Differential Proteomic Analysis of Rickettsia prowazekii Propagated in Diverse Host Backgrounds

Abstract

The obligate intracellular growth of Rickettsia prowazekii places severe restrictions on the analysis of rickettsial gene expression. With a small genome, predicted to code for 835 proteins, identifying which proteins are differentially expressed in rickettsiae that are isolated from different hosts or that vary in virulence is critical to an understanding of rickettsial pathogenicity. We employed a liquid chromatography (LC)-linear trap quadrupole (LTQ)-Orbitrap mass spectrometer for simultaneous acquisition of quantitative mass spectrometry (MS)-only data and tandem mass spectrometry (MS-MS) sequence data. With the use of a combination of commercially available algorithms and in-house software, quantitative MS-only data and comprehensive peptide coverage generated from MS-MS were integrated, resulting in the assignment of peptide identities with intensity values, allowing for the differential comparison of complex protein samples. With the use of these protocols, it was possible to directly compare protein abundance and analyze changes in the total proteome profile of R. prowazekii grown in different host backgrounds. Total protein extracted from rickettsiae grown in murine, tick, and insect cell lines or hen egg yolk sacs was analyzed. Here, we report the fold changes, including an upregulation of shock-related proteins, in rickettsiae cultivated in tissue culture compared to the level for rickettsiae harvested from hen yolk sacs. The ability to directly compare, in a complex sample, differential rickettsial protein expression provides a snapshot of host-specific proteomic profiles that will help to identify proteins important in intracellular growth and virulence.

INTRODUCTION

Rickettsia prowazekii, the causative agent of epidemic typhus, is an obligate intracytoplasmic bacterium with a genome annotated to encode 835 proteins (6, 12). The small rickettsial genome reflects the evolution of specialized transporters that exploit complex metabolic intermediates found in the host cell cytoplasm (7, 8, 29, 32, 35), as well as a reductive evolutionary process resulting in the loss of biosynthetic pathway components (3, 5, 6). This reductive evolution has resulted in a pseudogene-riddled genome with a high proportion of noncoding DNA (4). Characterizing rickettsial protein expression under various conditions and environments is critical to our understanding of how R. prowazekii, with its relatively small protein repertoire, exploits the intracellular niche in host cells as diverse as those of the louse vector and the human host.

Several groups have evaluated rickettsial protein expression using both gel-based and mass spectrometry (MS)-based methods (9, 10, 19, 24, 26, 27). Although gel-based methods offer certain advantages, the use of mass spectrometry offers the opportunity to evaluate comprehensive differential protein expression in complex samples. Differential analysis of proteomes has been performed using methods that employ protein labeling (20), isotope coded affinity tagging (ICAT) (14), and isobaric tags for relative and absolute quantitation (iTRAQ) (1, 28, 33, 36). Unfortunately, these methods are restrictive in that they often require sample mixing or fractionation. The primary advantage of a label-free approach (e.g., accurate mass tags and peptide derivatization) (11, 30) is the inclusiveness of the method and the multiplicity of information for each protein. When tandem mass spectrometry (MS-MS) and traditional database search algorithms (e.g., Mascot [Matrix Science] [22] or SEQUEST [Thermo Fisher Scientific, Waltham, MA] [13]) have been used, the position of a protein in an output list, ranked by protein scores, has been used to describe relative protein abundance (16). Protein ranks are calculated based on peptides selected for sequencing. As a consequence, less abundant peptides are often masked by the presence of peptides from more-abundant proteins during analyses. To address this problem, we evaluated MS-only data acquisition in a standard quadrupole time of flight (QTOF) instrument for global analysis of a bacterial proteome (31). The MS-only method provides an increased dynamic range and, with the associated peptide intensity in each run, an opportunity for true comparison of complex proteomic samples. This approach provided higher coverage in a single analysis and identified many of the proteins that had fallen below the level of detection in the traditional MS-MS analyses, generating a comprehensive analysis of the proteome of R. prowazekii (31).

The availability of instruments, such as the linear trap quadrupole (LTQ)-Orbitrap (Thermo Fisher Scientific), that can provide an MS mass accuracy of <2 ppm and parallel acquisition of sequence data (MS-MS), allows the simultaneous acquisition of intensity-based accurate mass data and the deep coverage of the peptide sequence data, permitting differential comparison of complex proteomic samples. The rapid acquisition of multiple MS-MS scans provides comprehensive peptide sequence coverage of complex protein samples and the higher mass accuracy of the LTQ-Orbitrap allows MS-only identification of less abundant unique peptides. Quantitative MS-only data also permit statistical comparisons within and between data sets, providing confidence levels in peptide assignment and differential protein abundance data.

