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. 2016 Jan 12;113(2):E110-6.
doi: 10.1073/pnas.1512057112. Epub 2015 Dec 29.

Trace incorporation of heavy water reveals slow and heterogeneous pathogen growth rates in cystic fibrosis sputum

Affiliations

Trace incorporation of heavy water reveals slow and heterogeneous pathogen growth rates in cystic fibrosis sputum

Sebastian H Kopf et al. Proc Natl Acad Sci U S A. .

Abstract

Effective treatment for chronic infections is undermined by a significant gap in understanding of the physiological state of pathogens at the site of infection. Chronic pulmonary infections are responsible for the morbidity and mortality of millions of immunocompromised individuals worldwide, yet drugs that are successful in laboratory culture are far less effective against pathogen populations persisting in vivo. Laboratory models, upon which preclinical development of new drugs is based, can only replicate host conditions when we understand the metabolic state of the pathogens and the degree of heterogeneity within the population. In this study, we measured the anabolic activity of the pathogen Staphylococcus aureus directly in the sputum of pediatric patients with cystic fibrosis (CF), by combining the high sensitivity of isotope ratio mass spectrometry with a heavy water labeling approach to capture the full range of in situ growth rates. Our results reveal S. aureus generation times with a median of 2.1 d, with extensive growth rate heterogeneity at the single-cell level. These growth rates are far below the detection limit of previous estimates of CF pathogen growth rates, and the rates are slowest in acutely sick patients undergoing pulmonary exacerbations; nevertheless, they are accessible to experimental replication within laboratory models. Treatment regimens that include specific antibiotics (vancomycin, piperacillin/tazobactam, tobramycin) further appear to correlate with slow growth of S. aureus on average, but follow-up longitudinal studies must be performed to determine whether this effect holds for individual patients.

