August 2003
Volume 44, Issue 8
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Immunology and Microbiology  |   August 2003
Analysis of Pseudomonas aeruginosa Corneal Infection Using an Oligonucleotide Microarray
Author Affiliations
  • Xi Huang
    From the Department of Anatomy and Cell Biology, Wayne State University School of Medicine, Detroit, Michigan.
  • Linda D. Hazlett
    From the Department of Anatomy and Cell Biology, Wayne State University School of Medicine, Detroit, Michigan.
Investigative Ophthalmology & Visual Science August 2003, Vol.44, 3409-3416. doi:10.1167/iovs.03-0162
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      Xi Huang, Linda D. Hazlett; Analysis of Pseudomonas aeruginosa Corneal Infection Using an Oligonucleotide Microarray. Invest. Ophthalmol. Vis. Sci. 2003;44(8):3409-3416. doi: 10.1167/iovs.03-0162.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

purpose. To compare the early gene expression pattern of normal versus Pseudomonas aeruginosa–infected corneas in resistant (cornea heals) versus susceptible (cornea perforates) mice.

methods. A microarray analysis of normal versus postinfection (PI) day 1 BALB/c and B6 corneas was performed with a murine gene microarray. Real-time RT-PCR was used to confirm the microarray pattern selectively.

results. The 1257 regulated transcripts detected were organized into nine clusters by a self-organizing map (SOM) algorithm according to their different behavior in each mouse group. At least three groups of genes associated with a CD4+ T-cell type-1 (Th1) immune response and three clusters linked with a type-2 T-cell (Th2) response were identified. Biological categorization revealed that the cornea of B6 mice showed a dominant type-1–like immune response profile, whereas BALB/c mice showed a dominant type-2–like profile. In addition, expression of several genes that promote apoptosis (e.g., caspase-9) was upregulated in BALB/c mouse cornea, whereas genes with apoptosis-inhibiting activity (e.g., BCL2) were significantly upregulated in B6 mouse cornea. The infected cornea of BALB/c mice also showed increased gene expression of factors associated with matrix remodeling and tissue repair (e.g., tissue inhibitor of matrix metalloproteinase [TIMP-2] and epidermal growth factor [EGF]) and/or bacterial killing (e.g., inducible nitric oxide synthase [iNOS]).

conclusions. The data provide new insight into biological processes involved in Pseudomonas aeruginosa keratitis and confirm that B6 mice are Th1 and BALB/c mice are Th2 cytokine responsive to bacterial antigen early after challenge with P. aeruginosa.