To take full advantage of the information provided by both methods, MS-only and MS-MS, we utilized commercially available algorithms to align the quantitative MS-only data based on elution time and the comprehensive peptide coverage generated from MS-MS to identify proteins. This resulted in the assignment of peptide identities with associated intensity values, allowing for direct comparison of protein abundance between samples. This approach was used to analyze changes in the total proteome profile of R. prowazekii grown in different host backgrounds, including Gallus gallus, embryonated hen egg yolk sacs (EYS), and cell lines from the mouse Mus musculus (L929), the tick Ixodes scapularis (ISE6), and the fall army worm Spodoptera frugiperda (Sf21). The resulting comparisons revealed changes, including the upregulation of stress-related proteins in rickettsiae grown in tissue culture and the identification of a subset of proteins whose levels remained relatively constant in all backgrounds. The ability to directly compare, in a comprehensive manner, differential rickettsial protein expression provides the means to identify potentially important proteins involved in intracellular survival and virulence.

MATERIALS AND METHODS

Growth of rickettsiae.

The R. prowazekii strain (Madrid E), passage number 283, was propagated in four cell types and purified as described below. The four cell types were the EYS (eggs purchased from Charles River Labs, Hartford, CT), the L929 murine fibroblast (ATCC CCL-1; Manassas, VA), the ISE6 tick (kindly provided by Ulrike Munderloh), and the Sf21 (Gibco, Grand Island, NY) cell lines. Rickettsiae were purified from EYS as previously described (35). All tissue culture lines were passaged multiple times at 34°C and inoculated with rickettsiae purified from egg yolk sacs. Rickettsial intracellular growth was monitored using Gimenez staining (15). L929 cells were grown in modified Eagle's medium (MEM) (Mediatech, Manassas, VA) supplemented with 10% newborn calf serum (NCS) (Sigma, St. Louis, MO). I. scapularis ISE6 cells were propagated in L15B300 medium as described previously (18). S. frugiperda Sf21 cells were grown in Grace's supplemented medium (Gibco) containing 10% NCS. Following rickettsial growth, cells were harvested by scraping and collected by centrifugation at 6,000 × g for 10 min at 4°C. The resulting cell pellet was suspended in 0.218 M sucrose, 3.76 mM KH₂PO₄, 7.1 mM K₂HPO₄, 4.9 mM potassium glutamate, and 10 mM MgCl₂ (SPG-Mg), with 1 ml of SPG-Mg added per T-185 flask equivalent. Rickettsiae were released from all tissue culture cells ballistically, using 1-mm borosilicate glass beads as described previously (25). Briefly, host cells were lysed in SPG-Mg using a mini-bead beater, and intact cells and cellular debris were removed by centrifugation. Purified rickettsiae were collected from supernatants by centrifugation at 14,400 × g for 10 min at 4°C. Due to differences in rickettsial growth within each host background, the number of rickettsiae harvested from each infection varies significantly, with high rickettsial yields from EYS and small amounts from ISE6 infections. To obtain adequate samples for repeated MS analysis, multiple infections in each host background were performed; rickettsiae were collected and similar samples pooled. Following host cell lysis, all samples were treated identically. To precipitate remaining cellular debris, Celite (1 g) was added to pooled rickettsial suspensions and removed by centrifugation at 1,000 × g for 10 min. The resulting rickettsia-containing supernatants were filtered through a Millipore AP20 filter and stored at −80°C until analyzed. To control for variability, two independent protein preparations from each rickettsial growth environment were collected and analyzed.

Preparation of rickettsiae for proteomic analyses.

