen GL, Medina M. 2009. Bacterial diversity and White Plague Disease-
556
associated community changes in the Caribbean coral Montastraea faveolata. ISME J.
557
3:512-21..
558
32. Ivanov II, Atarashi K, Manel N, Brodie EL, Shima T, Karaoz U, Wei D, Goldfarb KC,
559
Santee CA, Lynch SV, Tanoue T, Imaoka A, Itoh K, Takeda K, Umesaki Y, Honda K,
560
Littman DR. 2009. Induction of intestinal Th17 cells by segmented filamentous bacteria.
561
Cell. 139:485-98.
562
33. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA,
563
Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C.
564
2013. Predictive functional profiling of microbial communities using 16S rRNA marker
565
gene sequences. Nat. Biotechnol. 31:814-21.
566
34. Pauwels RA, Buist SA, Calverley PM, Jenkins CR, Hurd SS. 2001. Global Strategy
567
for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary
568
Disease. Am. J. Respir. Crit. Care Med. 163: 1256-1276.
25
569
35. Ventura M, Canchaya C, Tauch A, Chandra G, Fitzgerald GF, Chater KF, van
570
Sinderen D. 2007. Genomics of Actinobacteria: tracing the evolutionary history of an
571
ancient phylum. Microbiol. Mol. Biol. Rev. 71:495-548.
572
36. Atarashi K, Tanoue T, Shima T, Imaoka A, Kuwahara T, Momose Y, Cheng G,
573
Yamasaki S, Saito T, Ohba Y, Taniguchi T, Takeda K, Hori S,Ivanov II, Umesaki Y, Itoh
574
K, Honda K. 2011. Induction of colonic regulatory T cells by indigenous Clostridium
575
species. Science. 331(6015):337-41.
576
37. Stecher B, Chaffron S, Kappeli R, Hapfelmeier S, Freedrich S, Weber TC, Kirundi J,
577
Suar M, McCoy KD, von Mering C, Macpherson AJ, Hardt WD. 2010. Like will to like:
578
Abundances of closely related species can predict susceptibility to intestinal colonization
579
by pathogenic and commensal bacteria. PLoS Pathog. 6:e1000711.
580
38. Cushnie TP, Lamb AJ. 2011. Recent advances in understanding the antibacterial
581
properties of flavonoids. Int. J. Antimicrob. Agents. 38:99-107.
582
39. Vulić JJ, Cebović TN, Canadanović VM, Cetković GS, Djilas SM, Canadanović-
583
Brunet JM, Velićanski AS, Cvetković DD, Tumbas VT. 2013. Antiradical, antimicrobial
584
and cytotoxic activities of commercial beetroot pomace. Food Funct. 4:713-721.
585
40. Zoraghi R, Worrall L, See RH, Strangman W, Popplewell WL, Gong H, Samaai T,
586
Swayze RD, Kaur S, Vuckovic M, Finlay BB, Brunham RC, McMaster WR, Davies-
587
Coleman MT, Strynadka NC, Andersen RJ, Reiner NE. 2011. Methicillin-resistant
588
Staphylococcus aureus (MRSA) pyruvate kinase as a target for bis-indole alkaloids with
589
antibacterial activities. J. Biol. Chem. 286:44716-25.
590
41. Miravitlles M, Soler-Cataluña JJ, Calle M, Soriano JB. 2013. Treatment of COPD by
591
clinical phenotypes: putting old evidence into clinical practice. Eur Respir J. 41:1252-6.
26
592
42. Armbruster CE, Hong W, Pang B, Weimer KE, Juneau RA, Turner J, Swords WE.
593
2010. Indirect pathogenicity of Haemophilus influenzae and Moraxella catarrhalis in
594
polymicrobial otitis media occurs via interspecies quorum signaling. MBio. 1:e00102-10..
595
43. Sibley CD, Duan K, Fischer C, Parkins MD, Storey DG, Rabin HR, Surette MG.
596
2008. Discerning the complexity of community interactions using a Drosophila model of
597
polymicrobial infections. PLoS Pathog. 4:e1000184..