The corneal infection caused by Pseudomonas aeruginosa develops rapidly, triggering an inflammatory response that may lead to vision loss. 1 2 In the United States, the incidence of microbial keratitis is 25,000 to 30,000 cases annually, and the cost of medical treatment is estimated at $15 to $30 million. 3 4 P. aeruginosa keratitis frequently causes microbial keratitis, particularly in extended-wear contact lens users, 5 6 making disease induced by this pathogen of considerable medical and economic impact. 
The cellular response after ocular infection with P. aeruginosa is predominantly neutrophilic (polymorphonuclear neutrophils; PMN), but T-cells also have been implicated in disease pathogenesis, resulting in susceptibility (corneal perforation) in the mouse. 7 In this regard, Mosmann et al. 8 9 have reported that cloned murine CD4+ T helper (Th) lymphocytes could be divided into two subsets based on production of immunoregulatory cytokines. Th1 (type 1) clones produce IFN-γ, IL-2, and TNF-β (lymphotoxin), whereas Th2 (type 2) clones produce IL-4, -5, -10, and -13. Other cytokines not characterized as Th1 or Th2 type, including IL-12 10 11 and -18, 12 13 also contribute to immune regulation, and PMNs, macrophages (Mφs), and dendritic cells (DCs) 14 can produce both Th1 and Th2 cytokines. Recently, it was reported that T-helper subsets are further characterized by expression of chemokines and chemokine receptors 15 and Charles et al., 16 demonstrated that cytokines, chemokines, and chemokine receptors affect the Th1-Th2 bias decision at the transition from innate to acquired immune response. 
Pathogenesis studies have shown that type-1 and -2 immune responses control resistance and susceptibility to many microorganisms. 17 18 Infectious, 19 allergic, 20 and autoimmune 21 disease models have shown that type-1 and -2 immune responses can cause morbidity and death if unchecked. Nonetheless, the early molecular mechanisms underlying type-1 and -2 immunity and pathogen–host interactions remain unresolved. Studies from this laboratory have shown that after P. aeruginosa corneal infection, C57BL/6 (B6) versus BALB/c mice, characterized as type-1–like or type-2–like responsive to many antigens, respectively, 22 are susceptible (cornea perforates), versus resistant (cornea heals). 7 In the context of bacterial keratitis, the gene expression pattern of several proinflammatory mediators also has been described in P. aeruginosa corneal infection. 23 24 However, the overall gene expression profile promoting susceptibility or resistance to P. aeruginosa and whether B6 mice are dominant type-1–like and BALB/c mice are dominant type-2–like responsive to P. aeruginosa antigens remains untested. Therefore, our goal was to compare the early transcriptional profile of the infected cornea in the two mouse groups after P. aeruginosa infection, by using microarray and real-time reverse transcriptase–polymerase chain reaction (RT-PCR). 
Materials and Methods
Infection
Eight-week-old female BALB/c and B6 mice (The Jackson Laboratory, Bar Harbor, ME) were anesthetized with ether (Fisher Scientific, Fair Lawn, NJ) and placed under a stereoscopic microscope and the cornea scarified. 24 A 5-μL bacterial suspension containing 1 × 106 colony-forming units (CFU)/μL P. aeruginosa strain 19660 (American Type Culture Collection, Manassas, VA), prepared as described, 24 was topically applied and eyes examined at postinfection (PI) day 1 to confirm disease. Animals were humanely treated in compliance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. 
RNA Extraction
Corneas were collected from uninfected (unwounded, normal) and PI day-1 B6 and BALB/c mice. Unwounded versus wounded corneas were analyzed because in a previous study, 25 using an RNase protection assay (RPA), no difference between the two was detected in mRNA expression at 24 hours after wounding. For microarray, corneas (n = 10/group per time) were submerged in an RNA stabilization reagent (RNAlater; Qiagen Inc., Valencia, CA) at 4°C until RNA extraction. Total corneal RNA from each mouse group was isolated with extraction reagent (TRIzol; Invitrogen-Life Technologies, Carlsbad, CA). The quality and concentration of RNAs were determined with a bioanalyzer (Agilent Technologies model 2100; Caliper Technologies Corp., Palo Alto, CA) and judged sufficient when the 28S-to-18S rRNA ratio met or exceeded 1.3. For real-time RT-PCR, corneas (n = 5/group per time) were frozen in liquid nitrogen and stored at −80°C until RNA extraction. Frozen corneas were homogenized in RNA extraction reagent (STAT-60; Tel-Test, Friendsville, TX), and total RNA isolated per the manufacturer’s instruction. RNA was treated with DNase (Qiagen Inc.) to remove genomic DNA and avoid coamplification in the real-time PCR assay, 26 and the final concentration of total RNA was determined spectrophotometrically at 260 nm. 
Microarray
Total RNA was amplified and labeled with biotin 27 and samples hybridized as described by others. 28 For microarray, a murine array (MG-U74; Affymetrix; Santa Clara, CA) was used as the manufacturer recommended. Briefly, 8 μg of total RNA was reverse transcribed to generate cDNA. After second-strand synthesis, double-stranded cDNA was used in an in vitro transcription (IVT) reaction to generate biotinylated cRNA. After purification and fragmentation, 15 μg of cRNA was used in a 300-μL hybridization mixture containing IVT controls. Approximately 200 μL of the mixture was hybridized on the mouse array chips for 16 hours at 45°C. A standard posthybridization wash was performed and images were scanned (GeneArray Scanner; Hewlett-Packard, Palo Alto, CA). 
Data Analysis