Samples were thawed, rickettsiae collected by centrifugation, and approximately 250 mg (wet weight) was suspended in 500 μl of 8 M urea containing protease inhibitors: 4 mM 4-(2-aminoethyl) benzenesulfonyl fluoride (AEBSF), 4 mM phenylmethylsulfonyl fluoride (PMSF), 4 μg/ml pepstatin A, 40 μM bestatin, and 4 μg/ml leupeptin. Samples were incubated for 30 min at room temperature. After incubation, 500 μl of 100 mM ammonium bicarbonate (ABC) was added and samples were subjected to 3 freeze-thaw cycles (submersion in liquid nitrogen followed by thawing at 37°C). Samples were sonicated 6 times at 50 W for 10 continuous seconds on ice, with 30 s of cooling between sonication bursts. After urea solubilization and sonication, debris was removed by centrifugation at 18,000 × g for 10 min at 4°C. Supernatants were transferred to ultracentrifuge tubes (number 343778; Beckman), and insoluble proteins were pelleted at 100,000 × g for 1.5 h (Beckman TL-100 ultracentrifuge with TLA 100.2 rotor). Supernatants were diluted 2-fold with 100 mM ABC containing 2 mM PMSF, and total protein concentrations were determined (Bio-Rad protein assay kit; Bio-Rad, Hercules, CA). Equal amounts (30 μg) of each rickettsial sample were digested overnight at 37°C with 2.5 μg of trypsin and 6 mM TCEP [tris(2-carboxyethyl)phosphine], a reducing agent, in a final volume of 200 μl. Following digestion, samples were again centrifuged at 100,000 × g for 1.5 h to remove aggregated proteins.

LC-LTQ-Orbitrap mass spectrometry.

The mass spectrometer system used in these studies consists of an Agilent 1200 series nano-liquid chromatography (nano-LC) pump and thermostated auto-injector (Agilent Technologies, Santa Clara, CA), coupled to a Thermo Fisher Scientific LTQ-Orbitrap mass spectrometer. Solvent A was 3% acetonitrile and 0.2% formic acid in water, and solvent B consisted of 3% water and 0.2% formic acid in acetonitrile. A flow rate of 4 μl/minute of 5% solvent B was used to load the sample onto a C₁₈ precolumn (5 μm; 5 by 0.3 mm; Zorbax; Agilent Technologies), and a flow rate of 1 μl/minute was used to elute the sample from the precolumn directly to the separating Hypersil Gold C₁₈ chromatography column (30 mm by 0.18 mm; Thermo Fisher Scientific). The linear solvent gradient was slowly ramped to 40% B over 70 min and then to 90% B over the final 20 min. The total run time (precolumn and resolving chromatography) for each sample injection was 2 h. The chromatography effluent was then injected into the nanoflow source of the LTQ for MS-analysis. The LTQ-Orbitrap acquired one MS-only scan (Orbitrap) at a resolution of 60,000, while acquiring up to 5 MS-MS scans (LTQ), with a consistent cycle time of approximately 1 s, using the Xcalibur software program (Thermo Fisher Scientific). Peptide masses selected for fragmentation were then added to an exclusion list (within 10 ppm) to prevent repeated sequencing of abundant peptides for 120 s following the initial fragment selection.

Data analyses.

MS-MS peptide sequence data were converted to mascot generic format files (.mgf) and matches identified using the Mascot search engine (http://www.matrixscience.com) (2). Protein identifications (with a threshold of 95% confidence) were determined by the Mascot software program. Initially, all MS-MS files were searched against the complete nonrestricted NCBInr database to identify host proteins that may have copurified with the rickettsiae. To prevent misidentification of homologous proteins, all of the identified, nonrickettsial proteins (see Table S1 in the supplemental material) were added to the R. prowazekii database (based on the deduced proteome) (6), creating a custom database to refine peptide searching and protein identification.

The MS-only data were examined using the ReSpect algorithm (Positive Probability, Ltd., Isleham, United Kingdom). This algorithm deconvolves detected peaks, converts electrospray mass spectra to zero-charge spectra, and corrects baselines, improving signal-to-noise ratios. The raw MS-only isotopic data are processed, generating a file containing deconvoluted mass, time, intensity, and probability statistics. Peptides were only accepted for analyses if they appeared in at least three consecutive MS scans and had an isotopic profile agreement confidence level of ≥95%. The resulting file is a comma-separated spreadsheet file associating peptide mass, time, and, most importantly, intensity, critical in the differential analysis.

To integrate the MS-MS sequence data and the quantitative MS-only data, we utilized DifProWare, a Web-based platform developed at the University of South Alabama (available at http://mciproteomics.usouthal.edu/difproware/). This program uses the elution time data retained in both the MS-MS and the MS-only files to align the identified peptides and their detected intensity. This merged, time-aligned data can be used to align matched peptides in different samples and directly compare their intensities in complex proteomic samples. Briefly, the user selects a control (reference) sample and the software aligns the chromatograms, matches peptide masses across time-aligned samples, assigns MS-MS sequence data to aligned masses, and calculates relative abundance ratios based on MS-only intensities. Again, to control for variability in growth and sample preparation, two independent rickettsial protein preparations for each rickettsial growth environment were analyzed. We required that any identified protein be detected in both preparations prior to conduction of any comparison between different samples.

