Environmentally Responsive Gene Expression during Invasive Infection

We used a nanoString n-counter [10] to quantify selected C. albicans gene transcripts in whole kidney lysates at 12, 24, and 48 hr postinfection. We first tested our methodology using a probe set that contained 248 environmentally responsive genes chosen from previously described genome-wide datasets (S1 Data). Expression levels of these genes are known to respond to a range of signals, such as nutrient limitation and cell morphogenesis. Also, many of the response pathways that govern their expression are known to impact virulence, as inferred from mutant analysis and comparisons to other pathogens [14]. NanoString probe background signals were assessed by comparing uninfected and infected tissue samples. Signal-to-noise ratios for the majority of probes in all samples fell into the range of 10¹ to 10⁶ (S1 Fig.). Agreement among independent samples was excellent (S1 Fig.; R² values of 0.94 to >0.99), and induction ratios were confirmed by quantitative reverse transcription polymerase chain reaction (QRT-PCR) for all eight genes examined (S2 Fig.). Probe signals from infected tissue samples varied over a range of three orders of magnitude, and comparison with pure C. albicans cultures indicated that pathogen RNA makes up 0.2% or less of the total RNA in the infected kidney over the 48 hr time frame examined. These results show that this approach can reliably detect C. albicans RNAs of low abundance in invasive infection samples.

To assess changes in gene expression during infection, we compared the infected tissue samples to each other and to inoculum samples (stationary phase in yeast extract peptone dextrose medium [YPD]). The results revealed that C. albicans undergoes both early and late infection responses (Fig. 1; S1 Data). The early gene expression response comprises genes with RNA levels significantly different from the inoculum at 12 hr postinfection (p < 0.05 and ≥2-fold change in expression; Fig. 1). We found 65 up-regulated early genes and 74 down-regulated early genes. The late gene expression response comprises genes with RNA levels that are significantly different from the 12 hr time point at 48 hr postinfection (Fig. 1; S1 Data). We found 79 up-regulated late genes and 13 down-regulated late genes. These results indicate that C. albicans gene expression is dynamically regulated during invasive infection of a mammalian host.

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Fig 1. Expression of C. albicans environmentally responsive genes during invasive infection.

Changes in expression levels during mouse kidney invasion for 248 C. albicans genes (S1 Data) are presented in a heat map format. Mean values of biological triplicates are shown for up-regulation (yellow) and down-regulation (blue) of genes at 12, 24, and 48 hr postinfection relative to mean inoculum levels (0 hr). Color saturation represents the extent of the expression change, with full saturation at 10-fold up- or down-regulation. (All heat maps in this article follow the same color scale.) Portions of the heat map are expanded to illustrate representative early up-regulated genes (top), late genes (middle), and early down-regulated genes (bottom). In these portions, individual samples are presented separately to illustrate reproducibility. We define early expression changes as significant differences between the inoculum and 12 hr sample. We define late expression changes as significant differences between the 12 and 48 hr samples. Significance refers to changes of ≥2-fold and a p-value < 0.05. The data for each sample were normalized to RNA levels from control gene TDH3 before mean values were calculated. Our assignment criteria allow some dynamically regulated genes to fall into both the early and late expression classes. All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g001

To interpret the early and late gene expression changes, we compared our data to 166 published gene expression studies (S2 Data). We identified significant correlations among genes with expression changes of 2-fold or greater between datasets, as assessed with Fisher's exact test (FET). The most significant correlations were with genes up-regulated during invasive infection (Fig. 2A). Early up-regulated genes correlated well with genes expressed during the yeast-to-hyphal transition, including growth at 37° in Roswell Park Memorial Institute medium 1640 (RPMI), Lee's medium, Spider medium, or serum, all compared to growth at 30° in YPD. This correlation extended to the profiles of mutants defective in regulation of hyphal formation. For example, early up-regulated genes correlated with those up-regulated in a hyperfilamentous tup1Δ/Δ mutant (“tup1/wild type [WT]” dataset in Fig. 2A) and with those down-regulated in a nonfilamentous tec1Δ/Δ mutant (“WT/tec1” dataset in Fig. 2A). Early up-regulated genes also correlated well with genes that are repressed by iron acquisition gene repressor Sfu1 (“sfu1/WT” dataset) or activated by zinc acquisition gene activator Zap1/Csr1 (“WT/zap1” dataset). There was also a strong correlation with genes that depend upon transcription factor Rim101 for expression ("WT/rim101" dataset). Although Rim101-dependent genes overlap with yeast-to-hyphal genes, the correlation was significant with Rim101-dependent genes from an nrg1Δ/Δ background, in which hyphal gene expression changes were minimized [15]. There was also significant similarity to genes that were up-regulated mid-way through a zebrafish infection (“8 hr postinfection [PI]” dataset [16]), a point we return to in the Discussion. These correlations suggest the hypothesis that hyphal formation, iron limitation, zinc limitation, and Rim101 activation may be driving forces that govern early gene expression responses. We tested several of those inferences through the profiling of mutant strains presented below.

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Fig 2. Comparison of gene expression profiles during invasive infection to other profiling datasets.

Dataset comparisons were carried out with one sided Fisher's Exact Test (FET), using query gene sets from in vivo profiling data (applying a cutoff at 2-fold expression changes), and the entire set of nanoString probes (i.e., 248 environmental response genes) as a background set. Queries were matched to a database we assembled from 166 published expression datasets (S2 Data). (A). Representative matches to early up-regulated genes are shown with their respective p-values (top; higher bars indicate smaller p-values from FET, hence greater similarity between the two datasets), and in a heat map (bottom; same color scale as in Fig. 1; grey color indicates that no significant expression difference was reported and expression ratios were not readily available). (B). Representative matches to late up-regulated genes are shown with their respective p-values and in a heat map. Comprehensive FET results are available in S2 Data. Early and late gene sets are as defined in the Fig. 1 legend. All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g002

The late up-regulated genes correlated significantly with a distinct spectrum of gene expression responses (Fig. 2B). The late up-regulated genes correlated with genes up-regulated at late times during zebrafish infection. In addition, they correlated with genes induced by growth on acetate as carbon source, compared to glucose. The acetate response was assayed by Lorenz et al. [17] to model the metabolic state induced after phagocytosis by macrophages, and indeed the late infection response we describe here correlates with the response to co-culture with macrophages and to peroxide stress (Fig. 2B). Interestingly, we note that the zinc- and iron-limitation responses that were prominent early in infection begin to subside. This point is illustrated by the fact that early genes correlated with the "WT/zap1" dataset, while late genes correlate with its inverse, designated "zap1/WT." This reversal of iron and zinc responses may reflect a release of nutrients from damaged tissue, a phenomenon documented by Potrykus et al. with respect to iron levels [18]. These results suggest that the early and late infection environments are substantially different. The late gene expression responses may be driven by tissue damage and an inflammatory cell influx.

