Transcriptomic and proteomic analyses of the Aspergillus fumigatus hypoxia response using an oxygen-controlled fermenter
- Bridget M Barker†1,
- Kristin Kroll†2, 3,
- Martin Vödisch†2, 3,
- Aurélien Mazurie4,
- Olaf Kniemeyer2, 3Email author and
- Robert A Cramer1Email author
© Barker et al; licensee BioMed Central Ltd. 2012
Received: 16 September 2011
Accepted: 6 February 2012
Published: 6 February 2012
Aspergillus fumigatus is a mold responsible for the majority of cases of aspergillosis in humans. To survive in the human body, A. fumigatus must adapt to microenvironments that are often characterized by low nutrient and oxygen availability. Recent research suggests that the ability of A. fumigatus and other pathogenic fungi to adapt to hypoxia contributes to their virulence. However, molecular mechanisms of A. fumigatus hypoxia adaptation are poorly understood. Thus, to better understand how A. fumigatus adapts to hypoxic microenvironments found in vivo during human fungal pathogenesis, the dynamic changes of the fungal transcriptome and proteome in hypoxia were investigated over a period of 24 hours utilizing an oxygen-controlled fermenter system.
Significant increases in transcripts associated with iron and sterol metabolism, the cell wall, the GABA shunt, and transcriptional regulators were observed in response to hypoxia. A concomitant reduction in transcripts was observed with ribosome and terpenoid backbone biosynthesis, TCA cycle, amino acid metabolism and RNA degradation. Analysis of changes in transcription factor mRNA abundance shows that hypoxia induces significant positive and negative changes that may be important for regulating the hypoxia response in this pathogenic mold. Growth in hypoxia resulted in changes in the protein levels of several glycolytic enzymes, but these changes were not always reflected by the corresponding transcriptional profiling data. However, a good correlation overall (R2 = 0.2, p < 0.05) existed between the transcriptomic and proteomics datasets for all time points. The lack of correlation between some transcript levels and their subsequent protein levels suggests another regulatory layer of the hypoxia response in A. fumigatus.
Taken together, our data suggest a robust cellular response that is likely regulated both at the transcriptional and post-transcriptional level in response to hypoxia by the human pathogenic mold A. fumigatus. As with other pathogenic fungi, the induction of glycolysis and transcriptional down-regulation of the TCA cycle and oxidative phosphorylation appear to major components of the hypoxia response in this pathogenic mold. In addition, a significant induction of the transcripts involved in ergosterol biosynthesis is consistent with previous observations in the pathogenic yeasts Candida albicans and Cryptococcus neoformans indicating conservation of this response to hypoxia in pathogenic fungi. Because ergosterol biosynthesis enzymes also require iron as a co-factor, the increase in iron uptake transcripts is consistent with an increased need for iron under hypoxia. However, unlike C. albicans and C. neoformans, the GABA shunt appears to play an important role in reducing NADH levels in response to hypoxia in A. fumigatus and it will be intriguing to determine whether this is critical for fungal virulence. Overall, regulatory mechanisms of the A. fumigatus hypoxia response appear to involve both transcriptional and post-transcriptional control of transcript and protein levels and thus provide candidate genes for future analysis of their role in hypoxia adaptation and fungal virulence.
The frequency of invasive fungal infections (IFIs) has increased among immunosuppressed patient populations with the mold Aspergillus fumigatus the second most frequent cause of IFIs . As the use of immunosuppressive therapy is increasingly common for many medical conditions, continued increases in IFI incidence are expected. While the introduction and increased use of new triazoles such as posaconazole and voriconazole have improved patient outcomes, mortality from invasive aspergillosis (IA) remains high [2–5]. Given the relatively recent emergence of these infections, molecular mechanisms of IA pathogenesis and other forms of aspergillosis are poorly understood. In theory, a better understanding of IA pathogenesis should lead to an improvement in patient outcomes through better diagnosis and use of existing therapeutics. One research area with promise for improving patient outcomes is the study of infection site microenvironment conditions on the expression of fungal virulence and in vivo growth factors. Recently, we observed that infection site microenvironments in the lung of IA murine models are characterized in part by hypoxia [6, 7]. As oxygen is a critical component of many essential biochemical processes in eukaryotes, it has been hypothesized that the ability to overcome hypoxia is a key virulence attribute of human pathogenic fungi [8–15]. Thus, several studies in the human pathogenic yeast Candida albicans and Cryptococcus neoformans have examined the global fungal transcriptome response to hypoxia in order to better understand how human pathogenic fungi adapt to oxygen limitation [11, 14, 16, 17]. However, the global transcriptome response to hypoxia in the pathogenic mold A. fumigatus has not been previously reported.