Differential analyses.

For differential comparison of the rickettsial proteomes, 5 repeat injections from two independent isolations from each host background were analyzed, with the last injection (number 10) of each sample chosen as an alignment reference. For our studies, mass ranges were from 800 to 5,000 Da, and peptides with matching elution times within 95 s were considered. Additionally, peptides were required to have a mass agreement within 5 ppm of the mean mass to be accepted and considered for differential comparison.

To prevent erroneous ratio reporting resulting from samples with different total protein concentrations, the combined intensity of accepted peptides in each replicate were normalized against the combined intensity of the reference file. The data from all injections are used to generate the mean and standard deviation of the masses, retention times, and intensities established for each accepted peptide. The results (displayed in a spreadsheet format) contain the combined intensities of the repeat injections along with the statistical data.

The final spreadsheet displays a table sorted by mass with the associated retention time and the intensity ratio, determined by the averaged intensities observed in the experimental/reference data. Where found, MS-MS peptide sequence identification information is listed with the best Mascot score provided. If the intensity of the peptide detected in the experimental file was higher than that of the matching peptide in the reference sample, the reported ratio is >1. For example, in the comparison between EYS and ISE6, the peptide LQNNNLTIEFKNPK, from RP374 (Sec7), had ion intensities of 3,778 in EYS and 500 in ISE6; therefore, the ratio for this peptide is reported as 7.566. For peptides with intensities below the level of detection, a value of 100 (level of detection) is substituted to eliminate division by zero. So, for the peptide SNLNLEFVGDYLGTDGVDNQKVLESFTK, from the same protein, a value of 100 is substituted for the 0 in the raw data. The EYS intensity of 1,463 is therefore divided by 100, resulting in a ratio of 14.63. This results in an exaggerated fold change for proteins found in only one sample. For proteins with multiple peptides identified, ratios were averaged. If, however, the peptides have higher intensities in the reference sample than in the experimental sample, ratios are reported as fractions (see Data Set S1 in the supplemental material). The fractions are converted to fold differences for Fig. 1 and 2.

Fig. 1.

Comparisons of rickettsial proteins with ≥2-fold changes isolated from the following cell culture backgrounds: Sf21 versus L929 (A), Sf21 versus ISE6 (B), and L929 versus ISE6 (C). Fold change for each protein is plotted on the y axis, with each line representing a 2-fold increase. Functional categories were assigned based on COGs using the ERGO genome analysis suite (21).

Fig. 2.

Rickettsial proteins with ≥2-fold changes from rickettsiae isolated from EYS compared to rickettsiae harvested from three cell culture backgrounds: ISE6 (A), L929 (B), and Sf21 (C). Fold change for each protein is plotted on the y axis, with each line representing a 2-fold increase. The proteins with >14-fold changes were RP367 (*) and RP816 (#). For functional categories, see the legend in Fig. 1.

Using high-resolution LC-Fourier transform ion cyclotron resonance (FTICR) MS, Meng et al. established the reproducibility of MS signal intensity by measuring the coefficients of variance for one peptide or multiple ion species over more than 20 replicates and determined that a 1.5-fold change can be detected with statistical significance (17). Based on these analyses, we consider data with changes in intensity of at least 1.5 standard deviations significant and report these changes in Results and Discussion (see Table S2 in the supplemental material). However, for publication purposes, only proteins with ≥2-fold changes are shown in Fig. 1 and 2.

In silico analyses.

The identified proteins were placed in functional categories by clusters of orthologous groups (COGs) using the ERGO genome analysis suite (Integrated Genomics, Mount Prospect, IL) (21).

RESULTS

Identifying which proteins are differentially expressed in rickettsiae from diverse hosts is critical to an understanding of rickettsial intracellular growth and pathogenicity. We hypothesized that rickettsiae growing in different cell types, mimicking the various host environments, would exhibit sufficient proteomic differences for analysis. For comparison, we chose four host backgrounds: (i) rickettsiae harvested from embryonated hen egg yolk sacs (EYS), a standard host for the propagation of R. prowazekii, (ii) to represent the mammalian model in tissue culture, the murine fibroblast L929 cell line, (iii) to mimic the louse vector, the I. scapularis ISE6 cell line (although R. prowazekii is not normally associated with ticks, other rickettsial species are known to grow in these cells [18, 23, 34]), and (iv) an additional arthropod host, the commercially available S. frugiperda insect cell line (Sf21).