Two phases of host gene expression were also discernible (Fig. 3A; S3 Data). RNA levels for 46 mouse genes associated with fungal infection responses were assayed in each kidney sample. One set of genes was induced early in infection, reaching peak levels by 24 hr postinfection. This set included genes for pro-inflammatory cytokines (such as Tnfα and IL-6) and pattern recognition receptors (such as Mincle and Dectin-2), in keeping with the study by MacCallum at 24 hr postinfection [9]. These rapid responses are likely to be mediated by cells that are resident in the kidney. A second set of genes was induced late in infection, with significant expression increases between 24 and 48 hr (Fig. 3A). This second set included the neutrophil marker NOX (NADPH oxidase 1) and the inflammatory macrophage marker CCR2. The set also included genes whose expression is associated with T cells, such as interferon γ and IL-17a. These late responses probably reflect leukocyte migration into the kidney. Thus, both pathogen and host display distinct early and late temporal gene expression responses during invasive infection.

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Fig 3. Mouse immune response gene expression during Candida infection.

(A). RNA levels for 46 mouse immune response genes were assayed at 12, 24, and 48 hr postinfection in biological triplicates and were compared to uninfected kidney controls (S3 Data). Mouse genes that represent early and late expression classes are presented. We define early genes as those with significantly higher expression in 12 hr postinfection samples compared to uninfected samples. We define late genes as those with no significant difference in expression between uninfected and 12 hr postinfection samples, but with significantly higher expression in 48 hr postinfection samples compared to 24 hr postinfection samples. An asterisk indicates significant differences (p-value < 0.05) between successive time-course samples (uninfected samples versus 12 hr, 24 hr versus 12 hr, 48 hr versus 24 hr). (B). Mice were infected with the indicated transcription factor mutants and respective complemented strains; RNA levels for 46 mouse immune response genes were determined by nanoString at 24h postinfection (S3 Data). Induction of four early genes (the same genes shown in panel A for the wild-type strain) are shown here. All four genes were induced significantly more strongly by complemented strains than the respective mutants (p-value < 0.05), as indicated by the bracket and asterisk. P-values for expression of each gene are included in S3 Data. All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g003

Regulation of C. albicans Transcription during Infection

In order to identify regulators that govern gene expression during infection, we employed a probe set that included all 231 known or predicted C. albicans transcription factor genes. Early and late gene expression phases were evident among these genes (Fig. 4, S4 Data). Early up-regulated transcription factor genes included regulators of hyphal formation (Ume6, Tec1), zinc acquisition (Zap1), and iron acquisition (Hap43, Sef1). The late response included many transcription factors whose function has not been analyzed in detail previously. Functions of the early class of transcription factor genes that are known are consistent with the properties of environmentally responsive genes presented above.

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Fig 4. Expression of C. albicans transcription factor genes during invasive infection.

Changes in expression levels during mouse kidney invasion for 231 C. albicans transcription factor genes (S4 Data) are presented in a heat map format. Mean values of biological triplicates are shown for up-regulation (yellow) and down-regulation (blue) of genes at 12, 24, and 48 hr postinfection relative to mean inoculum levels (0 hr). Portions of the heat map are expanded to illustrate representative early up-regulated genes (top), late genes (middle), and early down-regulated genes (bottom). In these portions, individual samples are presented separately to indicate reproducibility. The early and late gene classes were defined as described in the Fig. 1 legend. All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g004

Transcription factors that are expressed at high levels or significantly up-regulated during infection may govern pathogen proliferation and virulence. An analysis of previously studied transcription factors supports this idea (Table 1). Expression level was an excellent predictor of function. Specifically, among the 30 most highly expressed transcription factor genes at 48 hr postinfection, 18 have been found to be required for proliferation or virulence among 24 tested previously (75%). Among the 30 most weakly expressed transcription factor genes at this time point, one is required for proliferation or virulence among 12 tested previously (8%). This difference is highly significant (p < 0.001 by FET). The up- or down-regulation of expression was a weaker predictor of function. Among the 30 most highly up-regulated transcription factor genes (comparing 48 hr postinfection to the inoculum samples), 15 are required for proliferation or virulence among 20 tested previously (75%). Among the 30 most down-regulated transcription factor genes, seven are required for proliferation or virulence among 16 tested previously (44%). There is no significant difference between these expression classes (p > 0.08 by FET). Therefore, the transcription factor gene expression level, rather than its regulatory response, is the most discerning predictor of function in this infection model. However, transcription factors that are most highly up-regulated or most highly expressed are equally likely to have clear roles in proliferation and virulence (p = 1.0 by FET).

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Table 1. Transcription factor gene expression and function in vivo.

https://doi.org/10.1371/journal.pbio.1002076.t001

Profile of the Attenuated rim101Δ/Δ Transcription Factor Mutant

We sought to understand regulatory relationships during infection, and we began this analysis with the transcription factor Rim101. We chose RIM101 because it is the most highly expressed transcription factor gene at 48 hr postinfection; its RNA levels comprise over 3% of the total RNA levels for all 231 transcription factor genes (S4 Data). Rim101 clearly functions during infection: early up-regulated genes correlate with known Rim101-dependent genes, and prior studies show that it is required for virulence in the mouse disseminated infection model, where it promotes both hyphal formation and proliferation [19]. In order to define the gene expression basis for the rim101Δ/Δ mutant virulence defect, we assayed expression of 148 environmentally responsive genes by the rim101Δ/Δ mutant and rim101Δ/Δ+pRIM101 complemented strain at 24 hr postinfection (S5 Data). The rim101Δ/Δ mutant presented significantly reduced accumulation of 33 RNAs (≥2-fold change and p < 0.05, including RIM101 itself), and increased expression of 14 RNAs, compared to the wild-type strain (S5 Data). RNA accumulation in the complemented strain closely mirrored that of the wild type (Fig. 5A). Specifically, RNA accumulation levels were restored by the complementing RIM101 allele for all genes except GAP2, PHR1, and HYR1 (p < 0.05). In the complemented strain, expression of GAP2 and PHR1 trended toward the wild-type level, thus suggesting that these few gene expression differences from the wild type are the result of reduced gene dosage of RIM101 alleles in the complemented strain compared to the wild type, as documented previously for these strains [19].

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Fig 5. Rim101-dependent gene regulation during invasive infection.

RNA levels for 148 C. albicans environmental response genes were determined by nanoString at 24 hr postinfection for the rim101 mutant and complemented strains (S5 Data). (A). Expression ratios are plotted for each gene in rim101Δ/Δ versus wild type (X axis) and rim101Δ/Δ versus complemented strain (Y axis). Three genes (red data points) have significantly different expression ratios in the two comparisons. (B). Expression ratios are presented for all genes significantly down-regulated in the rim101Δ/Δ strain relative to the complemented strain during kidney infection (blue bars; ≥2-fold change and p-value < 0.05). The expression ratios for the same genes in the same strains during intra-abdominal candidiasis (IAC) infection (red bars; reported in [12]) or during in vitro growth in Spider medium (green bars) are displayed. Complete data are in S5 Data. (C). Expression ratios are presented for all genes significantly up-regulated in the rim101Δ/Δ strain relative to the complemented strain during kidney infection (blue bars; ≥2-fold change and p-value < 0.05), during abdominal infection (red bars; reported in [12]) or during in vitro growth in Spider medium (green bars) are displayed. Complete data are in S5 Data. All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g005

Genes that were down-regulated in the rim101Δ/Δ mutant included many that are induced during hyphal growth (such as HWP1, ECE1, SOD5, and SAP5). Their reduced expression in the mutant is consistent with its deficiency in hyphal growth in vivo [19]. Two transcription factor genes that are required for hyphal growth, EFG1 and TEC1, were also down-regulated in the mutant and may account for its failure to form hyphae and express hyphal genes. We note that the few direct Rim101 target genes assayed have altered RNA levels in the mutant, including Rim101-activated genes PHR1 and PRA1, as well as Rim101-repressed gene PHR2 [20,21]. Overall, these in vivo profiling data are consistent with known features of the rim101Δ/Δ pathogenicity defect and the molecular data about the Rim101 protein.