Data were analyzed on computer (Microarray Suite; MicroDB, Data Mining Tool [DMT] and Netaffx software; Affymetrix). Each probe set (gene or expressed sequence tag [EST]) was monitored by 20 pairs of features that consisted of 25-mer oligonucleotides. One, perfectly complementary to the gene sequence, was considered a perfect match (PM), whereas the other identical, except for a single mismatch centrally in the oligonucleotide, was a mismatch (MM). A PM and its MM was considered a probe pair. Each probe pair in a probe set had a potential vote in determining whether the measured transcript was detected. The vote was described by the “discrimination score” (R), a property of a probe pair that describes its ability to detect its intended target. It measured the target-specific intensity difference of the probe pair (PM − MM) in relation to its overall hybridization intensity (PM + MM). The R score [R = (PM − MM)/(PM + MM)] was calculated for each probe pair and compared to a predefined threshold τ, a small positive number that can be adjusted to increase or decrease sensitivity and/or specificity. The default parameter τ (default, 0.015) was used, and probe pairs with R scores higher than τ voted for Presence, while R scores lower than τ indicated Absence. For probabilities, results below 0.04 were assigned a Present call and above 0.06 an Absent call, which indicated whether a transcript was reliably detected (Present) or not (Absent). Those transcripts that showed at least one Present call for an experimental point in all three repeats of the microarray were included. To classify the profile of gene expression, cluster analysis was performed with the microarray software (DMT 3.0; Affymetrix). This generated a subset of regulated transcripts that were organized into nine clusters based on a self-organizing map (SOM) algorithm, according to their different behavior. Biological functions were assigned on computer (Netaffx software; http://www.affymetrix.com/analysis/index.affx/ Affymetrix). 
Real-Time RT-PCR
The real-time RT-PCR methods have been described. 29 30 Primers for the quantitative detection of target mRNAs were designed on computer (Primer Express software; Applied Biosystems, Foster City, CA). To minimize variation, all RNA samples from a single experiment were reverse transcribed simultaneously. For subsequent PCR amplification, a maximum of 2 μL of each cDNA sample was used per 25-μL PCR reaction. The real-time measurements were analyzed in duplicate in three independent runs, using an automated cycler (Smart Cycler System; Cepheid Inc., Sunnyvale, CA). The fractional cycle number in which the amount of detected fluorescence exceeded the fixed threshold value was defined as the threshold cycle (CT). For each primer pair, the linearity of detection was confirmed to have a correlation coefficient of at least 0.98 (r 2 > 0.98) over the detection area by measuring a fivefold dilution curve with RNA isolated from each mouse cornea. The multiple showing the difference in expression on PI day 1 versus normal cornea was calculated after each CT was normalized to β-actin. 
Statistical Analysis
Significance was determined with the Wilcoxon signed rank test (Affymetrix software; Santa Clara, CA) for microarray data and an unpaired student t-test (Prism; Graph Pad, San Diego, CA) for real-time RT-PCR data. P < 0.05 was considered significant, and all experiments were repeated at least twice. Representative data from a single experiment are shown. 
Results
Corneal Gene Expression Profiles
Figure 1 shows microarray results, with data represented as scatterplots to illustrate expression levels of the approximately 12,000 genes tested. For each graph, the upper and lower boundaries represent a ± twofold change in gene expression. In B6 (susceptible; Fig. 1A ) or BALB/c (resistant; Fig. 1B ) mice, expression differences were obtained by comparing PI day-1 corneas with normal corneas. Approximately 7998 transcripts (Figs. 1A 1B ; blue dots) showed a Present call and underwent clustering analysis. For both B6 and BALB/c mice, most of the blue dots fell within the ± twofold limit. Of the 7998 transcripts, 1257 (approximately 10% of the total genes per chip) changed in expression more than twofold in either B6 or BALB/c mouse cornea. These transcripts were grouped into nine clusters based on an SOM algorithm. Of the 1257 transcripts, 737 changed in expression more than twofold in the infected cornea of both mouse strains. Of the remaining 520 genes, 115 genes changed in expression more than twofold in B6 versus BALB/c mice, whereas 405 transcript changed in expression more than twofold in BALB/c versus B6 mice. Figures 1C 1D 1E 1F show the scatterplot analysis of these clustered 1257 transcripts. Transcripts denoted by red dots (Figs. 1C 1D) indicate that expression of most of these genes changed in the range of ±2- to 300-fold in the infected cornea of susceptible B6 mice, but changed less than fourfold in resistant BALB/c mice. Green dots (Fig. 1E 1F) indicate genes for which most of the transcripts fell within the ± twofold limit in B6 mice when comparing PI day 1 versus normal cornea, but were upregulated greater than twofold in BALB/c mice. Figure 2 shows the average multiples of change in distribution of these 1257 transcripts in B6 and BALB/c mice. In the range of two- to sixfold change, a greater number of genes were upregulated in the infected cornea of BALB/c than in B6 mice, but in the range of a greater than sixfold change, more transcripts were elevated in the infected cornea of B6 mice. A similar number of transcripts were downregulated in both mouse strains. Table 1 shows the RNAs of cellular genes up- or downregulated fourfold or higher in both mouse strains. 
Clustering Analysis
A feature of the microarray software (DMT-version 3.0; Affymetrix) was used to organize the regulated genes into nine clusters of different expression patterns (Fig. 3) . Clusters 3 to 5 represent dominant type-1 immune response–associated genes with changes in expression after P. aeruginosa infection. These genes were strongly up- or downregulated 2- to 300-fold in the cornea of infected versus normal B6 mice, but were only slightly induced (<fourfold) in the infected cornea of BALB/c mice. Cluster 3 (62 transcripts) includes genes (e.g., caspase-1, IFN-γ receptor, CC-chemokine receptors [CCR]-2, -5, and -7) that were strongly upregulated in B6, but little changed in BALB/c mice. In cluster 4 (310 transcripts), genes (e.g., TNF-α, CD14, IL-1β, MIP-2) were strongly upregulated in B6, but only slightly upregulated in BALB/c mice. Genes (e.g., TGF-β-3, defensin-β-2, BMP-4) in cluster 5 (11 transcripts) were significantly downregulated in infected versus normal B6 mouse cornea, but were little changed in the cornea of BALB/c mice. Clusters 7 to 9 represent dominant type-2 immune response genes induced after bacterial infection. These gene clusters were upregulated in the BALB/c (resistant mouse) cornea. Cluster 7 (40 transcripts) includes genes (e.g., IL-10, caspase-3, eotaxin) that were upregulated more than twofold in BALB/c mice, but only slightly changed in B6 mice. In cluster 8, a total of 347 transcripts were strongly elevated in BALB/c mice, but were unchanged in B6 mice. Th2-responding genes, such as IL-4, -5, and -13 and MCP-2 were in this cluster. Cluster 9 (52 transcripts) was the only cluster that showed gene downregulation in susceptible mice, but significant upregulation in resistant mice. Genes, such as caspase-9, TCA-3, CCR-4, and CCR-8 are examples. Of the three remaining clusters, cluster 1 (52 transcripts) included genes (e.g., MyD88) upregulated in the infected cornea of both mouse groups, whereas clusters 2 (322 transcripts) and 6 (61 transcripts) included genes (e.g., TIMP-3, defensin-β3) downregulated in both mouse groups. Figure 4 shows multiples of change of the selected transcripts, representing the gene expression pattern of each of the nine clusters. Transcripts such as MyD88, TIMP-3, CCR-2, CD-14, TGF-β3, defensin-β3, IL-10, iNOS, and caspase-9 are shown to represent the gene expression pattern of clusters 1 to 9, respectively. 