598
44. Molyneaux PL, Mallia P, Cox MJ, Footitt J, Willis-Owen SA, Homola D, Trujillo-
599
Torralbo MB, Elkin S, Kon OM, Cookson WO, Moffatt MF, Johnston SL. 2013.
600
Outgrowth of the bacterial airway microbiome after rhinovirus exacerbation of chronic
601
obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 188:1224-31.
602
45. Zhao J, Schloss PD, Kalikin LM, Carmody LA, Foster BK, Petrosino JF, Cavalcoli
603
JD, Vandevanter DR, Murray S, Li JZ, Young VB, Lipuma JJ. 2012. Decade-long
604
bacterial community dynamics in cystic fibrosis airways. Proc Natl Acad Sci U S A.
605
109:5809-5814.
606
46. Tunney, MM, Einarsson, GG, Wei, L, Drain, M, Klem, ER, Cardwell, C, Ennis, M,
607
Boucher, RC, Wolfgang, MC & Elborn, JS. 2013. Lung microbiota and bacterial
608
abundance in patients with bronchiectasis when clinically stable and during exacerbation.
609
Am. J. Respir. Crit. Care Med. 187:1118-1126.
610
47. Hajishengallis G, Liang S, Payne MA, Hashim A, Jotwani R, Eskan MA, McIntosh
611
ML, Alsam A, Kirkwood KL, Lambris JD, Darveau RP, Curtis MA. 2011. Low-
612
abundance biofilm species orchestrates inflammatory periodontal disease through the
613
commensal microbiota and complement. Cell Host Microbe. 10:497-506.
614
27
Table 1. Clinical characteristics of subjects and exacerbations. All subjects were Caucasian male veterans. Age, lung function and smoking status information were collected during clinically stable state at parent study enrollment. Inhaled corticosteroid use status was current for analyzed time points. Subject ID
Age
FEV1 (%)
Smoking status (pack-yrs)
Inhaled steroid use
Clinical score at Exac
Sputum purulence at Exac
Sputum culture result from Exac sample
Pre-existing or new bacterial strain at Exac
Antibiotic for Exac
3
67
7
65
Systemic steroids for Exac
91
Ex (10)
Yes
20
Purulent
P. aeruginosa
New
Azithromycin
No
63
Ex (105)
No
18
Purulent
H. influenzae
New
Ofloxacin
No
19
51
63
Current (48)
Yes
19
Purulent
M. catarrhalis
New
Azithromycin
No
40
63
56
Current (112)
Yes
14
Mucoid
None
NA
None
Yes
46
71
48
Ex (125)
No
13
Mucopurulent
M. catarrhalis Klebsiella spp.
Pre-existing NA
None
Yes
48
64
46
Current (45)
Yes
15
Mucoid
None
NA
None
Yes
49
66
61
Ex (100)
Yes
24
Purulent
P. fluorescens
NA
Azithromycin
Yes
56
61
42
Current (135)
Yes
22
Purulent
H. influenzae
New
Ciprofloxacin
Yes
Mucoid
H. influenzae M. catarrhalis S. pneumoniae
Pre-existing Pre-existing Pre-existing
63 67
80 66
51 48
Ex (87) Current (50)
Yes No
13 23
None
Yes
Trimethoprim/ sulfamethoxazole
No
Purulent
S. pneumoniae
New Pre-existing New
Ofloxacin
Yes
New
Gatifloxacin
Yes
74
72
55
Ex (96)
Yes
21
Mucopurulent
H. influenzae, P. aeruginosa
111
68
38
Current (108)
Yes
23
Purulent
M. catarrhalis
Table 2. List of bacterial taxa that demonstrated the largest changes in relative abundance (> 2-fold, p-value < 0.05, q-value < 0.30), at the onset of exacerbation compared to the most recent pre-exacerbation time point (Pre2). The results are based on analysis of all subjects in the study (n = 12 subjects). Taxon (Class/Family/Genus)
prokMSA_ID for taxon representative species
γ-Proteobacteria/Moraxellaceae/Acinetobacter γ-Proteobacteria/Moraxellaceae/Perlucidibaca γ-Proteobacteria/Moraxellaceae/Acinetobacter γ-Proteobacteria/Moraxellaceae/Acinetobacter β-Proteobacteria/Anaplasmataceae/Ehrlichia γ-Proteobacteria/Pasteurellaceae/Actinobacillus α-Proteobacteria/Sphinogomonadaceae/Sphingomonas β-Proteobacteria/Comamonadaceae/Comamonas γ-Proteobacteria/Enterobacteriaceae γ-Proteobacteria/Pseudomonadaceeae/Pseudomonas Bacilli/Bacillaceae/unclassified ε-Proteobacteria/Campylobacteraceae/Campylobacter Bacteroidia/Rikenellaceae/unclassified Clostridia/Clostridiales Family XIII/Mogibacterium Actinobacteria/ Microbacteriaceae/Agromyces Clostridia/Lachnospiraceae/unclassified Actinobacteria/ACK-M1/unclassified Actinobacteria/Micrococcaceae/Rothia Actinobacteria/Nocardiaceae/Rhodococcus
