Bioinformatics-Extra Points
Species-Specific Bacteria Identification Using Differential Mobility Spectrometry and Bioinformatics Pattern Recognition
As bacteria grow and proliferate, they release a variety of volatile compounds that can be profiled and used for speciation, providing an approach amenable to disease diagnosis through quick analysis of clinical cultures as well as patient breath analysis. As a practical alternative to mass spectrometry detection and whole cell pyrolysis approaches, we have developed methodology that involves detection via a sensitive, micromachined differential mobility spectrometer (microDMx), for sampling head- space gases produced by bacteria growing in liquid culture. We have applied pattern discovery/recognition algorithms (ProteomeQuest) to analyze headspace gas spectra generated by microDMx to reliably discern mul- tiple species of bacteria in vitro: Escherichia coli, Bacillus subtilis, Bacillus thuringiensis, and Mycobacterium smegmatis. These encouraging in vitro results suggest that the microDMx technol- ogy, coupled with bioinformatics data analysis, has po- tential for diagnosis of bacterial infections.
Several chemical detectors and assays are presently being refined for use in the identification of volatile byproducts of bacterial metabolism which are sufficiently sensitive for analysis of volatile constituents in human breath as well as analysis of headspace above clinical cultures. Studies over the past 25 years provide consistent evidence that various microbes release different quantities and types of volatile organic compounds: automated headspace concentration gas chromatography-flame ionization detection (GC/FID) analysis of several common lung pathogens reveals a number of characteristic and highly conserved dominant components.
Previous work using microfabricated differential mobility spectrometry for bacteria classification has been coupled with pyrolysis, in which entire microorganisms are thermally degraded in search for unique cell chemistries. These techniques allow identification of organisms based on their cell components.
Another approach to studying bacteria for classification is to focus on compounds that viable bacteria naturally release. This approach requires fewer sample preparation steps as compared to pyrolysis
work and may be amenable to in vivo breath analysis applications as the process of volatile release by bacteria into vial headspace may be similar for bacteria in alveolar space.
One challenge in identification of organisms based on a set of consistent peaks in DMS profiles, as well as in MS, FID, and other detectors, is that production of volatile compounds is dependent on the dynamics of the whole ecosystem. Changes in growth conditions can produce subtle changes in the volatile profile for a given species. Moreover, the addition of other organisms can complicate the profile as volatiles released by these “contaminants” can act as a mode of communication, inducing changes in the target organism’s volatile compound production, altering the expected volatile profile.
In this work, we develop a methodology to analyze bacteria headspace based on a small, sensitive, and inexpensive detector and sophisticated data analysis that will allow classification of bacterial species despite sample-to-sample vari- ability within a species set. Bacteria selected for these experiments included Escherichia coli, Bacillus subtilis, Bacillus thuringiensis, an agent in opportunistic respiratory infections, and Mycobacterium smegmatis, a surrogate for Mycobacterium tuberculosis.
EXPERIMENTAL SECTION
Reagents.
2-Butanone, 2-pentanone, 2-heptanone, 3-octanone, 3-nonanone, and 2-decanone were purchased from Sigma Aldrich and used as received. Bacterial strains were obtained from American Type Culture Collection. Lowenstein-Jensen medium slants were purchased from Becton, Dickinson and Co. Luria-Bretani was obtained from Difco Laboratories. Agar was obtained from EM Science.
GC-microDMx Instrumentation.
The experimental setup consisted of an Agilent 7694 headspace sampler connected to the inlet of an HP 5890 IIGC.
Standards.
The detector sensitivity within this setup was tested using ketone standards (n ) 5 each).
Bacteria Culture Characterization.
The optical densities of the cultures were measured in a Cary 300 Bio UV-visible spectrophotometer (Varian, Palo Alto, CA) at 600 nm at 40-min intervals in 1-mL disposable optical polystyrene cuvettes. Duplicate samples were tested for each species. Duplicate samples were tested for each species. E. coli cell densities were approximated by plating dilutions of a culture grown for 5 h in a headspace vial.
Data Analysis.
The three-dimensional data sets that include compensation voltage (Vc), GC retention time, and signal intensity were plotted and processed using MATLAB 6.5.1 release 13. Spectra were aligned in the compensa- tion voltage dimension because Vc can be affected by moisture content and slight gas flow rate fluctuations.17,29 From each run, positive and negative spectra were concatenated. They were then aligned in the Vc dimension by a rigid shift of a few pixels or less as necessary, as determined by a maximum cross-correlation value.
RESULTS AND DISCUSSION
GC-MicroDMx Sensitivity. The sensitivity of our experimen- tal setup was determined by analyzing spectra for ketone standards at 1 ppm-1 ppb concentrations in liquid. Maximum peak intensities for each ketone at each concentration were found, and a value for estimated file background was subtracted. All positive ion spectra contain two reactive ion peak lines around -16 and -22 V. The response curves of the positive ion channel of the microDMx detector are shown in Figure 1.
Figure 1:
Response of the positive ion channel of the detector in GC-microDMx set up for bacteria headspace analysis using ketone test standards. The curves for each compound are linear fits with error as weight.
Bacteria Characterization.
We used an experimental method that created variability in volatile profiles within each species set to ensure that our bioinformatics approach is capable of finding biomarkers that were consistent in every file despite this variability. Growth curves for the organisms, shown in Figure 2, indicate that, under these culture conditions, B. thuringiensis was in lag phase for ∼1 h and in exponential growth for 5.2 h before entering stationary phase.
Figure 2
Growth curves for species studied.
Figure 3 contains representative microDMx spectra for M. smegmatis during the various phases of the growth curves. The signal intensity is in volts, and the scale is uniform for the three spectra.
Representative spectra for M. smegmatis at various stages of its growth cycle. A peak found in the lag phase, but that disappears in exponential and stationary phases, is circled. A peak that increases in intensity from exponential to stationary stage, but is not present in lag stage, is boxed.
CONCLUSIONS
It was shown that high sensitivity, potentially portable microDMx detection must be accompanied by sophisticated data analysis. The bioinformatics pattern recognition process has been successfully applied to find markers that identify bacterial species based on their volatile signatures from different phases of their growth curves.
Bibliografia:
Pubs.acs.org (n.d.) Species-Specific Bacteria Identification Using Differential Mobility Spectrometry and Bioinformatics Pattern Recognition - Analytical Chemistry (ACS Publications). [online] Available at: http://pubs.acs.org/doi/abs/10.1021/ac050348i [Accessed: 29 May 2013].