In mammalian cells, hypoxia has been observed to cause a strong and positive regulation of the transcriptome [18–20]. A key feature of the mammalian hypoxic response is the initiation of anaerobic glycolysis to maintain cellular homeostasis and regulation of glycolysis occurs both at the transcriptional and post-translational level . With regard to fungi, transcriptional induction of genes in glycolysis and repression of aerobic respiration appears to be a main feature of the hypoxia response in the yeast Candida albicans, a facultative anaerobe [11, 16, 17]. However, in the obligate aerobic yeast Cryptococcus neoformans, a general lack of changes in glycolytic mRNA abundance was observed in response to hypoxia, and genes involved in mitochondrial function have been observed to be critical for the hypoxia response [14, 22]. The effects of post-translational regulatory processes on glycolysis in C. neoformans are unknown. In the model obligate aerobic mold Aspergillus nidulans, exposure to hypoxia results in an increase in glycolytic gene transcripts, fermentation, and the GABA shunt, which bypasses two steps of the TCA cycle . Transcriptome data from A. nidulans largely correlated with a proteomics profile where proteins in core metabolism, utilization of the GABA shunt and increases in sulfur, nucleotide and fatty acid metabolism were identified . Recently, in A. fumigatus, glucose-limited chemostat cultures exposed to long-term hypoxia revealed 117 proteins altered in their abundance in response to hypoxia after steady-state conditions were reached . Of the proteins showing a change in abundance, several were associated with glycolysis, respiration, pentose phosphate pathway, and amino acid and pyruvate metabolism. These results showed that hypoxia tends to be a positive regulator of protein expression, with 83 protein spot levels increased in response to hypoxia. Taken together, these data suggest that mechanisms of hypoxia adaptation are variable among fungi.
Here, we provide further insight into the rapid A. fumigatus hypoxia adaptation response by utilizing a joint transcriptomics and proteomics approach. The rationale for our study is the emerging evidence that hypoxia is a critical component of the pathogenesis of IA and other human mycoses [7, 8, 10, 11, 13, 14, 16, 17]. Thus, understanding the molecular mechanisms of hypoxia adaptation in this human pathogenic mold will facilitate a greater understanding of aspergillosis and hopefully reveal potential areas to exploit for improving IA patient outcomes . Taken together, our results reveal new insights into the molecular mechanisms of hypoxia adaptation in A. fumigatus that are both similar and different from previous observations in pathogenic yeast. Importantly, we present several novel candidate genes and biochemical pathways that should be examined in future experiments for their role in hypoxia adaptation and fungal pathogenesis.
Results and Discussion
Effect of hypoxia on transcript and protein levels in batch-fermentation culture
GSEA also identified categories that are significantly reduced in response to hypoxia, and include ribosome biosynthesis, proteasome activity, pyrimidine and purine metabolism, and oxidative phosphorylation across all time points. Additionally, gene groupings associated with heat shock proteins, RNA recognition motifs, and intracellular trafficking and secretion factors were also reduced. There were fewer differences among the reduced transcripts than the increased transcripts across all time points, and more categories and domains were significant (Additional file 2). Thus, a large part of the mRNA response to hypoxia in A. fumigatus involves a reduction in transcript levels of genes associated with core cellular processes.
Looking at different primary metabolic pathways and performing protein set enrichment analysis with KEGG categories, growth under hypoxia resulted in an increased level of proteins involved in glycolysis, ethanol fermentation, electron transport, alanine, aspartate, glutamate metabolism and the oxidative stress response. In contrast, the abundance of several enzymes in the TCA cycle, the pentose phosphate shunt, and cysteine/methionine metabolism decreased during hypoxic growth conditions. Furthermore, a decreased level of proteins involved in ribosome biogenesis, sulfate assimilation and purine metabolism was also observed.