The gene expression impact of the rim101Δ/Δ mutation has been characterized extensively in vitro, and we noted several differences between published in vitro profiles and our in vivo data. Such differences can arise from the assay platform, and we thus examined the rim101Δ/Δ mutant and rim101Δ/Δ+pRIM101 complemented strain under hypha-inducing conditions in vitro (Spider medium at 37°C) with the nanoString platform. We examined expression of 144 environmentally responsive genes (S5 Data). In this set, 37 genes responded significantly (≥2-fold change, p < 0.05) to Rim101 in vivo, 11 genes responded significantly to Rim101 in vitro, and only eight responded significantly to Rim101 under both conditions (compare blue and green bars in Fig. 5B and Fig. 5C). The genes that responded significantly to Rim101 only in vivo include several with roles in pathogenicity (EFG1, HWP1, SAP6, and TEC1). These results emphasize that the invasive infection environment can alter the spectrum of genes that respond to a transcription factor, and they suggest that such alterations have the potential to influence virulence.

We have recently analyzed the rim101Δ/Δ mutant and rim101Δ/Δ+pRIM101 complemented strain on the nanoString platform during abdominal infection [12]. Among 144 genes assayed in both infection models, we found 46 genes that responded significantly to Rim101 during abdominal infection, and only 15 responded similarly in both infection models (Fig. 5B and Fig. 5C, blue and red bars). Notably, the hyphal regulatory genes EFG1 and TEC1 did not require Rim101 for expression during abdominal infection. Therefore, the site of infection can affect the relationship between a transcription factor and its target genes.

Profile of the Attenuated efg1Δ/Δ Transcription Factor Mutant

We also examined the gene expression impact of a defect in the transcription factor Efg1. An efg1Δ/Δ mutant was among the first attenuated C. albicans strains characterized [22,23]. Efg1 governs numerous pathogenicity-related phenotypes, including adherence to diverse cells and substrates, formation of hyphae at 37°C, colonization of the gastrointestinal tract, and antifungal drug susceptibility [14,24]. The impact of efg1Δ/Δ mutations has been assessed on the C. albicans transcriptome during growth in vitro [25], but never in vivo.

We confirmed that the proliferation of an efg1Δ/Δ mutant was severely defective in the mouse disseminated infection model, yielding overall RNA levels too low to detect expression of many C. albicans genes at 24 hr postinfection (Fig. 6B) and a greatly diminished host response (Fig. 3B). Therefore, we assayed C. albicans gene expression in an efg1Δ/Δ mutant and efg1Δ/Δ+pEFG1 complemented strain at 48 hr postinfection. Among 148 environmentally responsive genes assayed, we found 26 genes that were significantly down-regulated (including EFG1 itself) and 63 genes that were significantly up-regulated (≥2-fold and p < 0.05), compared to the wild-type strain (Fig. 6A; S5 Data). Presence of one EFG1 allele in the complemented strain increased EFG1 expression to 26% of the wild-type level, and permitted partial or complete restoration of environmentally responsive gene expression levels (S3 Fig.; S5 Data). This dosage effect is consistent with prior studies that document haploinsufficiency for EFG1 [26]. The 26 down-regulated genes included many hyphal genes (for example, ALS3, ECE1, HWP1, IHD1, and SOD5), as expected from profiling of an in vitro–grown efg1Δ/Δ mutant [25]. Among the 63 up-regulated genes, only four were up-regulated in the in vitro efg1Δ/Δ characterization [25], and their fold-change was much greater in vivo than in vitro. These results indicate that the efg1Δ/Δ mutant is defective in expression of hyphal genes during proliferation in vivo, as expected from its previous phenotypic characterization. In addition, the efg1Δ/Δ mutant, like the rim101Δ/Δ mutant, has impact on gene expression during proliferation in vivo that is distinct from what is observed in vitro.

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Fig 6. Comparison of C. albicans proliferation-defective transcription factor mutants.

(A). Heat map representation of gene expression ratios for 148 environmentally responsive genes at 24 hr postinfection (rim101Δ/Δ, rob1Δ/Δ, rpn4Δ/Δ, sut1Δ/Δ, or zap1Δ/Δ, each relative to the wild type) or 48 hr postinfection (efg1Δ/Δ, relative to the wild type). Complete data are in S5 Data. (B). Yield of C. albicans RNA relative to total kidney RNA for each mutant and complemented strain at 24 hr postinfection. Asterisks mark significant differences (p-value < 0.05) between mutants and respective complemented strains. (C). Histopathology images of kidney sections. All samples were at 24 hr postinfection. Arrows indicate fungal cells. The scale bar corresponds to 50 microns. For panel A, the specific strains used and the dose of viable cells per mouse, as determined by plating the inocula, were: CW 696 (wild type) 8.4 × 10⁵; CW730 (rob1) 10.2 × 10⁵; DAY25 (rim101) 11.1 × 10⁵; CW1018 (efg1) 9 × 10⁵; CW792 (rpn4) 7.9 × 10⁵; CW756 (zap1) 8.8 × 10⁵; CW704 (sut1) 8.6 × 10⁵. All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g006

Functional Relationships among Virulence Regulators

In order to visualize gene expression alterations associated with virulence defects in a broader context, we profiled a panel of mutants that are defective in proliferation in vivo. We selected a zap1Δ/Δ mutant because many known Zap1-dependent genes are up-regulated during infection, as discussed above. The zap1Δ/Δ mutant has been shown to be defective in proliferation previously [27], a phenotype that was confirmed by its reduced RNA levels in infected tissue (Fig. 6B) and a diminished host response (Fig. 3B). We also made mutations in three highly expressed or up-regulated transcription factor genes that had not been studied previously in a disseminated infection model: ROB1 (orf19.4998), RPN4 (orf19.1069), and SUT1 (orf19.4342). Each mutation caused reduced proliferation in vivo, as indicated by a decreased fungal RNA yield (Fig. 6B) and host response (Fig. 3B). Histopathology examination of kidney sections at 24 hr postinfection showed that the attenuated mutants were able to invade the kidney parenchyma, though fungal cells were much less abundant than observed in mice infected with the wild-type strain (Fig. 6C). Complementation with a wild-type copy of each gene led to increased fungal RNA yield and host response gene expression (Fig. 6B and Fig. 3B). Therefore, defects in ROB1, RPN4, and SUT1 cause defects in proliferation in vivo.