Reverse Transcription–Polymerase Chain Reaction
Quantitative real-time RT-PCR was used to confirm the expression levels of selected transcripts from each of the nine clusters identified by microarray. Table 2 shows primer sets for the quantitative detection of the nine genes. After normalization to β-actin, the average multiple of change was calculated based on the standard curve for each gene (Table 3) . Figure 5 shows PCR results. The molecular weights of the bands amplified by real-time PCR agreed with the predicted sizes of the corresponding primer sets. Overall, transcriptional preferences recorded with real-time PCR corresponded well with the differences recorded by microarray. Gene expression levels of MyD88, TIMP-3, CCR2, and defensin-β-1 obtained by real-time PCR were consistent with the pattern obtained by microarray. However, higher-multiple changes for IL-10, iNOS, CD14, and caspase-9 were shifted by real-time PCR, but the expression pattern remained similar to the microarray data. TGF-β-3 was the only selected gene that showed deviation. Gene expression levels by microarray analysis were downregulated −2.8-fold versus −1.4-fold in the infected cornea of susceptible versus resistant mice, respectively, but repressed −2.4-fold versus −7.7-fold by real-time RT-PCR. 
Discussion
Numerous studies have furthered our understanding of P. aeruginosa pathogenesis, but early regulatory mechanisms that may influence innate and acquired immunity have not been completely elucidated. Thus, microarray analysis was used for comparative examination of early pathogen–host interactions after P. aeruginosa corneal infection in groups of mice that are susceptible (cornea perforates) or resistant (cornea heals). 24  
Organizing microarray gene expression data rely on grouping genes with similar expression patterns that can be interpreted as indication of cellular processes, 31 and various techniques have evolved to monitor gene expression patterns. 32 33 In our study, a computer program (DMT; Affymetrix) and an SOM algorithm revealed 1257 transcripts (nine clusters) that were distinctly changed after bacterial infection in susceptible B6 or resistant BALB/c cornea. Distinct patterns of cytokine–chemokine production are also important in the pathogenesis of human inflammatory diseases. 34 35 Studies have focused on genes that are differentially regulated in type-1 or -2 immune responses to explain distinct functional profiles of immunologic disorders. 36  
Microarray profiles of P. aeruginosa infected cornea of susceptible B6 versus resistant BALB/c mice revealed a large number of molecules known to activate and potentiate inflammation. In addition to proinflammatory molecules such as IL-1β, TNF-α, IFN-β, and lymphotoxin, transcripts for MIP-1α, -1β, and -2; MCP-1; RANTES; GM-CSF; and CCR-1, -2, and -5 were expressed. MIP-1α, MIP-1β, RANTES, and lymphotactin are chemoattractants for Th1 cells, 37 but also function with IFN-γ and TNF-α to generate a type-1 immune response. 38 39 These data complement studies regarding cytokine mRNA expression analysis in both outbred 25 and inbred B6 and BALB/c mice, 40 41 42 using RPA analysis and with more recent semiquantitative RT-PCR studies. 13 Although the expression level of IFN-γ did not significantly change at PI day 1 versus normal B6 mice, transcripts for IL-12 and IFN-γ receptors were significantly elevated, complementing our semiquantitative RT-PCR work on IL-12. 43 In addition, CCR-1, -2, and -5, preferentially expressed in a type-1 immune response, 44 45 were coexpressed with chemokines in the infected B6 cornea. Thus, we suggest that susceptible B6 mice that respond to many antigens in a Th1-type 22 manner also initiate a dominant type-1–like immune response to P. aeruginosa corneal infection. In contrast, in the infected cornea of resistant BALB/c mice, classic Th2 cytokines IL-4, -5, -10, and -13 and transcripts such as TCA-3, TARC, MCP-2, CCR4, CCR8, and iNOS were expressed at a higher level. This suggests that BALB/c mice initiate a dominant type-2 immune response to P. aeruginosa corneal infection, and the data complement previous work. 7  
Apoptosis is important in the pathogenesis of bacterial infections, 46 47 but its role in P. aeruginosa corneal infection remains unresolved. Apoptosis is a default state for many cells such as PMN, Mφ, and T cells, and cell survival is contingent on cell rescue by environmental signals. 48 Although apoptosis in mature PMNs is a constitutive process, 49 it can be regulated by inflammatory mediators. 50 51 Our microarray data revealed that the expression of several apoptosis-inhibiting related genes, including caspase-1 and -11, TNF-α, GM-CSF, IL-1β and -6, and BCL-2, were upregulated in the P. aeruginosa–infected cornea of susceptible mice, whereas genes with proapoptotic activity, including caspase-3, -8, and -9 and cytochrome c, were upregulated preferentially in the infected cornea of resistant mice. These findings imply that in B6 mice, inhibition of apoptosis may fuel inflammation, enhance susceptibility, and contribute to stromal destruction, whereas the converse occurs in BALB/c mice. 
Toll-like receptors (TLRs) are critical in the innate immune response, functioning as recognition receptors for pathogen-specific molecules. 52 However, the role of TLRs in P. aeruginosa keratitis is not well understood. 4 Our array data showed that the expression of TLRs and related molecules including CD14, sIL-1Ra, TLR-6, and IL-18R accessory protein are significantly elevated in susceptible versus resistant mice, suggesting an important role for these molecules early in the disease response. 
In conclusion, microarray analysis, combined with quantitative real-time PCR, provides a comprehensive view of the genetic events characterizing the initial development of disease in P. aeruginosa keratitis. Results provide insight regarding unappreciated mechanisms of pathogenesis and perhaps new targets for treatment of this destructive inflammatory disease. 
 