Correlation of Proteomics and Gene Expression
Comparison of the proteomic response of A. fumigatus to short- and long-term incubation under hypoxic conditions
In a previous study we analyzed the proteome of A. fumigatus cultivated in an oxygen-controlled, glucose-limited chemostat under normoxic (21% pO2) and hypoxic (0.2% pO2) growth conditions. In contrast to the experimental set-up chosen for the work described here, the fungus was cultivated under conditions of glucose depletion for a long time period (10 days), and the medium was exchanged continuously. Only one third of the differentially regulated proteins found in our study were also found to be differentially expressed in chemostat cultures . Under both conditions, exposure of A. fumigatus to hypoxia caused an increase in the abundance of glycolytic enzymes and the NO-detoxifying flavohemoprotein Afu4g03410, whereas fatty acid metabolism associated protein levels were reduced. In contrast, the level of proteins associated with the pentose phosphate pathway and the citric acid cycle decreased during short-term incubation at low oxygen-levels, but increased after long-term exposure. The short-term response to hypoxia was also characterized by the activation of ethanol fermentation (see below), which could not be observed after cultivation of A. fumigatus in a chemostat. Furthermore, in comparison to the previous report the pseurotin A cluster was not significantly induced during the short-term exposure to hypoxia. It is interesting to speculate that either the formation of pseurotin A is only activated after longer periods of hypoxia, or glucose depletion may be a factor for derepression of the pseurotin A biosynthesis gene cluster .
Glycolysis and Fermentation
Also related to glycolysis, both transcripts that are required for the conversion of pyruvate to ethanol are induced in response to hypoxia: pdcA (Afu3g11070) encoding a pyruvate decarboxylase, is one of the most highly induced transcripts, along with two alcohol dehydrogenases, alcC (Afu5g06240) and adh2 (Afu2g10960). PdcA and AlcC were both recently confirmed to be required for ethanol fermentation in A. fumigatus, and AlcC was demonstrated to have an important but undefined role in fungal pathogenesis . Consistent with the transcript level results, the protein level of pyruvate decarboxylase PdcA was increased in hypoxia (Additional File 5). In addition, the observed increase in ethanol fermentation is in contrast to the previous proteomic analysis of the A. fumigatus hypoxic response, which did not find evidence of an NAD+ regenerating system . This is likely explained by the different growth conditions used between the two studies. However, our results here with the short term hypoxia response are consistent with recent findings in a murine model of IPA that suggest ethanol fermentation is part of in vivo growth mechanisms of A. fumigatus . In addition, in A. nidulans, an ethanol fermentation response to hypoxia was also observed [23, 24]. However, loss of ethanol fermentation genes in A. fumigatus did not dramatically affect the in vitro growth rate of the fungus in hypoxic conditions. Thus, other mechanisms of NAD+ regeneration are likely present in A. fumigatus and it is unclear what role ethanol fermentation plays in the hypoxia response. One possible alternative fermentation mechanism is lactate fermentation as has been observed in A. nidulans. In support of this hypothesis, a mitochondrial lactate dehydrogenase transcript (Afu1g00510) was transcriptionally increased in response to hypoxia. However, the determined lactate concentration in the culture supernatant was only in the micromolar range (see Figure 1B). Thus, the role of fermentation and NAD+ regeneration in the A. fumigatus hypoxia response awaits further mechanistic characterization.
In contrast to transcript levels, high protein levels of AlcA (Afu7g01010) suggest a critical role for this protein in the hypoxia response, as it was one of the highest induced proteins (Additional files 3 and 4). In A. nidulans, AlcA is involved in utilization of ethanol as a carbon source  and in accordance with this, the level of the AlcA protein highly increased during growth of A. fumigatus on ethanol as shown by a 2D gel electrophoresis study . Thus, it is possible that as ethanol production increases in response to hypoxia, A. fumigatus is then able to utilize the produced ethanol as a carbon source, which may have potential implications for fungal pathogenesis. Alternatively, as these experiments were conducted in a glucose rich environment, the role of ethanol fermentation and/or utilization in the hypoxia response may be overstated and depend on the availability of fermentable substrates.