In order to identify the virulence pathways that are governed by these transcription factors, we assayed expression of 148 C. albicans environmentally responsive genes at 24 hr postinfection in the wild type and the attenuated transcription factor mutant strains (Fig. 6A; S5 Data), and compared the results to the rim101Δ/Δ and efg1Δ/Δ data presented above. Gene expression alterations in each mutant were largely restored in the respective complemented strains (S3 Fig.; S5 Data), thus indicating that the mutation introduced into each strain was the cause of the gene expression alteration. Only three genes had significantly altered expression (≥2-fold, p < 0.05) in all mutants, the up-regulated cell wall or secreted protein genes GCA2, RBR1, and RBR2. Although no firm conclusion can be drawn from so few genes, the results suggest that attenuated mutants may undergo a common cell surface alteration. Prior and current studies indicate that Rim101, Rob1, and Efg1 are required for hyphal formation, so we looked for common gene expression alterations in those three strains. Common down-regulated genes included CRH11, ECE1, FAV2, HWP1, IFD6, SAP5, and SAP6, most of which are associated with hyphal morphogenesis. Therefore, this feature of the in vivo gene expression profile is consistent with prior in vitro analysis of the hyphal morphogenesis program and the mutant phenotypes observed in vivo.

Three of the mutants, rpn4Δ/Δ, sut1Δ/Δ, and zap1Δ/Δ, had extremely similar gene expression profiles (Fig. 6A top right; S5 Data). These results were unexpected because these three C. albicans mutants have distinct phenotypes in vitro. For example, on low-zinc medium, the zap1Δ/Δ mutant fails to grow, while the rpn4Δ/Δ and sut1Δ/Δ mutants grow well (S4 Fig.). Also, during growth in vitro, the gene expression alterations of the three mutants have little similarity (S4 Fig.). We considered two explanations for our observations. One possibility is that Rpn4, Sut1, and Zap1 act in a pathway that operates in the invasive infection environment to govern proliferation; in vitro growth conditions may alter their spectra of target genes. A second possibility is that Rpn4, Sut1, and Zap1 are functionally unrelated, and the in vivo gene expression profiles of the three mutants represent general proliferation-defective responses.

We focused on Sut1 and Zap1 to determine whether there may be a functional relationship in vivo. We chose those two transcription factors because, during proliferation in vivo, the sut1Δ/Δ mutant had reduced RNA levels for ZAP1 and the Zap1-dependent genes PRA1, ZRT1, and ZRT2 (Fig. 7A). Therefore, we considered the specific hypothesis that the sut1Δ/Δ phenotypic defect during infection arises from its ZAP1 expression defect. This model predicts that overexpression of ZAP1 will suppress the defects of a sut1Δ/Δ mutant. We created a ZAP1 overexpression allele by fusing the ZAP1 coding region to a strong TDH3 promoter in a sut1Δ/Δ strain. The sut1Δ/Δ TDH3-ZAP1 strain expressed zinc acquisition genes PRA1, ZRT1, and ZRT2 during infection at higher levels than the sut1Δ/Δ mutant (Fig. 7A). In order to assess the biological significance of the Sut1-Zap1 relationship, we made use of the finding that the sut1Δ/Δ mutant is defective in virulence (Fig. 7B). Specifically, mice infected with the sut1Δ/Δ mutant survived substantially longer than those infected with the wild type or complemented strain. The sut1Δ/Δ TDH3-ZAP1 strain displayed much greater virulence than the sut1Δ/Δ mutant, causing host lethality with nearly wild-type kinetics (Fig. 7B). We considered the possibility that ZAP1 overexpression may cause a nonspecific increase in virulence. However, an otherwise wild-type strain that carried the TDH3-ZAP1 allele had reduced virulence rather than increased virulence (Fig. 7B). These findings are consistent with the model that Sut1 is necessary for pathogenicity because it is required for ZAP1 expression during infection. To test the specific hypothesis that the sut1Δ/Δ mutant is attenuated because it is defective in zinc uptake, we assayed virulence of strains that express the zinc transporter gene ZRT2 from the TDH3 promoter. The TDH3-ZRT2 allele did not alter virulence in a wild-type background, but fully restored virulence of a sut1Δ/Δ mutant (Fig. 7C). This finding proves that the zinc transporter expression defect of the sut1Δ/Δ mutant is the cause of its virulence defect. Therefore, Sut1 is required for zinc acquisition in the invasive infection environment.

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Fig 7. Effect of ZAP1 and ZRT2 overexpression in a sut1Δ/Δ mutant.

(A). Expression of zinc acquisition genes ZAP1, PRA1, ZRT1, and ZRT2 was measured by nanoString at 24 hr postinfection in the wild type, the zap1Δ/Δ mutant, the sut1Δ/Δ mutant, and the sut1Δ/Δ mutant that overexpresses ZAP1. The mean of triplicate determinations is shown. (B). Mouse survival was determined after inoculation with the wild-type strain, the sut1Δ/Δ mutant, the sut1Δ/Δ+pSUT1 complemented strain, the wild-type strain that overexpresses ZAP1, and the sut1Δ/Δ mutant that overexpresses ZAP1. Mouse survival was significantly better after infection with the sut1Δ/Δ mutant than after infection with the wild-type strain, the sut1Δ/Δ+pSUT1 complemented strain, or the sut1Δ/Δ mutant that overexpresses ZAP1 (p < 0.05 by the log-rank test). (C). Mouse survival was determined after inoculation with the wild-type strain that overexpresses ZRT2 and the sut1Δ/Δ mutant that overexpresses ZRT2. Survival data after inoculation with the wild-type strain and the sut1Δ/Δ mutant from panel B are also included for comparison; all infections shown in panels B and C were carried out in parallel. The specific strains used and the dose of viable cells per mouse, as determined by plating the inocula, were: CW696 (wild type) 4.4 × 10⁵; CW704 (sut1) 4.9 × 10⁵; CW1035 (SUT1 complement) 3.2 × 10⁵; WX134 (WT ZAP1-OE) 3.35 × 10⁵; WX102 (sut1 ZAP1-OE) 3.65 × 10⁵; WX144 (sut1 ZRT2-OE) 4.85 × 10⁵; WX137 (WT ZRT2-OE) 3.75 × 10⁵. Complete genotypes are given in S1 Table. All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g007

In Vivo Response to Antifungal Therapy

Caspofungin, a cell wall inhibitor, is an extremely effective antifungal drug [6]. The gene expression response to caspofungin treatment has been assayed during growth in vitro [28–30], but not under infection conditions. We reasoned that the response to caspofungin may be different in vivo. Therefore, we assayed expression of the 248 environmentally responsive genes 2 hr after caspofungin administration in mice that had already been infected for 24 hr. There was no detectable decline in fungal cell number after this brief treatment time, but an extensive gene expression response was manifested (S6 Data). A much broader set of genes was induced by caspofungin in vivo than had been detected through previous in vitro studies (Fig. 8A). Specifically, 44 of the genes assayed showed significantly increased RNA accumulation (≥2-fold, p < 0.05) after caspofungin treatment in vivo compared to untreated infection samples. Several induced genes specify cell wall or secreted proteins (ALS4, ALS9, GCA2, PGA13, PGA26, PGA31, PGA37, PHR2, PIR1, RBR2, SAP1, and SAP9), and others specify enzymes that function in glucose generation (PCK1, GPM2, and DAK2). Thus, the in vivo response suggests that the cell wall is restructured in response to the drug through an alteration of cell wall protein composition and an increase in β-glucan synthesis.