Figure 1.
 
Scatterplot analysis. For each, the top and bottom boundaries represent a ± twofold change in gene expression. (A, B) Most of the blue dots (Present call) fell within the ± twofold limit. The expression of 1257 transcripts was changed greater than twofold in both mouse groups. A scatterplot analysis of these 1257 transcripts is shown in (CF). The transcripts shown in red dots (C, D) represent type-1 immune response genes that changed ±2- to 300-fold in B6 (C) mice, but changed less than 4-fold in BALB/c (D) mice. Green dots (E, F) represent type-2 immune response transcripts that were changed within the ± twofold limit in B6 (E) mice, but were upregulated more than twofold in BALB/c (F) mice.
Figure 1.
 
Scatterplot analysis. For each, the top and bottom boundaries represent a ± twofold change in gene expression. (A, B) Most of the blue dots (Present call) fell within the ± twofold limit. The expression of 1257 transcripts was changed greater than twofold in both mouse groups. A scatterplot analysis of these 1257 transcripts is shown in (CF). The transcripts shown in red dots (C, D) represent type-1 immune response genes that changed ±2- to 300-fold in B6 (C) mice, but changed less than 4-fold in BALB/c (D) mice. Green dots (E, F) represent type-2 immune response transcripts that were changed within the ± twofold limit in B6 (E) mice, but were upregulated more than twofold in BALB/c (F) mice.
Figure 2.
 
Frequency distribution. A histogram of 1257 selected transcripts revealed mouse strain–specific differences in corneal gene expression profiles. Distribution of average differences are shown for normal versus P. aeruginosa–infected corneas in B6 versus BALB/c mice.
Figure 2.
 
Frequency distribution. A histogram of 1257 selected transcripts revealed mouse strain–specific differences in corneal gene expression profiles. Distribution of average differences are shown for normal versus P. aeruginosa–infected corneas in B6 versus BALB/c mice.
Table 1.
 
RNA Expression of Cellular Genes Up- or Downregulated Fourfold or More in Both Mouse Strains
Table 1.
 
RNA Expression of Cellular Genes Up- or Downregulated Fourfold or More in Both Mouse Strains
Gene Name Accession No.
Cytokines
 IL-1α M14639
 IL-1β M15131
 IL-6 X54542
 IL-12 M86672
 IFN-β V00755
 CSF-3 M13926
 GM-CSF X03020
 TNF-α D84196
 IL-4 AA967539
 IL-10 M37897
 IL-18 D49949
Chemokines/inflammatory factors
 MIP-1α (CCL3, Scya3) J04491
 MIP-1β (CCL4, Scya4) X62502
 MIP-1γ (CCL9, Scya9) U49513
 RANTES (CCL5, Scya5) AF065947
 MIP-2 (CXCL1-3, Scyb2) X53798
 MCP-1 (CCL2, Scya2) M19681
 CCR1 U29678
 CCR2 U56819
 CCR4 U15208
 CCR5 AV370035
 KC J04596
 MRP-14 M83219
 TARC (CCL17, Scya17) AJ242583
 Eotaxin (CCL11, Scya11) U77462
Apoptosis/cell cycle
 Cytochrome c X15963
 Apoptosis inhibitor 1 U88908
 Caspase 11 Y13089
TLRs/signal transduction
 CD14 X13333
 sIL-1Ra L32838
 TLR-6 AB020808
 IKKi AB016589
 NIK U88984
 Fas antigen M83649
 TNF Receptor 1 X87128
 TRAF-1 L35302
Extracellular Matrix/MMPs
 MMP9 X72795
 MMP-10 Y13185
 MMP-13 X66473
 TIMP-1 V00755
 Casein Kappa M10114
 PAI-1 M33960
 PAI-2 X16490
 Tenascin C X56304
Other molecules
 Laminin-β-3 U43298
 Laminin-γ-2 U43327
 Integrin-β-2 M31039
 L-Selectin M36058
 P-Selectin M72332
 ICAM-1 (CD54) M90551
 Defensin-β-1 AF003525
Figure 3.
 