60270 105696 106871 101304 6030 52835 89065 71872 10365 8434 89195 108770 101395 14167 12174 72977 96076 58305 85556
Fold-change (increase or decrease at exacerbation) +2.9 +2.7 +2.4 +2.4 +2.2 +2.1 +2.1 +2.0 +2.0 +2.0 -1.9 -1.9 -1.9 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8
p-value
0.01 0.02 0.04, 0.006 0.02 0.04 0.01 0.03 0.04 0.01 0.04 0.03 0.01 0.002 0.02 0.005 0.01 0.03 0.04
1
Figure legends.
2 3
Figure 1. Time points of samples collected before, at the onset of, and after an acute COPD
4
exacerbation. Line coloring indicates the type of exacerbation treatment the subject received.
5 6
Figure 2. A. Non-metric multidimensional scaling (NMDS) ordination demonstrates that
7
samples from all subjects in the study segregate into distinct clusters (indicated by ellipses) based
8
on the type of treatment prescribed for exacerbation [antibiotics (Abx) only, oral steroids only, or
9
both; n = 4 subjects per treatment group; Bray-Curtis distance-based PERMANOVA, p < 0.05].
10
Ellipses represent 95% confidence interval for the standard error of the distances of samples in
11
each treatment group. B. NMDS plot of only post-exacerbation treatment samples from all
12
subjects demonstrates that the relationship between sample community composition and
13
treatment type remains strong, though not statistically significant (p = 0.08), when the analysis is
14
limited to only these samples. C. NMDS plot of pre-exacerbation samples alone from all subjects
15
shows that chronic use of inhaled corticosteroids may be associated with differences in bacterial
16
community composition (Bray-Curtis distance-based PERMANOVA, p = 0.08).
17 18
Figure 3. A. Volcano plots indicating taxa that are significantly increased (upper right quadrant)
19
or decreased (upper left quadrant) in pair-wise comparisons as shown, using moderated t-test
20
models (R package limma). Results shown are from analyses of all subjects. Dashed lines
21
indicate significant FDR-adjusted p-values and changes in relative abundance of at least 2-fold,
22
or log2 =1. Taxa exhibiting significant changes are colored by phylum-level classification as
23
shown. Note that in addition to highlighted taxa, many other microbiota members exhibit
24
smaller scale changes in abundance, which cumulatively may contribute importantly to
25
microbiome community function, B. Changes in taxa relative abundance from Exac to Post1 and
26
Post1 to Post2, segregated by type of exacerbation treatment (antibiotics only, systemic
27
corticosteroids only, or both; n = 4 subjects in each treatment group).
28 29
Figure 4. Correlations between the relative abundance of H. influenzae (a member of the
30
Pasteurellaceae/GammaProteobacteria) and that of all other identified taxa. The analysis was
31
performed using data from all 60 samples in this study. Significant positive and negative
32
correlations are shown (Pearson R > 0.5, Benjamini-Hochberg adjusted p < 0.05). Positive
33
correlations (red) occur predominantly with members of Pasteurellaceae or closely related
34
bacterial families and classes of Proteobacteria. Negative correlations (blue) occur with bacterial
35
families and classes that phylogenetically are more distant to H. influenzae/Pasteurellaeae. Tree
36
branches are color-coded by bacterial class (legend).