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Fig 8. Gene expression response to caspofungin treatment during infection.

(A). Changes in expression levels for 248 C. albicans environmentally responsive genes are presented for caspofungin treated versus untreated cells at 24 hr postinfection (“Kidney,” S6 Data), in vitro in YPD at 30°C (“YPD,” from [30]), and in vitro in RPMI at 37°C (“RPMI,” S6 Data). These environmentally responsive genes are the same ones for which expression was measured during the time-course of infection depicted in Fig. 1. For comparison, the expression ratios of the same genes at 12 hr postinfection relative to the inoculum are shown (“12 hr/0 hr,” from Fig. 1). The data are presented in heat map format. Regions “1” and “2” are expanded on the right to make gene names legible. (B). Expression levels for 231 C. albicans transcription factor genes were measured for caspofungin treated versus untreated cells at 24 hr postinfection (“In vivo caspo-induced,” S6 Data) and in vitro in RPMI at 37°C (“In vitro caspo-induced,” S6 Data). Significantly up-regulated transcription factor genes are listed (≥2-fold change and p < 0.05). For comparison, the significantly down-regulated transcription factor genes at 12 hr postinfection are listed (“Early down-regulated,” S6 Data). All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g008

The gene expression response to caspofungin during infection showed little similarity to the previously characterized response during growth in vitro (Fig. 8A). Among significantly regulated genes from one previous in vitro study [30], 32 could be detected by our nanoString probes, and only four were regulated in parallel (up-regulated: PHR2, RTA4, SAP9; down-regulated: FGR41). Previous studies had been carried out with yeast-form cells and with a microarray platform, both of which may contribute to the divergence of results. Therefore, we assayed the gene expression response to caspofungin during hyphal growth conditions (RPMI, 37°C) with the same 248 nanoString probes used for in vivo profiling. We found a broad response to caspofungin under these in vitro conditions, with 19 down-regulated genes and 32 up-regulated genes (Fig. 8A; S6 Data). The up-regulated genes included numerous cell wall or secreted protein genes (ALS1, CHT2, CRH11, DFG5, ECM331, KRE1, PGA17, PGA26, PHR1, RBR1, and SOD4) as well as glucose generation genes (FBP1, ICL1, and PCK1). Surprisingly, though, there was still limited similarity to the in vivo response to caspofungin: seven genes were regulated in parallel (up-regulated: PGA26, PCK1, MET3, and YHB1; down-regulated: ATO2, CIP1, and LAP3). These results indicate that the infection environment has considerable impact on the gene expression response to caspofungin antifungal treatment.

In order to determine whether any previously characterized gene expression responses resemble the in vivo caspofungin response, we compared in vivo caspofungin-responsive genes to our database of gene expression data. We found striking overlap between caspofungin up-regulated genes and those genes that are repressed at 12 hr postinfection (Fig. 8A; p < 0.0001 by FET). Specifically, among 44 genes that were up-regulated in vivo in response to caspofungin, 35 genes had been repressed within 12 hr postinfection (Fig. 8A). These 35 genes include several with roles in cell wall biogenesis and integrity, such as PGA31, PHR2, PIR1, and SAP9. Down-regulation of these genes at an early time during infection may render infecting cells more vulnerable to cell wall inhibitors than in vitro-grown cells.

The gene expression responses to caspofungin may differ in vivo and in vitro because of a difference in the transcription factors that mediate the response under each condition. Therefore, we examined the transcription factor genes that are induced by caspofungin under the two conditions (Fig. 8B; S6 Data). Under infection conditions, we detected induction of 18 transcription factor genes (≥2-fold induction, p < 0.05). Under in vitro conditions (RPMI, 37°C), we detected induction of 13 transcription factor genes. The two sets of induced genes had minimal overlap; only iron regulator SFU1 was induced under both conditions. However, the caspofungin up-regulated transcription factor genes included 11 genes that were down-regulated within 12 hr postinfection. It seems reasonable that the different gene expression responses to caspofungin treatment in vivo and in vitro reflect the difference in transcription factor gene responses that we have discovered here.

Infection Portrait from Gene Expression Dynamics

The expression profile reported here is, to our knowledge, the largest time-course analysis of C. albicans infection of a mammalian host. The results indicate that there are both early and late gene expression changes during infection. Early genes underscore the importance of many driving forces in infection that have been deduced from previous studies of C. albicans and other invasive pathogens: hyphal formation and limitation for iron and zinc [31,32]. Late genes reflect responses that have been logically inferred from interactions with innate immune cells: oxidative stress and the consequences of phagocytosis by macrophages [32,33]. In addition, late responses suggest that iron and zinc limitation become less severe as infection proceeds, as reported for iron availability by Potrykus et al. [18]. Therefore, the overall features of gene expression during infection that we describe here fit well with the current understanding of C. albicans infection biology.

The changes in carbon metabolic gene expression we observe during infection suggest that invasive cells shift from glycolysis to gluconeogenesis. Early up-regulated genes included four genes that specify subunits of pyruvate dehydrogenase, which is needed for carbon flux from glycolysis into the tricarboxylic acid cycle. Early down-regulated genes included the glycerol biosynthetic genes GPD2 and RHR2, a likely reflection of metabolic feedback from high levels of the kidney osmoprotectant glycero-phosphocholine [34], which is utilized by C. albicans during infection [35]. Also down-regulated early was the PCK1 gene, which specifies the gluconeogenic enzyme phosphoenolpyruvate carboxykinase. This observation is consistent with the idea that carbon is metabolized through glycolysis, perhaps augmented by catabolism of glycerol, at early times in infection. Late gene expression changes suggest that carbon metabolism shifts toward gluconeogenesis as infection progresses; the glyoxylate cycle genes ICL1 and MLS2 were up-regulated, as was the gluconeogenic gene PCK1. These changes may reflect increased lipid catabolism for carbon because the β-oxidation gene FOX2 is up-regulated as well. Interestingly, up-regulation of ICL1, MLS2, PCK1, and FOX2 occurs upon internalization by macrophages [17]. Our results are consistent with an analysis of green fluorescent protein (GFP) fusion gene expression during infection by Barelle et al. [36], who detected higher expression of PCK1 and ICL1 in infecting cells than in glucose-grown cells, which are arguably equivalent to our inoculum. Our interpretation that cells shift from glycoloysis to gluconeogenesis during infection is consistent with the finding by Barelle et al. that a defect in glycolysis blocks establishment of infection, whereas defects in the glyoxylate cycle or gluconeogenesis impair later events in infection [36]. Therefore, the gene expression data reported here are consistent with the observation that glycolysis is required early in infection and that invasive C. albicans cells respond to phagocytosis at later times during infection.