Cluster analysis. The 1257 genes were grouped into nine clusters. In each graph, the x-axis represents different time points, and the y-axis represents the normalized factor of induction or repression. Clusters 1 and 2 represent the up- or downregulated genes in the infected cornea of B6 and BALB/c mice. Clusters-3, -4, and -5 represent type-1, and clusters-7, -8, and -9 represent type-2 immune response genes.
Figure 3.
 
Cluster analysis. The 1257 genes were grouped into nine clusters. In each graph, the x-axis represents different time points, and the y-axis represents the normalized factor of induction or repression. Clusters 1 and 2 represent the up- or downregulated genes in the infected cornea of B6 and BALB/c mice. Clusters-3, -4, and -5 represent type-1, and clusters-7, -8, and -9 represent type-2 immune response genes.
Figure 4.
 
Degree of change of selected genes. Data are presented as multiples of change (fold) when comparing PI day 1 versus normal corneas in each group of mice. Each gene represents one of the nine clusters.
Figure 4.
 
Degree of change of selected genes. Data are presented as multiples of change (fold) when comparing PI day 1 versus normal corneas in each group of mice. Each gene represents one of the nine clusters.
Table 2.
 
Sequences of Primer Sets
Table 2.
 
Sequences of Primer Sets
Gene Name Accession No. Primer Sequence (5′–3′) Product Size (bp)
MyD88 X51397 AGCAGAACCAGGAGTCCGAGAAGC (Forward) 149
GGGGCAGTAGCAGATAAAGGCATCG (Reverse)
TIMP-3 U26437 ATGCCTTCTGCAACTCCGACATCG (Forward) 330
AACCCAGGTGGTAGCGGTAATTGAGG (Reverse)
CCR2 U56819 ACCTGCTCTTCCTGCTCACATTACC (Forward) 263
TCCTGGTAGAGAGGCAAACACAGC (Reverse)
CD14 X13333 AGCTAGACGAGGAAAGTTGTTCCTGC (Forward) 134
ACGCTTTAGAAGGTATTCCAGGCTGC (Reverse)
TGF-β-3 M32745 ATTCGACATGATCCAGGGACTGGC (Forward) 285
AAAGACAGCCATTCAGCGGTGC (Reverse)
Defensin-β-1 AF003525 TGGCATTCTCACAAGTCTTGGACG (Forward) 142
GCTCTTACAACAGTTGGGCTTATCTGG (Reverse)
IL-10 M37897 TGCTATGCTGCCTGCTCTTACTGACTGG (Forward) 309
AATGCTCCTGATTTCTGGGCCATGC (Reverse)
iNOS U43428 TTGATGTGCTGCCTCTGGTCTTGC (Forward) 121
AGCTCCTGGAACCACTCGTACTTG (Reverse)
Caspase-9 AB019600 TGCACTTCCTCTCAAGGCAGGACC (Forward) 206
TCCAAGGTCTCCATGTACCAGGAGC (Reverse)
β-Actin NM_007393 TGCGTGACATCAAAGAGAAGCTGTG (Forward) 144
ATCGGAACCGCTCGTTGCCAATAG (Reverse)
Table 3.
 
Changes of Gene Expression by Real-Time Quantitative RT-PCR using the Relative Standard Curve
Table 3.
 
Changes of Gene Expression by Real-Time Quantitative RT-PCR using the Relative Standard Curve
Gene Name PI Day 1 Cornea Normal Cornea Change (×)
CT Normalized CT Normalized
C57BL/6
 MyD88 28.8 108.0 31.5 42.000 2.6
 TIMP-3 29.2 6.3 28.2 24.500 −3.9
 CCR 2 35.1 9.7 40.2 0.880 11.0
 CD14 30.3 10.4 39.8 0.057 182.0
 TGF-β-3 39.6 0.16 39.4 0.390 −2.4
 Defensin-β-1 32.2 9.2 30.1 68.900 −7.5
 IL-10 35.6 13.0 39.1 3.250 4.0
 iNOS 25.2 87.0 29.1 17.400 5.0
 Caspase 9 36.7 0.18 33.5 2.900 −16.0
 β-Actin 30.5 1.00 31.6 1.000 1.0
BALB/c
 MyD88 27.8 474.00 28.3 286.000 1.7
 TIMP-3 28.5 23.00 25.5 127.000 −5.5
 CCR 2 33.1 78.00 34.7 24.000 3.2
 CD14 29.1 52.00 34.6 1.400 37.0
 TGF-β-3 37.2 1.65 33.5 12.800 −7.7
 Defensin-β-1 29.5 113.00 28.0 233.000 −2.1
 IL-10 34.0 82.00 37.0 10.900 7.5
 iNOS 24.6 296.00 27.8 35.600 8.3
 Caspase 9 39.5 0.07 41.2 0.020 3.5
 β-Actin 31.8 1.00 31.5 1.000 1.00
Figure 5.
 
