Bronchial Alveolar Lavage (BAL) Fluid Cell Measurements

The bacterial cell load was assessed through direct culture in separate experiments. As seen in table 1, the number of colony forming units was measured to be one to four thousand for both the wild-type (Fn) and attenuated (Fn-ATT) F. novicida with significantly larger dose measured for the P. aeruginosa (Pa) exposure.

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Table 1. Measurement of bacterial deposition in colony forming units (CFU) per lung for virulent (Fn) and attenuated (Fn-ATT) F. novicida and P. pseudomonas (Pa) with standard error of the mean (SEM) indicated for each measurement.

https://doi.org/10.1371/journal.pone.0007047.t001

The response to bacterial exposure was measured by the number of immune cells found in the BAL fluid for four mice in each group. Both mononuclear (MN) and poly morphonuclear (PMN) cells were counted in the BAL at 4 h, 24 h, and 48 h for Fn and Fn-att and at 4 h and 24 h for Pa. (Figure 1). The total cell counts revealed differences between each type of exposure. At 4 hours, only the Pa exposure showed a significant increase in cell count while at 24 hours both Pa and Fn showed a significant increase in immune cell recruitment. Much of this data can be explained by differences in PMN count alone (Figure 1B). No PMN are seen in the 0 hour control and only the Pa sample contained PMN at 4 hrs. The Pa and Fn samples showed PMN at 24 hrs similar to the total cell count data.

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Figure 1. Total number of mononuclear (MN) and poly morphonuclear (PMN) cells counted in the BAL fluid at 4 h, 24 h, and 48 h for Fn and Fn-ATT and at 4 h and 24 h for Pa (A).

Error bars indicate the standard error of the mean (SEM) for each. Percent PMN cells in BAL fluid (B).

https://doi.org/10.1371/journal.pone.0007047.g001

MALDI-MS Mass Measurement

Mass spectral comparisons rely on reproducible measurements and assessment of the variability of those measurements. Mass calibration for the plate was done using plate locations to calibrate an area of the plate with each sample being within three plate locations of the calibrant. Sinapinic acid was also chosen as the MALDI matrix because of the increased instrument performance with greater peak resolution over another common matrix, α-hydroxy cinnamic acid (data not shown). Typical peak resolutions were estimated using external calibrant peaks appearing in the same m/z range as many of the markers observed, such as ubiquitin ([M+H] ⁺ theo. m/z 8565.89) and cytochrome c ([M+2H] ⁺ m/z 6181.05). Mass resolution of 500 +/− 50 was observed as well as an average mass measurement accuracy of 25 ppm and precision of 130 ppm. The peak statistics for sample markers indicated slightly lower performance for a commonly observed marker in BALF, m/z 3349.4. The standard deviation of the mass measurement was 0.92 m/z (precision of 274 ppm) was determined over fifteen replicate mass measurements with an average peak resolution of 390 +/− 30.

To increase consistency of data acquisition, ground stainless steel sample plates were used to provide more reproducible crystal formation with the chosen matrix. However, despite the mass resolution advantages, sinapinic acid tends to form less homogeneous crystals than α-hydroxy cinnamic acid. Therefore it was determined that 500 laser shots needed to be acquired in 50 shot intervals to get a representative mass spectrum for each spot.

Feature Extraction

Following acquisition, the data were analyzed based on the set of 219 binary peaks. Peaks were picked using an approach previously applied to MALDI-MS spectra of bacterial samples [40]. For this analysis, all control time points were combined as a single group totaling 12 mice. Of the 219 peaks, only ∼5% were deemed significant by Kruskal-Wallis with a Bonferroni correction for multiply hypothesis tests, Table 2. Examples of MALDI-MS data used in the study are provided in Fig. 2 showing comparison of Fn-4 hr sample to a Pa-4 hrs sample. Several of the masses determined to be significant in table 2 are present in the Pa-4 hr, including m/z 5555.1, 1674.5 and 1792.8.

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Figure 2. Example mass spectra comparing of a portion of the mass range for a Fn-4 hrs sample (A) and a Pa-4 hrs sample (B).

https://doi.org/10.1371/journal.pone.0007047.g002

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Table 2. The significant peaks with a p-value of less than 0.05 based on a Kruskal-Wallis test of the data.

https://doi.org/10.1371/journal.pone.0007047.t002

Discriminatory Power of Biosignatures

As seen in Figure 3, some separation can be viewed for the different treatment classes from a standard Principal Component analysis (PCA). Missing data were imputed using a simple probability based method. If a peak was observed over 50% of the time the value was imputed as the average intensity, otherwise it was imputed as ½ of the minimum observed intensity. However, only ∼17.4% of the variability is explained in these first two components. Clearly the various treatments were distinct from the control samples of all time points. The Fn ATT samples also seemed to produce the most distinct response using these two principle components. The PCA gives some indication of distinctness of groups, however since imputation is not standard for MALDI, in-complete data methods, such as fingerprinting, are a more appropriate analytical method for MALDI.