37 38
Figure 5. KEGG pathways associated with predicted gene functions (KOs) encoded by
39
metagenomes of bacterial taxa that either were increased (red) or decreased (blue) ≥ 2-fold in
40
relative abundance at the time of and after exacerbations. Among the microbiome-related
41
functions identified were those involved in promotion of inflammation by pathogens (*) and
42
production of anti-microbial and anti-inflammatory compounds (**), functions that were
43
differentially represented at exacerbation compared to post-exacerbation.
Exacerbation onset subj 111 subj 74 subj 56
Exacerbation treatment group
subj 49 subj 63
Antibiotics only Corticosteroids only Both antibiotics & corticosteroids
subj 48 subj 46 subj 40 subj 67 subj 19 subj 7 subj 3
-150
-100
-50
0
50
Days relative to exacerbation onset
Figure 1.
100
150
0.05
111Ex
Both
63Ex 56Ex
111Pre1 111Post1
46Post2 63Post2 63Pre1 63Post1
0.00
NMDS2
63Pre2
111Pre2
40Pre1
-0.05
Steroid only
46Pre2 7Post1 111Post2 48Pre1 67Post2 56Post1 7Pre1 49Post1 7Ex 74Pre1 48Ex 67Ex 74Post1 67Pre1 40Pre2 74Ex 48Post2 67Post1 74Post2 56Pre2 1 7Post2 48Post 46Post1 56Pre1 48Pre2 49Pre2 7Pre2 56Post2 74Pre2 46Ex 67Pre2 40Post2 46Pre1 49Pre1 49Post2 40Post1 40Ex 3Pre1 3Ex 3Post1 3Post2 49Ex19Pre2 19Post2 19Pre1 19Post1 3Pre2 Abx only 19Ex
-0.05
0.00 NMDS1
Figure 2A.
0.05
0.06 0.04
Steroid only
0.00
NMDS2
0.02
40Post1 40Post2 56Post2 48Post2 48Post1 49Post2 63Post1
49Post1 3Post1
-0.02
63Post2
7Post2 74Post2 19Post1 7Post1 46Post1 74Post1 67Post2
56Post1 67Post1
Both
111Post2 19Post2
111Post1
3Post2
Abx only
-0.06
-0.04
46Post2
-0.05
0.00
0.05 NMDS1
Figure 2B.
Figure 2C.
Clostridiaceae/Lachnospiraceae (Clostridia)
Classes (tree branches): Gammaproteobacteria Verrucomicrobia e Chlamydiae Aquificae gut clone group Catabacter Bacilli Fusobacteria Mollicutes Symbiobacteria Deltaproteobacteria Betaproteobacteria Alphaproteobacteria Acidobacteria NC10−2 Spirochaetes Sphingobacteria Bacteroidetes Flavobacteria Epsilonproteobacteria mgA−1 TM7−3 Cyanobacteria OP9 Actinobacteria
Bacillaceae
Staphylococcus spp.
Lactobacillus spp.
Streptococcaceae (Lactobacillales)
Enterobacteriaceae Helicobacter Campylobacter (Epsilonproteobacteria)
0.01
Pasteurellaceae (includes genus Haemophilus)
Abundance Higher
Lower Positive correlation Negative correlation
Deltaproteobacteria
Pseudomonadaceae Moraxellaceae Oxalobacteraceae (Betaproteobacteria)
Figure 4.
KEGG pathways related to biologic functions represented by predicted KOs Apoptosis Basal transcription factors Betalain biosynthesis** Biosynthesis of 12-,14-, and 16-membered macrolides** Circadian rhythm-plant Cytochrome P450 Epithelial cell signaling in H. pylori infection* Ether lipid metabolism Flavone and flavonol biosynthesis** Hypertrophic cardiomyopathy Indole alkaloid biosynthesis** Influenza A* p53 signaling pathway Renin-angiotensin system RIG-I-like receptor signaling pathway Staphylococcus aureus infection* Steroid biosynthesis Toxoplasmosis Transcription related proteins Viral myocarditis
Figure 5.
Pre2 to Exac
Exac to Post1