Although total protein preparations were generated for each rickettsial sample to encompass the largest number of proteins examined, extensive purification of the rickettsiae from host cells was performed to reduce nonspecific carryover proteins or host proteins that may interact specifically with rickettsiae. To prevent the misidentification of conserved carryover proteins from the host, each sample was analyzed by MS-MS and searched against the NCBI nonredundant database. The sequences of all identified nonrickettsial contaminating proteins (see Table S1 in the supplemental material) were then added to the predicted rickettsial proteome database to prevent the erroneous addition of intensities from conserved eukaryotic peptides to the rickettsial intensities.

Comparisons of the proteins from each host background (EYS, L929, ISE6, and Sf21) generated six data sets, EYS/L929, EYS/ISE6, EYS/Sf21, L929/Sf21, ISE6/L929, and ISE6/Sf21. Initial comparison data for each set are shown in Table 1. For details about specific proteins identified, complete data from all comparisons are available in Data Set S1 in the supplemental material. After all of the stringent parameters were applied, the number of total proteins identified within a set ranged from 102 to 178. Although data sets that included L929 had more peptide ions detected by MS, sequenced peptides (MS-MS), and total proteins identified, the increase is a result of 17 murine carryover proteins identified in these samples (Table S1). When we subtracted the host carryover proteins, the number of rickettsial proteins remaining in each data set ranged from 70 to 158, with the highest number of rickettsial proteins identified in data sets containing EYS samples. While the percentage of proteins exhibiting significant changes (≥1.5-fold) ranged from 48 to 64% (Table S2), that percentage drops to 19 to 41% when applying the more stringent 2-fold-change parameter. As expected, at least 58% of the proteins identified changed less than 2-fold in all comparisons, revealing a subset of proteins whose expression levels are similar in all of the samples examined. Interestingly, under the parameters used in these analyses, there were only four proteins (RP070 [secB], RP087 [tsf], RP124 [dgeP], and RP228 [ctp]) whose relative intensities varied by less than 1.5-fold between all sample comparisons (Data Set S1).

Table 1.

General statistics of rickettsial mass spectrometry comparisons

Characteristic	Value for:
	EYS/L929	EYS/ISE6	EYS/Sf21	L929/Sf21	ISE6/L929	ISE6/Sf21
No. of ions detected	6,540	3,082	4,822	6,259	4,536	3,166
No. of peptides sequenced	709	398	660	655	413	375
No. of host proteins	33	18	20	30	32	14
No. of rickettsial proteins	128	126	158	116	70	120
No. of proteins with ≥1.5-fold change (%)	80 (63)	80 (64)	88 (56)	64 (55)	34 (49)	57 (48)
No. of proteins with ≥2-fold change (%)	53 (41)	31 (25)	55 (35)	31 (27)	13 (19)	29 (24)

Comparisons of rickettsiae harvested from the three tissue culture backgrounds show the least variation between samples, with a maximum of 31 proteins (L929/Sf21) showing ≥2-fold changes (Fig. 1). Only 13 proteins were differentially expressed ≥2-fold between the L929 and ISE6 samples, while only 2 of the 29 differences in the Sf21/ISE6 comparison are a result of a relative increase in proteins from the tick cell-derived rickettsiae.

When compared to rickettsiae harvested from EYS, tissue culture-derived samples demonstrate increased ratios in several stress-related proteins. In Fig. 2, there are 10 examples (yellow bars) of stress-related proteins elevated ≥2-fold. Among the proteins that change significantly (≥1.5-fold) (see Table S2 in the supplemental material), the cold-shock protein CspA (RP670) is elevated in all three tissue culture backgrounds (1.8- to 3.56-fold). The relative increase of some stress-related proteins is unique to a specific host cell background, e.g., RP196 is only found elevated ≥1.5-fold in L929-derived samples.

In addition to the stress-related proteins, there were proteins uniquely elevated in each cell culture background. For example, 8 proteins were elevated ≥1.5-fold in Sf21 rickettsiae, 4 of which show significant increases in all three comparisons (Table 2). RP196 and the dUTPase (RP399) were elevated in all comparisons, as was RP365 in ISE6 cells (Table 2).

Table 2.