Real-time RT-PCR. The overall transcriptional preferences recorded with this method corresponded well with the differences in the microarray. The band size amplified by real-time PCR agreed with the predicted size of the corresponding primer set. β-actin was the internal control.
Figure 5.
 
Real-time RT-PCR. The overall transcriptional preferences recorded with this method corresponded well with the differences in the microarray. The band size amplified by real-time PCR agreed with the predicted size of the corresponding primer set. β-actin was the internal control.
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Figure 1.
 
Scatterplot analysis. For each, the top and bottom boundaries represent a ± twofold change in gene expression. (A, B) Most of the blue dots (Present call) fell within the ± twofold limit. The expression of 1257 transcripts was changed greater than twofold in both mouse groups. A scatterplot analysis of these 1257 transcripts is shown in (CF). The transcripts shown in red dots (C, D) represent type-1 immune response genes that changed ±2- to 300-fold in B6 (C) mice, but changed less than 4-fold in BALB/c (D) mice. Green dots (E, F) represent type-2 immune response transcripts that were changed within the ± twofold limit in B6 (E) mice, but were upregulated more than twofold in BALB/c (F) mice.
Figure 1.
 
Scatterplot analysis. For each, the top and bottom boundaries represent a ± twofold change in gene expression. (A, B) Most of the blue dots (Present call) fell within the ± twofold limit. The expression of 1257 transcripts was changed greater than twofold in both mouse groups. A scatterplot analysis of these 1257 transcripts is shown in (CF). The transcripts shown in red dots (C, D) represent type-1 immune response genes that changed ±2- to 300-fold in B6 (C) mice, but changed less than 4-fold in BALB/c (D) mice. Green dots (E, F) represent type-2 immune response transcripts that were changed within the ± twofold limit in B6 (E) mice, but were upregulated more than twofold in BALB/c (F) mice.
Figure 2.
 
Frequency distribution. A histogram of 1257 selected transcripts revealed mouse strain–specific differences in corneal gene expression profiles. Distribution of average differences are shown for normal versus P. aeruginosa–infected corneas in B6 versus BALB/c mice.
Figure 2.
 
Frequency distribution. A histogram of 1257 selected transcripts revealed mouse strain–specific differences in corneal gene expression profiles. Distribution of average differences are shown for normal versus P. aeruginosa–infected corneas in B6 versus BALB/c mice.
Figure 3.
 
Cluster analysis. The 1257 genes were grouped into nine clusters. In each graph, the x-axis represents different time points, and the y-axis represents the normalized factor of induction or repression. Clusters 1 and 2 represent the up- or downregulated genes in the infected cornea of B6 and BALB/c mice. Clusters-3, -4, and -5 represent type-1, and clusters-7, -8, and -9 represent type-2 immune response genes.
Figure 3.
 
Cluster analysis. The 1257 genes were grouped into nine clusters. In each graph, the x-axis represents different time points, and the y-axis represents the normalized factor of induction or repression. Clusters 1 and 2 represent the up- or downregulated genes in the infected cornea of B6 and BALB/c mice. Clusters-3, -4, and -5 represent type-1, and clusters-7, -8, and -9 represent type-2 immune response genes.
Figure 4.
 
Degree of change of selected genes. Data are presented as multiples of change (fold) when comparing PI day 1 versus normal corneas in each group of mice. Each gene represents one of the nine clusters.
Figure 4.
 
Degree of change of selected genes. Data are presented as multiples of change (fold) when comparing PI day 1 versus normal corneas in each group of mice. Each gene represents one of the nine clusters.
Figure 5.
 
Real-time RT-PCR. The overall transcriptional preferences recorded with this method corresponded well with the differences in the microarray. The band size amplified by real-time PCR agreed with the predicted size of the corresponding primer set. β-actin was the internal control.
Figure 5.
 
Real-time RT-PCR. The overall transcriptional preferences recorded with this method corresponded well with the differences in the microarray. The band size amplified by real-time PCR agreed with the predicted size of the corresponding primer set. β-actin was the internal control.
Table 1.
 
RNA Expression of Cellular Genes Up- or Downregulated Fourfold or More in Both Mouse Strains
Table 1.
 