Keywords: cystic fibrosis; hydrogen isotope labeling; infectious disease; metabolic heterogeneity; slow growth.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Using stable isotope tracers to measure microbial activity. (A) Model of a microbial population illustrates the change in biomass and the corresponding isotope label incorporation over time after addition of an isotopic spike. In all three scenarios, the isotopic enrichment proceeds identically, allowing determination of the underlying growth rate from isotopic measurements, regardless of total biomass accumulation or decline. (B) The minimum incubation times required to detect microbial activity at analytical detection limits of 0.01, 0.1, or 1 atom% 2H above natural abundance for different average generation times of microbial populations exposed to a 10% 2H2O labeling solution. See Supporting Information for equations.
Fig. 2.
Fig. 2.
Schematic illustrating the experimental approach with clinical sample acquisition, biological considerations, and data processing steps. Four stages of the process are highlighted in color: sample labeling at the hospital (gray), incorporation of the isotope label by the cells (green), measurement of specific fatty acids (guava), and quantitative data processing (gold). Specific biological considerations and data reduction steps are listed and point to the relevant figures in the text and Supporting Information. In parentheses in blue, we provide what the relative overestimates (+)/underestimates (-) of the growth rates would be for the average clinical sample without the respective corrections. For the uncertainty assessment of all parameters involved in the growth rate calculations, please see Fig. S7. Also noted are the analytical difference and data processing steps required for single-cell measurements.
Fig. S1.
Fig. S1.
S. aureus produces characteristic fatty acids distinct from host and competitors. Different pathogens have different fatty acid fingerprints that provide species-specific targets for growth rate measurements. However, only a subset is exclusively microbial (green shaded) or usually microbial (blue shaded), with several major fatty acids unsuitable for targeted analysis because they are also produced by host cells (orange shaded). We focused on S. aureus and targeted its most characteristic fatty acids (highlighted in red): a-C15:0 and a-C17:0, the anteiso methyl branched C14 and C16 saturated fatty acids.
Fig. S2.
Fig. S2.
S. aureus does not recycle exogenous fatty acids for the synthesis of methyl branched (a-C15:0, a-C17:0) fatty acids. We investigated whether S. aureus is capable of recycling exogenous fatty acids it might encounter in the lung environment to derive the biomarkers (a-C15:0 and a-C17:0) targeted in this study. The lack of fatty acid recycling is a critical aspect for this quantitative approach; if organisms build the targeted lipids from exogenous fatty acids, the amount of 2H incorporated from heavy water would be greatly reduced and therefore underestimate population growth rates. To test this, two perdeuterated precursor fatty acids (the naturally abundant octadecanoic acid and the microbially produced pentadecanoic acid, both entirely 2H-substituted in the hydrocarbon tail) were provided as an exogenous source of free fatty acids to test for recycling by S. aureus (Bottom and Middle, respectively). Although S. aureus is capable of elongating the exogenous fatty acids to produce longer-chain derivatives, it does not appear to partly break down the exogenous fatty acids and build them back up, unlike P. aeruginosa (no partly deuterated fatty acids shorter than C17/C20 could be detected in any analysis). This indicates that S. aureus’ methyl branched fatty acids (a-C15:0 and a-C17:0) measured in the lung environment are products of de novo synthesis and can be targeted for growth rate measurements.
Fig. S3.
Fig. S3.
S. aureus has a high tolerance for 2H2O in synthetic cystic fibrosis medium. We investigated the toxicity effects of increasing concentrations of 2H2O on S. aureus. This figure shows the semilog growth curves of S. aureus in the presence of varying amounts of 2H2O. Lines represent averages of at least four biological replicates; shaded area represents the maximal range of ODs in each condition.
Fig. S4.
Fig. S4.
Parameterization of noninstantaneous water exchange. The isotopic composition of newly synthesized fatty acids after administration of the isotopic water spike depends on the isotopic composition of sputum water in the clinical samples. For this study, we conducted experiments with differently sized sputum samples (0.5−2.5 g) to derive an empirical relationship for the water equilibration time that would allow a functional parameterization of the average isotopic composition microorganisms experience in the sputum over time. (A) The measurements of saline solution around sputum over time: F2 decreases as the label is exchanged into the sputum. Experiments are grouped by color, symbol sizes reflect sample weights, and dashed/dotted lines illustrate the best-fit equilibration curves for the heavy water spike/sputum water. (B) The expected sample weight dependence of the equilibration rate constant; i.e., as sputum samples become larger, it takes water from the isotopic labeling solution longer to exchange with water in the sputum sample. (C and D) The extent of the noninstantaneous water exchange effect for an average clinical sample. The solid lines show the modeled isotopic composition of the sputum water (C) and resulting fatty acid enrichment (D) if water exchange between the labeling solution and the sample were instantaneous. The dotted lines show the same metrics for a typical clinical sample (average weight of 0.86 g), based on the empirically derived water exchange model used in this study. The fatty acid enrichment for both conditions is modeled using the average growth rate measured in clinical sample.
Fig. S5.
Fig. S5.
Physiological parameters of water hydrogen assimilation. The isotopic composition of newly synthesized fatty acids after administration of the isotopic water spike depends partly on the physiology of hydrogen assimilation from water. The value of the water hydrogen assimilation constant (aw) for S. aureus was determined from the slopes of the hydrogen isotope compositions of individual fatty acids (2Ffa) vs. medium water hydrogen isotope composition (2Fwater) in cultures grown in synthetic cystic fibrosis medium with variable water isotope compositions. (A) The regression lines for individual fatty acids with 95% confidence bands. (B) A summary of the water hydrogen assimilation constants (aw) derived for individual fatty acids from regression analysis of A, and (C) their numerical values. The size of the symbols in B indicates the relative membrane abundance of the individual fatty acids. Error bars indicate 95% confidence intervals of the coefficients from the linear regression fit. The dashed horizontal line illustrates the average value for aw (0.41), determined from all fatty acids’ aw values weighted by the relative abundances of the individual fatty acids.