Proteins uniquely elevated in each cell culture background^a

Cell line	Gene	Protein description
L929	RP196	Universal stress protein UspA-like nucleotide-binding protein
	RP399	Deoxyuridine 5′-triphosphate nucleotidohydrolase
ISE6	RP365	Enoyl-(acyl carrier protein) reductase
SF21	RP039	30S ribosomal protein S6
	RP137	50S ribosomal protein L1
	RP576	Protein export protein PrsA precursor (prsA)
	RP653	30S ribosomal protein S3

^aProteins elevated greater than 1.5-fold compared to all other host backgrounds.

While proteins involved in growth and general metabolism were identified in all sample sets, rickettsiae isolated from EYS are the only samples to show relative increases in proteins involved in secretion, membrane transport, and metabolism of nucleotide and phosphorus. Although equal amounts of total protein were analyzed for each sample, rickettsiae harvested from EYS had the highest number of rickettsial proteins identified (see Table 2 and Data Set S1 in the supplemental material). Finally, there are 34 proteins with unknown function that vary under our different growth conditions. Included in these are 7 (RP-068, -075, -226, -245, -409, -441, and -827) whose relative protein levels are elevated only in rickettsiae from EYS.

DISCUSSION

Analyzing complex protein mixtures is difficult, especially when comparisons of different samples are the goal. This paper describes an analysis regimen that permits accurate identification of proteins that differ in relative expression between two different samples. This protocol was used to identify rickettsial proteins that vary in expression when the rickettsiae are growing in different host cell backgrounds.

Stringent proteomic analysis tools were applied to generate comparison data between rickettsiae growing in different host cell environments. This included rickettsiae grown in embryonated hen eggs (the laboratory standard culture system for obtaining high bacterial numbers), the mammalian L929 cell line, and two arthropod cell lines, from S. frugiperda and I. scapularis (surrogates for growth in the R. prowazekii louse vector). Because of the problems associated with proteomic analyses, especially when applied to an obligate, intracellular, parasitic bacterium, it was imperative to demonstrate the reproducibility of the methods used in order to ensure the accuracy of differential data and instill confidence that proteins designated differentially expressed are accurately identified. The data demonstrate the power of these techniques and provide the means to compare a variety of rickettsial proteomic samples.

As with any comprehensive analysis of the total proteome profile, the amount of data generated in these studies is massive. Each protein identified, whether differentially expressed or consistently detected, may hold interest for an individual researcher. To that end, all of the data are available in the tables in the supplemental material. For this publication, we chose to focus on the proteins that were differentially expressed between the samples with changes that were statistically significant (≥1.5-fold) or stringently defined (≥2-fold). However, even this selective view of the data reveals some striking information.

The four proteins (RP070 [secB], RP087 [tsf], RP124 [dgeP], and RP228 [ctp]) whose relative intensities varied less than 1.5-fold between all sample comparisons may represent examples of housekeeping proteins and provide targets for normalizing proteomic data in future studies. As more targeted proteomic analyses are undertaken, the presence of a stable rickettsial protein, similar to GAPDH (glyceraldehyde-3-phosphate dehydrogenase) or β-actin utilized in eukaryotic protein analysis, will allow quantitative comparisons at the protein level.

The data from the EYS-derived rickettsiae demonstrate a relative elevation in protein abundance involved in protein synthesis. These include cell wall and ribosomal components and proteins involved in membrane transport, secretion, and the metabolism of phosphorous and nucleotides. These observations suggest that rickettsiae grown in embryonated hen eggs have increased metabolic activity and may indicate an environment better suited for rickettsial growth. This may be the result of the rickettsiae growing within the endothelial cells of the EYS, their preferred target cell. In contrast, the rickettsiae grown in any of the cell cultures differentially express proteins associated with stress responses. This observation is very important to those researchers who choose to use cell culture systems as a model for understanding rickettsial intracellular growth and pathogenicity. The stress proteins identified are similar between the rickettsiae grown in L929, ISE6 tick, and Sf21 cells, suggesting cell culture is a general stress environment for the rickettsiae. Further studies into the stress response may lead to a better understanding of the adaptations rickettsiae undergo when transitioning between hosts. We may also discover additional host cell contributions to rickettsial growth that may be lacking in cell culture, furthering our understanding of the intracellular growth of this unique pathogen.

For most researchers, a predicted gene with unknown function becomes infinitely more interesting when that protein is actually translated and detected in vivo. When combined with the fact that several of the proteins reported in this study are differentially expressed or are uniquely found in certain host backgrounds, these unknowns become viable targets for future studies. The ability to compare complex protein mixtures and identify differentially expressed proteins provides a powerful tool for dissecting the critical components involved in rickettsial obligate intracellular growth.