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Figure 3. Distribution of replicate samples for seven classes of treatment: Control, PA-4 hrs, PA-24 hrs, Fn-4 hrs, Fn-24 hrs, Fn ATT-4 hrs, Fn ATT-24 hrs using two principle components.

https://doi.org/10.1371/journal.pone.0007047.g003

Since our sample size was relatively small, most of the seven groups of interest, have only four samples, leave-one-out cross-validation (LOOCV) was used to classify each sample in a manner that was independent from the data used to build the reference fingerprints. Degree of association was used to build a predictive model on 35 of the 36 samples and a fingerprint was generated for the left out sample and probabilistically assigned to a class (Eq. 2). Table 3 summarizes the results with respect to each class in which 27 of the 36 samples are appropriately classified.

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Table 3. The class predictions of the full 7 class Degree of Association model (Global Accuracy ∼75.0%).

https://doi.org/10.1371/journal.pone.0007047.t003

The statistical approach to classifying marker sets demonstrated an ability to discriminate classes of exposure 75% of the time. Not all treatment groups could be equally well defined using this approach. In the case of Pa-4 was accurately predicted for all four animals and Pa-24 three of the four times. The Fn-ATT-4, Fn-ATT-24 and Fn-4 were also predicted with relatively high confidence with all four mice for Fn-ATT-4 and three of four mice for the other two classes. The class that was most difficult to predict was Fn-24, where only one in four mice was correctly predicted and the other three mice predicted to be in three different treatment classes.

Materials

The acetic acid, triflouroacetic acid, acetonitrile, methanol, PMSF and ammonium hydroxide, ammonium acetate, ammonium bicarbonate was obtained from Sigma (St Louis, MO) at the highest purity available. The phosphate buffered saline (PBS) was obtained from Cell Gro (Voigt global inc, Lawrence, KS). Microcon molecular weight cutoff (MWCO) filters were purchased from Millipore (Burlington, MA). The 1 ml (100 mg bed volume) reverse phase C-18 T (P/N 8B-S004-EAK) and the 1 ml (30 mg bed volume P/N 8B-S029-TAK) strata X-C strong cation exchange solid phase extraction (SPE) cartridges were obtained from Phenomenex (Torrance, CA). The hydrophobic interaction (C-18) magnetic beads (MB-HIC) as well as protein and peptide mass calibration standards (P/N 219042) were purchased from Bruker Daltonics (Billerica MA).

Bacterial Cultures

Francisella novicida U112 (Fn) and the F. novicida mglA mutant (Fn ATT) were provided by Dr. Francis Nano (University of Victoria, Canada). Pseudomonas aeruginosa PAK (Pa) was obtained from Dr. Steve Lory (Harvard University). Glycerol stocks were inoculated 1∶1000 into nutrient broth (TSB-0.1% L-cysteine for F. novicida strains and LB for P. aeruginosa) and incubated for 16 h at 37°C in a rotating platform incubator at 200 rpm. Bacteria were pelleted by centrifugation at 4°C, washed twice with PBS and resuspended in 5 ml cold PBS. Bacterial concentrations were estimated by optical density, using an OD450 of 0.2 as indicating a concentration of 2×10e9 CFU/ml (Fn and Fn ATT) or 2×10e8 CFU/ml (Pa). All bacteria were mixed thoroughly and diluted to 5×10^∧9 CFU/ml in PBS in a total of 11 ml, of which 10 ml were used for each aerosolization.

Aerosol exposure of mice

All animal experiments were approved by the Institutional Animal Care and Use Committee of the University of Washington (Seattle, Washington). Specific pathogen-free male and female C57BL/6 mice were obtained from Jackson Labs (Bar Harbor, ME) and were 8 weeks of age at the time of study. Mice were housed in filtered cages in a laminar flow rack (BioZone (Fort Mill, SC), and were permitted ad lib access to sterile food and water. For inhalation challenges, mice were placed in individual restraining tubes and exposed to aerosolized bacteria or PBS using a snout-only inhalation system (In-Tox Products, Moriarty, NM). Aerosols were generated using Mini-Heart Hi-flo nebulizers (Westmed, Tucson, AZ) driven at 44 psi with flow maintained at 2.5–2.6 L/min. Total airflow through the chamber was maintained at 17–26 L/min by negative pressure. Aerosol exposures were conducted for ten min, followed by 5 min purging with air. After each exposure the chamber was sterilized with Process NPD. Aerosol exposure experiments were conducted over 2 days using the following schedule:

Day 1:

1. PBS (8 mice)
2. Sterilization and clean
3. F. novicida (Fn - 6 mice)
4. Sterilization and clean
5. F. novicida mglA mutant (Fn-ATT - 6 mice)
6. Sterilization and clean

Day 2:

1. P. auriginosa (PAK) (Pa- 8 mice)
2. Sterilization and clean
3. F. novicida (Fn - 6 mice)
4. Sterilization and clean
5. F. novicida mglA mutant (Fn-ATT - 6 mice)
6. Sterilization and clean
7. PBS (8 mice)

Actual deposition of bacteria in the lungs was determined by quantitative cultures of lung tissues harvested from three sentinel mice immediately after each aerosol exposure, as described [42], [43]. The measured number of bacteria deposited lung with this treatment is provided in table 1.

Collection of bronchial alveolar lavage fluid

Mice samples were harvested at T = 0 (PBS exposed only), 4H, 24H, and 48H (for PBS only); at 4H and 24H for F. novicida, F. novicida mglA mutant and P. auriginosa PAK post exposure. The samples were collected after the mice were anesthetized with pentobarbital and exsanguinated by heart puncture. The trachea and lungs were exposed and trachea cannulated with a catheter. Both lungs were lavaged by instilling 0.8 ml×4 of warm lavage fluid (0.9% saline with 0.6 mM EDTA). The lavage fluid was spun at 1200 rpm at room temperature and aliquoted with a final concentration of 1 mM PMSF.

Culture supernatant preparation and acid extraction

A 0.2 mL portion of the bronchial alveolar lavage fluid (BALF) was applied to a Microcon 30 MWCO micro centrifuge spin filters. After centrifuging at 10,000 RCF for 30 minutes, the filtrate was collected. Acetic acid was added to a final concentration of 2% and the samples were allowed to incubate for 5 minutes at room temperature. The samples were then centrifuged again at 10,000×g for five minutes to remove any precipitated material.

The supernatants were applied to C-18 SPE cartridges placed on a vacuum apparatus that were previously conditioned according to the manufacturer recommendations. After washing with two 1 ml volumes of 0.1% TFA, the samples were eluted using 1 mL of 90% acetonitrile and 0.1% TFA into 1.5 mL eppendorf tubes. The eluted material was dried under nitrogen gas. Each sample was resuspended in 25 µL of 10% acetonitrile 0.1% TFA.

Two stage SPE sample fractionation and cleanup

The mouse BALF samples were prepared for mass spectrometry in twelve sample batches using a twelve port manifold. As a result, a randomization order for preparation and analysis was employed. Table 2 contains the sample order used.

Two stages of SPE were employed in purification of the peptide fraction following a 30 kDa molecular weight cutoff purification step and acetic acid extraction. The strata-X-C column was conditioned with 2 mL of MeOH, 2 mL of 10 mM ammonium acetate (in 25% acetonitrile, 0.1% TFA pH 3.0), 2 mL of 500 mM ammonium acetate (in 25% acetonitrile, pH 6.8), 2 mL HPLC grade water, 2 mL 10 mM ammonium acetate (in 25% acetonitrile, 0.1% TFA pH 3.0) followed by vacuum to dry.

The 0.15 ml of acidified, filtered supernatant was added along with 0.85 mL of 10 mM ammonium acetate (in 25% acetonitrile, 0.1% TFA pH 3.0).After drawing the sample through the SPE bed, it was washed with 1 ml of 10 mM ammonium acetate (in 25% acetonitrile, 0.1% TFA pH 3.0), 1 ml of 100 mM ammonium acetate (in 25% acetonitrile, 0.1% TFA pH 3.0). Finally the sample was eluted with 1 mL of 80% MeOH/5% NH₄OH. The eluate was dried under N₂ to a volume of ∼50–100 µL and then brought up to a volume of 1 ml with 0.1% TFA.

Finally a C-18 hydrophobic interaction resin was used to clean-up marker for mass spectral analysis. The C-18 SPE was first conditioned using 2 mL of MeOH, 2 mL of H₂O and 1 ml of 0.1% TFA. The reconstituted sample was then loaded and washed with 2 mL of 0.1% TFA. The peptides were then eluted into a clean 1.5 ml eppendorf tube with 1 ml of 90% Acn/0.1% TFA. The sample was dried under N₂ flow and resuspended in 20 µL of 10% Acn/0.1% TFA or keep dry for longer term storage.

Matrix assisted laser desorption/ionization mass spectrometry

The mass spectrometric analysis of purified proteins was performed using an Autoflex II MALDI tandem time of flight mass spectrometer equipped with a HIMAS ^tm detector (Bruker Daltonics, Billerica MA). Protein samples were spotted on a ground steel 384 multi target plate using 0.5 µL of sample followed immediately by 0.5 µL of matrix solution. Sinapinic acid (SA) matrix was used as a 10 mg/ml solution in 70% acetonitrile/0.03% TFA.