Our gene expression data correlate with a recent gene expression profile by Chen et al. of whole zebrafish through a time-course of C. albicans infection [16]. The study normalized samples to the mean expression level over the time-course for each gene. Our early up-regulated genes correlated with genes that Chen et al. detected as up-regulated at 8 hr postinfection (roughly the midpoint of the time-course) and as down-regulated at 0.5 hr postinfection. Because of the normalization procedure, it is reasonable that both of these zebrafish samples would correlate with our early class. Our late up-regulated genes correlate with genes that Chen et al. detected as up-regulated at 16 hr postinfection, close to the end of the time-course. These correlations suggest that C. albicans carries out similar biological processes in both infection models. Many matches among both early and late genes map to metal ion homeostasis GO terms. This correlation is consistent with the idea that nutritional immunity is used broadly as a defense mechanism [31].

Does C. albicans express different sets of genes at different infection sites? We can begin to address that question by comparison of the kidney infection profile reported here with infection profiles of other mouse infection models: oropharyngeal candidiasis [13] and abdominal infection [12]. Expression levels for 115 genes were assayed in all three models, and we converted the data to expression ratios relative to the inoculum samples used here (Fig. 9). Thirty-two genes are up-regulated in all infection models, and they include many genes related to hyphal formation (ALS3, ECE1, HWP1, SOD5, TEC1, UME6), zinc limitation (PRA1, ZRT1, ZRT2) and iron limitation (PGA7, RBT5). Therefore, with the qualification that the dataset is small, there seem to be common driving forces in all three infection models. There are some examples of genes up-regulated only during oral infection, though too few to allow an inference about relevant regulatory or functional relationships. There are also genes up-regulated only during abdominal infection, which include the adhesin genes ALS5, ALS6, and ALS9. These adhesins may be involved in the abscess formation that characterizes this model [12]. The most striking conclusion, though, is that we see no examples of genes that are expressed only during kidney infection. We have sampled only 2% of the C. albicans genome, and it seems likely that kidney-specific genes will be found. The genes we have sampled are enriched for stress response genes, though, and we speculate that the kidney may be a common target organ for disseminated infection because it is a relatively hospitable environment.

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Fig 9. Gene expression during murine infection.

Expression levels for 114 genes are compared in three murine infection models: oropharyngeal candidiasis (48 hr postinfection; oropharyngeal candidiasis [OPC]) [13], kidney infection (12, 24, and 48 hr [this study]), and intra-abdominal infection (48 hr postinfection; IAC) [12]. Expression levels are presented as ratios to levels in the inoculum samples used in this study (stationary phase, YPD), and shown as a heat map. Expanded portions illustrate genes induced during oral infection, during all three types of infection, and during abdominal infection. All numerical data for this figure are in S7 Data.

https://doi.org/10.1371/journal.pbio.1002076.g009

Gene Regulation during Infection

The C. albicans transcription factors that govern infection have been extensively studied through both single gene analyses and large scale competitive growth assays [27,37]. We find that strong in vivo expression or up-regulation are excellent predictors of function during invasive infection. Expression level seems to be the most discerning predictor; highly expressed transcription factor genes are much more likely to have measurable impact on infection than the weakly expressed. Such considerations may offer useful prioritization of prospective virulence regulators in pathogens that lack the wealth of prior analysis of C. albicans.

The infection environment modifies the gene expression impact of the majority of transcription factors that we examined, compared to what is observed under in vitro conditions. The gene expression impact of a mutation in vivo may reflect an amalgam of the direct effects of the transcription factor bound to its target genes, along with input from a spectrum of stress response pathways activated in each mutant when it is unable to proliferate. This explanation seems especially compelling for the efg1Δ/Δ mutant. The profile of this strain in vivo displayed a good correlation among down-regulated hyphal genes but little correlation among up-regulated genes, when compared with in vitro efg1Δ/Δ expression data [25]. The genes up-regulated in vivo were diverse, with many annotated to the GO term Response to Stress (e.g., CAP1, CAT1, HSP104, HSP70, and YHB1). Our data suggest that the kidney environment encountered by each mutant is similar, in that host gene expression response is essentially proportional to the fungal burden assessed by fungal RNA level; we see no evidence for qualitative alteration of the host response by individual mutant strains. Therefore, we would expect to see a common set of genes with altered expression in many different attenuated mutants. We see some evidence of a shared gene expression response among all of the mutants assayed. We believe that genome-wide analysis combined with additional replicates, given the low mutant titers, will document a more extensive shared gene expression response among attenuated strains.

Are in vivo mutant profiles informative? In the case of Rim101, comparison of the mutant profiles from two different infection sites offers a simple explanation for niche-specific rim101Δ/Δ mutant phenotypes. During invasive growth in the kidney, the rim101Δ/Δ mutant is defective in hyphal formation [19]. Our results here provide a simple explanation that was not understood previously (see [14,38] for recent reviews): the rim101Δ/Δ mutant has reduced expression of two hyphal regulatory genes, EFG1 and TEC1, during kidney invasion. In contrast, during growth in an abdominal infection model, the rim101Δ/Δ mutant is not defective in hyphal formation, nor in EFG1 or TEC1 expression [12]. Many mutant strains are attenuated in multiple infection models, and we often infer that the same pathways or functions are thus required for infection in multiple contexts. Interestingly, though, if the gene expression impact of a mutant defect is tailored differently in distinct infection environments, then the same genetic lesion may lead to different causes for attenuation.

If regulatory relationships are modified during infection, then it seems possible that some regulatory pathways may be more prominent during growth in vivo than in vitro. Here, we have identified such a pathway relationship, in which transcription factor Sut1 is required for Zap1 expression, and Zap1 in turn activates zinc acquisition genes that are necessary for proliferation in vivo. The sut1Δ/Δ and zap1Δ/Δ mutants have very similar gene expression alterations in vivo, an observation that led us to hypothesize that they function in the same pathway. Interestingly, the two mutants have little similarity between their gene expression alterations during growth in vitro, and only the zap1Δ/Δ mutant has a zinc acquisition deficiency in vitro. In addition, there is no indication of a functional interrelationship between the orthologous genes in Saccharomyces cerevisiae. In other words, the traditionally sought indications that two genes are functionally related were negative. We demonstrated that Sut1 and Zap1 are functionally related in vivo with the demonstration that ZAP1 overexpression restores zinc acquisition gene expression and virulence to a sut1Δ/Δ mutant. This conclusion was strengthened by the finding that overexpression of the zinc transporter gene ZRT2 also restores virulence to a sut1Δ/Δ mutant. Therefore, in vivo profiling data can define biologically relevant functional relationships that are not evident from in vitro analysis.

Our hypothesis is that Rpn4 is functionally related to Sut1 and Zap1, given the similarity of the three mutant gene expression profiles during infection. Rpn4 is not required for zinc acquisition gene expression, so Rpn4 may govern a response to zinc limitation. There are many alternative hypotheses, though, and the issue may be resolved by future functional studies.

Response to Drug Treatment In Vivo

It seems logical that pharmacological perturbations, like genetic perturbations, may have distinct effects in vivo and in vitro. The gene expression response to the cell wall inhibitor caspofungin is a clear example. The functional spectrum of caspofungin-induced genes seems similar under all conditions and includes many cell wall–modification genes. However, the particular genes that are induced in infecting cells are quite different from those induced in cells grown in vitro. Our finding that a different selection of transcription factor genes is induced by caspofungin in vivo and in vitro helps to explain the overall difference in transcriptional responses.