RNA Expression of Cellular Genes Up- or Downregulated Fourfold or More in Both Mouse Strains
Gene Name Accession No.
Cytokines
 IL-1α M14639
 IL-1β M15131
 IL-6 X54542
 IL-12 M86672
 IFN-β V00755
 CSF-3 M13926
 GM-CSF X03020
 TNF-α D84196
 IL-4 AA967539
 IL-10 M37897
 IL-18 D49949
Chemokines/inflammatory factors
 MIP-1α (CCL3, Scya3) J04491
 MIP-1β (CCL4, Scya4) X62502
 MIP-1γ (CCL9, Scya9) U49513
 RANTES (CCL5, Scya5) AF065947
 MIP-2 (CXCL1-3, Scyb2) X53798
 MCP-1 (CCL2, Scya2) M19681
 CCR1 U29678
 CCR2 U56819
 CCR4 U15208
 CCR5 AV370035
 KC J04596
 MRP-14 M83219
 TARC (CCL17, Scya17) AJ242583
 Eotaxin (CCL11, Scya11) U77462
Apoptosis/cell cycle
 Cytochrome c X15963
 Apoptosis inhibitor 1 U88908
 Caspase 11 Y13089
TLRs/signal transduction
 CD14 X13333
 sIL-1Ra L32838
 TLR-6 AB020808
 IKKi AB016589
 NIK U88984
 Fas antigen M83649
 TNF Receptor 1 X87128
 TRAF-1 L35302
Extracellular Matrix/MMPs
 MMP9 X72795
 MMP-10 Y13185
 MMP-13 X66473
 TIMP-1 V00755
 Casein Kappa M10114
 PAI-1 M33960
 PAI-2 X16490
 Tenascin C X56304
Other molecules
 Laminin-β-3 U43298
 Laminin-γ-2 U43327
 Integrin-β-2 M31039
 L-Selectin M36058
 P-Selectin M72332
 ICAM-1 (CD54) M90551
 Defensin-β-1 AF003525
Table 2.
 
Sequences of Primer Sets
Table 2.
 
Sequences of Primer Sets
Gene Name Accession No. Primer Sequence (5′–3′) Product Size (bp)
MyD88 X51397 AGCAGAACCAGGAGTCCGAGAAGC (Forward) 149
GGGGCAGTAGCAGATAAAGGCATCG (Reverse)
TIMP-3 U26437 ATGCCTTCTGCAACTCCGACATCG (Forward) 330
AACCCAGGTGGTAGCGGTAATTGAGG (Reverse)
CCR2 U56819 ACCTGCTCTTCCTGCTCACATTACC (Forward) 263
TCCTGGTAGAGAGGCAAACACAGC (Reverse)
CD14 X13333 AGCTAGACGAGGAAAGTTGTTCCTGC (Forward) 134
ACGCTTTAGAAGGTATTCCAGGCTGC (Reverse)
TGF-β-3 M32745 ATTCGACATGATCCAGGGACTGGC (Forward) 285
AAAGACAGCCATTCAGCGGTGC (Reverse)
Defensin-β-1 AF003525 TGGCATTCTCACAAGTCTTGGACG (Forward) 142
GCTCTTACAACAGTTGGGCTTATCTGG (Reverse)
IL-10 M37897 TGCTATGCTGCCTGCTCTTACTGACTGG (Forward) 309
AATGCTCCTGATTTCTGGGCCATGC (Reverse)
iNOS U43428 TTGATGTGCTGCCTCTGGTCTTGC (Forward) 121
AGCTCCTGGAACCACTCGTACTTG (Reverse)
Caspase-9 AB019600 TGCACTTCCTCTCAAGGCAGGACC (Forward) 206
TCCAAGGTCTCCATGTACCAGGAGC (Reverse)
β-Actin NM_007393 TGCGTGACATCAAAGAGAAGCTGTG (Forward) 144
ATCGGAACCGCTCGTTGCCAATAG (Reverse)
Table 3.
 
Changes of Gene Expression by Real-Time Quantitative RT-PCR using the Relative Standard Curve
Table 3.
 
Changes of Gene Expression by Real-Time Quantitative RT-PCR using the Relative Standard Curve
Gene Name PI Day 1 Cornea Normal Cornea Change (×)
CT Normalized CT Normalized
C57BL/6
 MyD88 28.8 108.0 31.5 42.000 2.6
 TIMP-3 29.2 6.3 28.2 24.500 −3.9
 CCR 2 35.1 9.7 40.2 0.880 11.0
 CD14 30.3 10.4 39.8 0.057 182.0
 TGF-β-3 39.6 0.16 39.4 0.390 −2.4
 Defensin-β-1 32.2 9.2 30.1 68.900 −7.5
 IL-10 35.6 13.0 39.1 3.250 4.0
 iNOS 25.2 87.0 29.1 17.400 5.0
 Caspase 9 36.7 0.18 33.5 2.900 −16.0
 β-Actin 30.5 1.00 31.6 1.000 1.0
BALB/c
 MyD88 27.8 474.00 28.3 286.000 1.7
 TIMP-3 28.5 23.00 25.5 127.000 −5.5
 CCR 2 33.1 78.00 34.7 24.000 3.2
 CD14 29.1 52.00 34.6 1.400 37.0
 TGF-β-3 37.2 1.65 33.5 12.800 −7.7
 Defensin-β-1 29.5 113.00 28.0 233.000 −2.1
 IL-10 34.0 82.00 37.0 10.900 7.5
 iNOS 24.6 296.00 27.8 35.600 8.3
 Caspase 9 39.5 0.07 41.2 0.020 3.5
 β-Actin 31.8 1.00 31.5 1.000 1.00
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