Five sample spots were deposited for each sample with successive samples in a statistically randomized order and deposited in a horizontal pattern. Five replicate spots for each sample type deposited in a vertical column. An external mass calibration was performed using protein standard mix II: Bovine myoglobin, cytochrome c, ubiquitin and insulin; spotted on adjacent locations.

Data was collected in linear mode from 1,000 to 30,000 m/z using a pulsed ion extraction delay time of 120 nsec. The resulting mass spectra consisted of 500 laser shots summed together. Automated data collection proceeded across each horizontal row before proceeding to the subsequent row containing the next set of replicate sample spots. This aided in randomizing data collection. Data was processed using available functions in the vendor software including a baseline subtraction and smoothing function (Savitzky-Golay with 0.2 m/z window) followed by peak picking using the centroid function and retained peaks with s/n greater than 5.

Mass spectral data comparison

Mass spectra were compared using Clinprotools 2.0 (Bruker Daltonics, Billerica MA) for initial quality control of data. The goal of this analysis was to assess spectral quality and identification of missing data. Realignment and recalibration was done using the following settings within the software. Spectral preparation utilized the Convex Hull baseline function over a 1000–50,000 m/z range. Peak picking utilized a noise reduction threshold ignoring peaks below s/n two and picking peaks with s/n greater than five. The recalibration used a minimum resolution of 100 with a 0.2% maximum peak shift requiring 30% match to the calibrant peaks.

Feature Extraction

Peaks are detected using an automated peak detection algorithm developed at PNNL [41] creating a list of peak locations (m/z) and associated intensity values from the mass spectrum of each sample. Targeting peptides, we consider only peaks having an m/z of less than 10,000. Following methods described in [41], the full set of spectra (all technical replicates from all experimental replicates) are aligned by combining the extracted peak lists and calculating an aligned peak location for each peak by averaging the m/z values from the samples in which that peak exists. This creates a peak table with each row representing a sample, each column an aligned peak, and each entry being either the intensity of a peak occurring in a sample or a missing peak flag if that peak doesn't occur in that sample. The code for the peak detection and alignment algorithms runs in MatLab® Version R2008a.

A Kruskal-Wallis test [44] was used to extract the relevant peaks from the full peak file using the MatLab® Version R2008a. The Kruskal-Wallis test is a nonparametric version of one-way analysis of variance, which tests the hypothesis that samples are drawn from the same population. The test will return a significant p-value if anyone of the defined groups has a normalized average peak area over the replicates that are significantly different from any of the rest. The Kruskal-Wallis test was based on seven groups in the data: (1) Control, (2) Pa (4 Hrs), (3) Pa (24 Hrs), (4) Fn (4 Hrs), (5) Fn (24 Hrs), (6) Fn-ATT (4 Hrs), and (7) Fn-ATT (24 Hrs). The results of the Kruskal-Wallis test are summarized in Table 2.

Classification – Degree of Association

MALDI spectra were used to separate out the defined treatment and time groups. A mass spectral fingerprinting algorithm based on the degree of association between a MALDI reference library and the spectra of interest was used [40]. In particular the null hypothesis (H_O) that a specific sample is from class k (e.g. Fn at 4 hours) is considered versus the alternative that the specific sample is not from class k. Assuming H_O the sample under consideration can be described by the probability of observing peak i (p_i). These probabilities are compared to the reference fingerprint of class k based on the set of fingerprint peaks that are differ and are in common between the sample fingerprint and the reference fingerprint – Degree of Association (DA):(1)

M is the fingerprint peaks that are not observed in the sample, i.e. missing, and M^C is the complement, or observed peaks. All fingerprints are also subject to an occurrence filter that requires that a peak be observed in at least 60% of the replicates to be included in the fingerprint. The code to compute the DA for a fingerprint runs in MatLab® Version R2008a. The result of the model is the likelihood that that the sample is in the modeled class. Thus, the final classification of a sample i (s_i) is taken to be the class k (c_k) that the sample is classified in with maximum discrimination:(2)

Model Validation

A key step when fitting models that are to be used for prediction is to verify the results using cross-validation (CV). CV essentially is an approach to se gregate the data into independent sets so that the observations that are used to estimate the parameters of the model are not used to evaluate the classification accuracy [45]. In particular, with the DA fingerprint model leave-one-out CV (LOOCV) is used to evaluate the model accuracy. In LOOCV a single observation is left out of the model training phase. The trained DA fingerprint model is then run on this left out observation and the results are evaluated for classification accuracy. Iterating this process for each observation in the dataset returns a result for each observation that was attained in a manner that is independent from the data used to fit the model.