An unanticipated finding from our caspofungin response profiling is that caspofungin induces many of the same genes that are repressed early in infection. It is not clear why caspofungin reverses the repression of many genes that occurs early in infection; perhaps the high osmolarity of the kidney relieves the need for cell wall reinforcement by eliminating turgor, thus repressing cell wall reinforcing functions early in infection. Alternatively, the sequestration of nutrients in tissue may cause repression of genes early in infection, and perhaps lysis of the first drug-exposed C. albicans cells relieves nutrient limitation and thus repression. What we find exciting is that the correlation may mean that infecting cells, by virtue of repressing genes that help survive drug treatment, may be in a more drug-susceptible state than in vitro–grown cells. This inference fits well with the study of Wheeler et al. [39], who observed that cell wall β-glucan is more exposed on the C. albicans cell surface of infecting cells than of in vitro–grown cells. Thus the Wheeler study indicates that cell wall structure is different in vitro and in vivo; our data argue that one source of this difference is the transcriptional regulation of cell wall biogenesis genes.

Perspective

Many of the key regulatory and environmental signals that have been deduced to govern C. albicans behavior during infection are indeed manifested at the level of gene expression. Regulatory relationships are modified during infection though, with two main consequences that we have reported. First, some regulatory relationships are more evident in vivo than in vitro, and the in vivo relationships can reveal pathways that are relevant to pathogenicity. Our analysis of SUT1, ZAP1, and ZRT2 stands as an example. Second, the response to drug treatment manifested during infection can be distinct from that detected in vitro, as revealed by our analysis of caspofungin treatment. The drug response during infection includes induction of many infection-repressed genes, a relationship that may contribute to drug efficacy. Casadevall and colleagues [40] have argued that virulence is an emergent property, a unique state manifested only when host and pathogen interact. Our results provide support for this concept by illustrating that unique regulatory relationships emerge in the environment of invasive infection.

Ethics Statement

All animal procedures were approved by the Institutional Animal Care and Use Committee at the Los Angeles Biomedical Research Institute (protocol 011000) and carried out according to the National Institutes of Health (NIH) guidelines for the ethical treatment of animals.

The mice were caged in an AAALAC-accredited facility located on the campus of Harbor-UCLA Research and Education Institute. A full-time veterinarian who specializes in laboratory animal medicine oversaw their care. Caging and husbandry was provided according to the guidelines in the United States Public Health Service publication Guide for the Care and Use of Laboratory Animals.

Every attempt was made to treat the mice humanely. The survival and health of the mice was monitored three times daily. Obviously sick, lethargic mice were segregated from the group and euthanized to minimize suffering.

The mice were euthanized by pentobarbital overdose (210 mg/kg), as recommended by the Panel on Euthanasia of the American Veterinary Medical Association.

Media

C. albicans strains were grown at 30°C in YPD (2% Bacto peptone, 2% dextrose, 1% yeast extract) or 37°C in RPMI-1640 (with L-glutamine and 0.165M MOPS, without sodium bicarbonate), both with shaking at 200 rpm. Transformants were selected on synthetic medium (2% dextrose, 1.7% Difco yeast nitrogen base with ammonium sulfate and auxotrophic supplements) or on YPD+clonNAT400 (2% Bacto peptone, 2% dextrose, 1% yeast extract, and 400 μg/ml nourseothricin [clonNAT, WERNER BioAgents]) for nourseothricin-resistant isolates. Growth on low-zinc medium [41] was assayed with synthetic medium lacking added zinc (2% dextrose, 1.7% yeast nitrogen base without ammonium sulfate and without zinc sulfate, 0.2% ammonium sulfate, 2.5 μM EDTA, and auxotrophic supplements).

Construction of pSG1 IRO1 Plasmid

The C. albicans IRO1 gene was polymerase chain reaction (PCR) amplified from strain SC5314 genomic DNA using primers pSG1 Nde1 IRO1 18 F and pSG1 Nde1 IRO1 257, which have flanking homologous sequences to plasmid pSG1, a derivative of pDDB78 [42] from which the S. cerevisiae TRP1 marker had been deleted. The PCR product was co-transformed with NdeI digested pSG1 into S. cerevisiae for homologous recombination. The resultant plasmid was isolated from S. cerevisiae and transformed into Escherichia coli for amplification. The plasmid pSG1 IRO1 can be linearized by Afe1 digestion and then transformed into a His^- C. albicans strain to direct integration at the IRO1 locus, making the strain His⁺ IRO1.

C. albicans Strains

C. albicans deletion mutant strains were constructed in the BWP17 strain background through homologous recombination [43]. Briefly, gene disruption PCR products were synthesized using the plasmids pRS-ARG4 or pGEM-URA3 as templates. The primers were designed to include homology to the sequence immediately upstream of the start codon or the sequence immediately following the stop codon of the target gene. Arg⁺ Ura⁺ homozygous deletion strains were then made His⁺ along with restoring IRO1 by integration of linearized plasmid pSG1-IRO1 at the HIS1 locus. For complementation of deletion strains, PCR primers were designed to amplify genomic DNA of strain SC5314 from 1 kb upstream to 0.5 kb downstream of the open reading frame of specific genes. Shorter distances were used when there were additional genes located within this region. These primers have 5′ flanking sequences with homology to pSG1 IRO1. The resulting PCR product was co-transformed into S. cerevisiae with Not1 and Sac1 digested pSG1 IRO1. Plasmid DNA was isolated, transformed into E. coli, and isolated plasmid DNA was digested with AfeI and transformed into the respective C. albicans mutant strains. The complementing cassette was targeted to the IRO1 locus and these complemented strains were then also His⁺ IRO1.

Where indicated, we used the prototrophic reference strain CW696 as a control. It is derived from the SC5314 background [44] strain DAY286 [45], which in turn derives from strain BWP17 [43], as do all of the deletion mutants studied here. Strain DAY286 was transformed with Afe1-digested plasmid pSG1 IRO1 to restore both HIS1 and IRO1. His+ transformants were selected on CSM-his. We verified that CW696 expresses IRO1 through nanoString assays, and confirmed that its virulence is comparable to that of strain SC5314, based on median survival assays in the mouse hematogenously disseminated infection model.

For ZAP1 and ZRT2 overexpression strains, PCR primers were designed to amplify the NAT1-pTDH3 cassette from plasmid CJN542 [15]. These primers were designed to include homology to the sequence immediately upstream of the start codon or the sequence immediately following the stop codon of the ZAP1/ZRT2 gene. The PCR products were transformed into the respective C. albicans mutant strains, replacing one allele of ZAP1/ZRT2 promoter with the TDH3 promoter by homologous recombination. C. albicans strains used in this study are listed in S1 Table. PCR primers used in this study are listed in S2 Table.

