1
2
Supplementary Methods:
Sample collection: The treating physicians determined the severity of the disease state
3
and the selection and duration of clinical therapies. The study subjects were diagnosed with CF
4
based on a clinical scenario consistent with the disease and confirmed by the identification of
5
two disease associated CFTR mutations and/or an abnormally elevated sweat chloride test result.
6
Eligibility for non-CF inclusion was solely based upon this criterion; these volunteers were non-
7
smokers without known pulmonary disease. A sterile saline solution (60 mL) was used as a
8
mouth rinse prior to saline inhalation to minimize saliva contamination. The sputum samples
9
were collected after the inhalation of four mL of 7% hypertonic saline via a Pari LC nebulizer.
10
The samples were processed immediately by syringe homogenization. Sample volumes were
11
normalized to 12 mL by the addition of phosphate buffered saline. An additional 27 sputum
12
samples were collected for targeted detection of P. aeruginosa quinolones. These samples were
13
collected and extracted with identical procedures except they were immediately stored in a liquid
14
nitrogen dewar prior to extraction.
15
Bacterial culturing and metabolite extraction. P. aeruginosa strains PAnmFLR01,
16
PAnmFLR02, PAmFLR07, PAmFLR08 and PAmFLR09, methicillin sensitive Staphylococcus
17
aureus (SaFLR01), methicillin resistant S. aureus (MRSAFLR01), Stenotrophomonas
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maltophilia (SmFLR01), Escherichia coli (EcFLR01, isolated from patient CF6) and
19
Streptococcus salivarius (SsFLR01, isolated from patient CF1) were isolated directly from CF
20
sputum samples at the UCSD Center for Advanced Laboratory Medicine (CALM). These strains
21
were not isolated from patients in this study unless stated above. P. aeruginosa strain MPAO1
22
was obtained from the University of Washington (1). All strains were kept in glycerol at -80°C
23
until grown and maintained on Todd Hewitt Agar (THA) at 37°C. The P. aeruginosa strains
24
were inoculated on 10 mL of ISP2 agar (1% malt extract, 0.4% yeast extract, 0.4% glucose and
25
2% agar) in 100 mm by 25 mm petri dishes directly from a THA plate. After a 48-hour
26
incubation at 30oC (PAO1) or 37oC (CF isolates), the agar of the entire plate including the
27
colony biofilm was sliced into small pieces and extracted with 10 mL of ethyl acetate followed
28
by 10 mL methanol. The solvent was separated from agar pieces by filtration using a Whatman©
29
cellulose grade 1 filter and concentrated in vacuo. The combined extracts were resuspended in 1
30
mL of methanol, centrifuged and diluted 10-fold prior to LC-MS/MS analysis. For assessment of
31
the quinolones produced in a CF-like environment compared to aerobic media, strains
1
1
PAmFLR07, PAmFLR08 and PAmFLR09 were grown overnight in THA, pelleted, and
2
resuspended in phosphate buffered saline to an optical density of 0.2. Cultures were spot plated
3
on to a single ISP2 plate and grown overnight. Extracts were taken by cutting out the agar around
4
the colony and extracting in 200 μl of ethyl acetate and methanol as described above. The same
5
cultures were also grown in artificial sputum medium in thin glass capillary tubes simultaneously
6
overnight according to the method developed in (2), which is meant to mimic the conditions of a
7
CF bronchiole. The culture media was then removed with a needle and syringe and extracted in
8
200 μl of ethyl acetate and methanol.
9
Microbiome sequence profiling: The paired end reads were preprocessed with Prinseq (3)
10
and analyzed using a new mothur standard operating procedure (SOP) designed for MiSeq data
11
(4); the mothur MiSeq SOP was accessed in December 2013. The parameters of the mothur
12
processing are available in the online supplementary information. The Silva database (5) was
13
used with Ribosomal Database Project taxonomy (6) to identify the taxonomy of OTUs at a
14
cluster cutoff of 97% sequence identity. The OUT tables were rarefied to a minimum of 4000
15
sequence reads per sample using the rrarefy command of the vegan package in the R statistical
16
software. For display in Figure 2a 19 of these most abundant OTUs are shown in the profile, for
17
clarity the remaining OTUs were clustered into ‘other’ (Figure 2b). The microbiome profiles of
18
the patients in this study have been published previously in a culture based study with a different
19
objective (7).
20
Sputum metabolite extractions. Samples were incubated at room temperature for 1 hour
21
in ethyl acetate, briefly centrifuged, and then the ethyl acetate supernatant was removed and
22
evaporated in a centrifugal evaporator. The sputum sample remaining was then incubated in
23
methanol for 1 hour, centrifuged as before. The methanol extract was added to the ethyl acetate
24
extract and evaporated in a centrifugal evaporator. All sputum extracts were solubilized in 50 μl
25
of methanol and analyzed by LC-MS/MS; the UPLC samples were diluted 1 in 10 prior to LC-
26
MS/MS analysis.
27
LC-MS/MS. A sphingomyelin standard was purchased from Sigma-Aldrich®, diluted to 3
28
μM in ethanol and 20 μl was subjected to LC-MS/MS analysis according to the same parameters
29
as the UPLC sputum runs. This compound is thus classified as a level 1 annotation according to
30
the MSI guidelines, related sphingomyelins are considered putatively annotated level 2 (8).