Fig. S6.
Fig. S6.
Chemostat constraints on maintenance vs. growth. Symbols show the time-dependent isotopic enrichment of S. aureus membrane fatty acids (weighted average isotopic composition of the different membrane components). The x axis records the time after spiking a steady-state culture of S. aureus growing at a controlled growth rate of 0.14 divisions per day (doubling time of ∼4.9 d) with heavy water. The relative fatty acid maintenance turnover rate was calculated from 2H uptake in excess of growth and was determined to amount to 56% of the growth rate.
Fig. S7.
Fig. S7.
Sensitivity of growth rate calculations to parameter uncertainty. Various sources of uncertainty introduced by analytical constraints and mathematical approximations contribute to the uncertainty estimates for the growth rates presented in this study. This figure provides a visual summary of the sensitivities to the different error sources. Dashed vertical lines indicate the average value of each parameter for the clinical samples (fatty acid isotopic composition, spiked sputum water isotopic composition, incubation time, fatty acid maintenance turnover, water exchange rate, and water hydrogen assimilation constant). The sensitivities of the growth rate estimates to the uncertainty ranges considered for each parameter are illustrated in red. Because the growth equation in this system (Eq. S17) does not have an analytical solution for the growth rate, the upper and lower error bounds presented in Fig. 3 were estimated from the maximal combined uncertainties of all key parameters that offset the growth rate positively and negatively.
Fig. 3.
Fig. 3.
Average growth rates of S. aureus in CF sputum are slow. S. aureus population growth rates calculated from the isotopic enrichment of S. aureus specific membrane components (a-C15:0△しろさんかく, a-C17:0,∇) and the abundance-weighted average isotopic composition for each data point (○しろまる/□しろいしかく). Growth rates are plotted on a logarithmic scale. Symbol size illustrates the relative abundance of the component within each sample. Error bars indicate the propagated error estimate on the population growth rate (Fig. S7). Dashed vertical lines connect the a-C15:0 and a-C17:0 data points of each sample. Samples are grouped by patient health status (well vs. sick) and color-coded by patient. Samples from sick patients are sorted in ascending order by their hospital stay day. Samples from patients whose treatment regimen at the time included any antibiotics active against S. aureus are marked with □しろいしかく instead of ○しろまる. (Antibiotics are vancomycin, oxacillin, piperacillin/tazobactam, amoxicillin/clavulanic acid, trimethoprim/sulfamethoxazole, cefazolin, clindamycin, ciprofloxacin, tetracycline, linezolid, unless S. aureus resistance to the antibiotic was reported for the sample; see Table S1 for details.) Separate column (chemostat) shows a representative growth rate from a chemostat culture of S. aureus (Fig. S6), demonstrating that it is possible to model slow clinically relevant growth rates in the laboratory. Area highlighted in blue represents the typical range of growth rates studied in laboratory experiments with S. aureus.
Fig. S8.
Fig. S8.
The membrane composition of S. aureus is growth rate-dependent. Lipid profile data from laboratory experiments at different growth rates suggest that slower growing populations of S. aureus shift their membrane composition to a higher a-C17:0 ratio (average a-C17:0 to a-C15:0 fatty acid ratio of S. aureus membranes are plotted as a function of growth rate; growth rate is plotted on a log scale for clarity). This could explain the observation in clinical samples that a-C15:0 systematically incorporates more isotope tracer than a-C17:0, potentially reflecting underlying heterogeneity in the microbial population with slower-growing cells producing a-C17:0 preferentially and faster-growing cells producing a-C15:0. Data are from Kopf et al. (27).
Fig. 4.
Fig. 4.
NanoSIMS analysis reveals that single-cell growth rates are diverse. (A) (Top) Example of target identification and 2H enrichment in plastic sections. Frames shown are 10 ×ばつ 10 μm, (Bottom) First column shows microscopy pictures with overlaid DAPI (blue), bacterial EUB338 FISH (red), and sample autofluorescence in the GFP channel (green); second column shows autofluorescence alone (contrast enhanced for mapping to ion image); third column shows the 14N12C− ion image; fourth column shows the fractional abundance image of 2H. (Scale bar in ion maps, 1 μm.) (B) Distribution of single-cell growth rates of S. aureus cells (in red) in four clinical samples using the approach described in Supporting Information. An unidentified group of bacteria (in blue) was captured in the fourth sample. Bars indicate the measured growth rates of individual cells. Curves illustrate smoothed density distribution function of the data. Growth rates plotted on logarithmic scale.
Fig. S9.
Fig. S9.
S. aureus growth rates are correlated with few clinical parameters. This figure expands on correlations with clinical parameters discussed in Correlation with Clinical Parameters (Table 1) and illustrates the correlation between the clinical parameters and the measured bulk growth rates for S. aureus. (Top) Each panel denotes a different binary parameter with the respective P values of the MWW test for the alternative hypotheses indicated in the header. (Middle and Bottom) Each panel shows the respective Spearman correlation coefficient and P value for the correlation. The different colors indicate samples from different patients in the study. All growth rates (y axis) are plotted on a log scale for clarity. Not all clinical information was available for all data points (see Table S1 for data).
Fig. S10.
Fig. S10.
S. aureus growth rates are correlated with specific antibiotic treatments, but more longitudinal data are required at the patient level. This figure expands on correlations with clinical parameters discussed in Correlation with Clinical Parameters (Table 1) and illustrates the correlation between antibiotic treatments and the measured bulk growth rates for S. aureus. (Top and Upper Middle) Each panel in the denotes a different antibiotic with the respective P values of the MWW test for the alternative hypotheses indicated in the header. Only antibiotics with at least five samples in each category (yes/no) are shown. (Lower Middle and Bottom) Each panel in the shows the same data only for patients with data in both categories (yes/no) and respective statistical tests broken down on a per-patient basis. Patients are distinguished by color. All growth rates (y axis) are plotted on a log scale for clarity. Not all clinical information was available for all data points (see Table S1 for data).

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