Mouse Infection and Survival Assay

Male Balb/c mice weighing 20–22g (Taconic Farms) were used for all studies. In the survival studies, five mice per strain of C. albicans were inoculated intravenously with 5 × 10⁵ yeast-phase cells that had been grown in YPD at 30°C to saturation as described previously [46]. A portion of each inoculum was plated on a YPD plate, and CFU was measured after 2 d of growth to check viability of cells in the inoculum. The animals were monitored three times daily for 21 d, and moribund mice were humanely euthanized. The results of the survival experiments were analyzed with the Log-Rank Test. For isolation of host and fungal RNA and histopathology, three mice per strain were inoculated intravenously with 1 × 10⁶ yeast-phase organisms, and the kidneys were harvested at specific time points. The right kidney was snap frozen in liquid nitrogen and stored at −80°C for later RNA extraction. The left kidney ways fixed in zinc-buffered formalin, embedded in paraffin, sectioned, and stained with Periodic acid-Schiff. All mouse experiments were approved by the Animal Care and Use Committee at the Los Angeles Biomedical Research Institute (protocol 011000) and carried out according to the National Institutes of Health (NIH) guidelines for the ethical treatment of animals.

RNA Isolation from Mouse Tissues

RNA isolations were performed using Qiagen RNeasy mini kit (Cat#74104) with modifications described here. Approximately 1.5 ml of buffer RLT with 1% β-ME was added to each kidney immediately before homogenization using gentelMACS dissociator (Miltenyi Biotec) on pre-loaded setting RNA_02.01. The M tube (Miltenyi Biotec) was centrifuged at 2,000 rpm for 1 min in Eppendorf tabletop centrifuge 5810R at room temp. 600 μl homogenate was transferred to a fresh 2 ml screw-cap tube and mixed with 600 μl Phenol:Chloroform:Isoamyl Alcohol 25:24:1 and 300 μl of Zirconia beads (Ambion), vortexed on a mini-beadbeater (Biospec Products) for 3 min, and centrifuged at 14,000 rpm for 5 min in a 4°C cold room. The aqueous phase was transferred to a new 1.5 ml microfuge tube, mixed well with equal volume of 70% ethanol, and loaded onto the RNeasy spin column. The washing and RNA elution steps were carried out following Qiagen Quick-Start Protocol for RNeasy Mini Kit (Cat#74104).

Caspofungin Treatment

For caspofungin treatment in vivo, caspofungin was administrated at a dose of 1 mg/kg(approximately 20 ug per mouse) intraperitoneally at 24 hr postinfection. The control mice received a similar volume of PBS intraperitoneally. The mice were killed 2 hr after drug administration and the kidneys were collected for RNA isolation. For caspofungin treatment in vitro, an overnight YPD 30°C culture of wild-type SC5314 was diluted 1:1,000 into 50 ml RPMI cultures and grown at 37°C with 200 rpm shaking for 4 hr. Caspofungin was then added to a final concentration of 100 ng/ml, and the control cultures received equal amount of water. Cells were collected by filtration 2 hr after drug administration.

NanoString Codeset Design

Because nanoString technology is not genome-wide, we developed strategies to select high-priority fungal and host genes for investigation. First, we selected 248 C. albicans genes that play important roles in environmental responses, such as iron/zinc acquisition, oxidative/nitrosative stress, pH response, hyphal growth, and general stress responses. We reasoned that these environmental response gene probes (ER codeset) should give us clues about what environmental challenges Candida cells face during infection and which pathways are employed to cope with such challenges. Secondly, we designed a codeset to include 231 Candida genes that specify known and predicted transcription factors (TF codeset). Finally, we selected 46 mouse genes that are critical in host responses (HR codeset) to microbial infection, including genes encoding for fungal pattern recognition receptors, chemokines and chemokine receptors, interleukins and interleukin receptors, interferons and antimicrobial peptides. All the Candida gene codesets also include control genes TDH3 and YRA1, and the mouse gene codeset includes control genes ACTB, GAPDH, and PPIA. The complete list of genes for all codesets is shown in S1, S3, and S4 Data.

NanoString Sample Preparation and Data Analysis

For each nanoString assay, 10 μg of total tissue RNA isolated from a mouse kidney or 100 ng of pure Candida RNA isolated from an in vitro culture was mixed with a nanoString codeset mix and incubated at 65°C overnight (16–18 hr). The reaction mixes were loaded on the nanoString nCounter Prep Station for binding and washing, and the resultant cartridge was transferred to the nanoString nCounter digital analyzer for scanning and data collection. A total of 600 fields were captured per sample. The raw data were first adjusted for binding efficiency and background subtraction, following nCounter data analysis guidelines. The total adjusted counts (i.e., before normalization using the internal control genes) for genes on the same codeset were used to estimate relative fungal RNA abundance in each sample. For example, 10 ug RNA isolated from kidney 24 hr postinfection by wild-type strain SC5314 generated 324,648 total counts for 150 genes (mean of six determinations), while that from rim101Δ/Δ infected kidney generated 49,083 counts (mean of three determinations). We estimated that at 24 hr postinfection, the relative fungal RNA abundance for rim101Δ/Δ infected kidney is roughly 15% (49,083/324,648) of that of the wild type. Following the same logic, we estimated that fungal RNA consists approximately 0.1% of total RNA isolated from kidney infected by the wild-type strain SC5314 at the 24 hr time point (by comparing to total counts from in vitro samples).

To calculate gene expression ratios among different samples, we normalized adjusted raw counts using internal control genes. For the ER codeset, data were normalized using the control gene TDH3. For the TF codeset, data were normalized by total counts of 231 genes. We tested a number of methods for normalization, including using another control gene YRA1, using total counts for all genes, and using geometric mean of robustly expressed genes. We found that the normalization factors resulting from these methods are similar and will not affect our main conclusions should we use a different method for normalization. For the HR codeset, data were normalized using the geometric mean of three internal control genes: ACTB, GAPDH, and PPIA. All expression ratios were calculated using mean values of three independent biological samples, and statistical significance was determined by two-tailed Student’s t-test (n = 3, p < 0.05, unless specified otherwise). The heat maps were generated using Multiexperimental Viewer 4.9.0. Dataset comparisons were carried out with one-sided Fisher's Exact Test, using query gene sets from in vivo nanoString data described here (applying cut-off at 2X, 4X or 10X changes) and the entire set of nanoString probes as a background set. Queries were matched to a database we assembled of 166 published expression datasets, as well as nanoString datasets we generated during this study.

Quantitative Reverse Transcription PCR

Quantitative reverse transcription PCR reactions were carried out as previously described [47]. Briefly, 10 μg total RNA was treated with the DNA-free kit (Ambion) followed by first-strand cDNA synthesis from half of the DNA-free RNA using the AffinityScript multiple temperature cDNA synthesis kit (Stratagene). Absence of DNA contamination was confirmed using control sets for which reverse transcriptase was omitted from the cDNA reaction. Primers were designed to amplify a 150–200 bp region for target genes including TDH3, which was used as a reference gene for normalization. 2X iQ SYBR Green Supermix (Bio-Rad), 1 μl of first-strand cDNA reaction mixture, and 0.1 μM of primers were mixed in a total volume of 50μl per reaction. Real-time PCR was performed in triplicate using an iCycler iQ real-time PCR detection system (Bio-Rad). The program for amplification had an initial denaturation step at 95°C for 5 min, followed by 40 cycles of 95°C for 45 s and 58°C for 30 s. Product amplification was detected using SYBR Green fluorescence during the 58°C step, and specificity of the reaction was monitored by melt-curve analysis following the real-time program. Gene expression was determined using Bio-Rad iQ5 software (ΔΔCT method).