31
Xenobiotics were identified by parent mass and MS/MS spectral matching to compounds
2
1
available in GNPS that were run on the same MS instrument and are thus annotated at MSI level
2
1 as known compounds.
3
Statistical Analysis: The UPLC-MS/MS raw line spectra data from the Bruker Maxis
4
instrument were imported into the Bruker Compass DataAnalysis v4.1 software for calling
5
individual molecular features using the ‘Find Molecular Features’ algorithm. Parameters of this
6
algorithm were a signal to noise ratio of 5, correlation coefficient threshold of 0.7, a minimum
7
peak length of 8 spectra and a smoothing width of 2. All files were then imported into Bruker
8
Profile analysis software v2.1 and the bucket table calculated to produce and abundance matrix
9
of molecules in the metabolomes. A bucket window of 0.5 min retention time and m/z 0.3 was
10
used to identify individual molecular features in the data sets. No filter was applied for proton,
11
sodium, potassium or ammonium as the charging ion in the molecular species that are described.
12
This call of individual molecular features was exported as a matrix showing each sample and the
13
abundance of each molecule detected by the mass spectrometer.
14
Molecular Networking. Molecular networks were generated using in house
15
bioinformatics tools. A preview of the tools can be found at gnps.ucsd.edu. The sputum networks
16
used the following settings: parent mass tolerance: 0.5 Da; ion tolerance: 0.5 Da; cosine cutoff:
17
0.65; minimum matched peaks: 4; minimum cluster size: 3. For microbial metabolomics
18
analysis, the following settings were utilized: parent mass tolerance: 0.5 Da; ion tolerance: 0.5
19
Da; cosine cutoff: 0.65; minimum matched peaks: 4; minimum cluster size: 3. This network was
20
searched against the spectral library for annotation using the following settings: library search
21
minimum matched peaks: 4; score threshold: 0.7. Both group mapping and attribute mapping
22
were used to enhance visualization of these molecular networks within Cytoscape (v.2.8.1). All
23
putative identifications from the molecular network matched spectra from the pure cultures
24
(Figure S1) and published data (9, 10). Edge widths indicate the cosine similarity where thicker
25
lines indicate higher cosine similarity. Node attributes (color, shape, size) were used to assist in
26
network analysis. Verification of library correlation and putative annotations from MS1 data was
27
performed by comparing spectra and LC retention times. All data (including molecular
28
networks) are available through massive.ucsd.edu.
29
30
References:
3
1
2
3
4
1.
Jacobs MA, Alwood A, Thaipisuttikul I, Spencer D, Haugen E, Ernst S, Will O, Kaul R,
Raymond C, Levy R, Chun-Rong L, Guenthner D, Bovee D O, MV MC. Comprehensive
transposon mutant library of Pseudomonas aeruginosa. Proc Natl Acad Sci U S A
2003;100:14339–14344.
5
6
7
2.
Quinn RA, Lim YW, Maughan H, Conrad D, Rohwer F, Whiteson KL. Biogeochemical
forces shape the composition and physiology of polymicrobial communities in the cystic
fibrosis lung. MBio 2014;5:e00956–13.
8
9
3.
Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets.
Bioinformatics 2011;27:863–4.
10
11
12
4.
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dualindex sequencing strategy and curation pipeline for analyzing amplicon sequence data on
the MiSeq Illumina sequencing platform. Appl Environ Microbiol 2013;79:5112–20.
13
14
15
5.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. The
SILVA ribosomal RNA gene database project: improved data processing and web-based
tools. Nucleic Acids Res 2013;41:D590–6.
16
17
18
6.
Cole JR, Chai B, Farris RJ, Wang Q, Kulam SA, McGarrell DM, Garrity GM, Tiedje JM.
The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA
analysis. Nucleic Acids Res 2005;33:D294–6.
19
20
21
22
7.
Quinn RA, Whiteson K, Lim Y, Salamon P, Bailey B, Mienardi S, Sanchez SE, Blake D,
Conrad D, Rohwer F. A Winogradsky-based culture system shows an association between
microbial fermentation and cystic fibrosis exacerbation. ISME J 2014;1–
15.doi:10.1038/ismej.2014.234.
23
24
25
26
27
8.
Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, Fan TW-M, Fiehn
O, Goodacre R, Griffin JL, Hankemeier T, Hardy N, Harnly J, Higashi R, Kopka J, Lane
AN, Lindon JC, Marriott P, Nicholls AW, Reily MD, Thaden JJ, Viant MR. Proposed
minimum reporting standards for chemical analysis Chemical Analysis Working Group
(CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 2007;3:211–221.
28
29
30
9.
Moree WJ, Phelan V V, Wu C-H, Bandeira N, Cornett DS, Duggan BM, Dorrestein PC.
Interkingdom metabolic transformations captured by microbial imaging mass
spectrometry. PNAS 2012;109:13811–6.
31
32
33
34
10.
Watrous J, Roach P, Alexandrov T, Heath BS, Yang JY, Kersten RD, van der Voort M,
Pogliano K, Gross H, Raaijmakers JM, Moore BS, Laskin J, Bandeira N, Dorrestein PC.
Mass spectral molecular networking of living microbial colonies. Proc Natl Acad Sci U S
A 2012;109:1743–52.
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