To further support the observed changes in transcript levels from the microarray data, we chose six transcripts involved in glycolysis for additional analyses using real-time RT-PCR (Figure 7C). Glycolysis starts with the conversion of D-glucose to D-glucose 6-phosphate by hexokinase or glucokinase. There are three hexokinases annotated in the KEGG database (Afu2g00450, Afu2g05910, and Afu6g03980) in A. fumigatus, and these tended to be transcriptionally reduced or unchanged in response to hypoxia, whereas the two glucokinases (Afu6g02230 and Afu2g16330) were induced at later time points when glucose levels began to drop. Thus, it is unclear whether these glucose responsive transcripts were altered due to hypoxia or changes in the glucose level in the culture medium at later time-points. We chose to look at Afu6g02230 (glkA) and Afu2g05910 (hxkA) as these are thought to be the main active enzymes in A. fumigatus for this part of glycolysis . As predicted by the microarray data, the hxkA transcript was repressed and the glkA transcript was enhanced. This regulation may be explained either by a stress-induced expression of hxkA  or by an increase in transcript levels of the high-affinity sugar kinase glkA when the glucose concentration decreases.
Afu4g00960 transcript, 6-phosphofructokinase, one of the early steps in converting glucose 6-phosphate to glyceraldehyde 3-phosphate was slightly reduced in the microarray, and relatively unchanged in PCR. Afu1g10350, phosphoglycerate kinase (pgkA) at the midpoint of the glycolytic pathway was not significantly changed in the microarray. However, RT-PCR data suggest that this transcript is increased in response to hypoxia, which is consistent with findings in mammals for this transcript . Proteomic data confirms the PCR values and shows that PgkA protein levels were also enhanced in response to hypoxia (Additional files 3 and 4). Afu6g07430 transcript, a putative pyruvate kinase, is the final step in the glycolytic pathway and transcript levels were reduced in the microarray and verified with RT-PCR (Figure 7C). However, changes in protein levels of this enzyme were not detected.
Another gene associated with glycolysis investigated with qRT-PCR was Afu6g11430 (aldA), aldehyde dehydrogenase A, which is necessary for conversion of acetaldehyde to acetate for central carbohydrate and lipid metabolism. Microarray results suggest that this transcript was reduced in response to hypoxia and RT-PCR results confirm this observation (Figure 7C). However, there are four additional aldehyde dehydrogenase encoding genes in A. fumigatus, Afu2g00720 and Afu7g01000 (NAD+) and Afu4g08600 and Afu4g13500 (NAD(P)+), so it is possible that these enzymes, and not AldA, are utilized under hypoxic conditions to metabolize ethanol. Afu4g08600 does show a gradual increase in transcript by 24 hours in hypoxia when ethanol levels are assumed to be higher, whereas Afu7g01000 and Afu4g13500 transcripts are reduced, although not to the same degree as aldA (Additional file 5). A recent proteomics study indicated that Afu7g01000 is most probably the major aldehyde dehydrogenase involved in the metabolism of ethanol, as it was highly expressed in A. fumigatus during growth on ethanol . Afu2g00720 was not on the microarray. Taken together, these results suggest that hypoxia plays a small role in altering the transcription of glycolysis encoding enzymes, however, the increase in protein levels of several enzymes coupled with the apparent increase in fermentation suggest that glycolytic activity is likely increased in response to hypoxia in A. fumigatus and regulation may occur at posttranscriptional levels. This observation confirms a previous study, which showed post-transcriptional regulation of glycolysis in anaerobic S. cerevisiae cultures .
Of critical relevance to fungal pathogenesis, cell wall biosynthesis transcripts are affected by hypoxia (Figure 7D) . Specifically, the 1,3-β-glucan synthase fksA (Afu6g12400) and the 1,3-α-glucan synthase agsA (Afu3g00910) are increased along with transcripts for glucanosyltransferases gelC (Afu2g12850) and gelA (Afu2g01170). It is also noted that chitin synthases are altered in expression. Specifically, chsE (Afu2g13440) and chsF (Afu8g056300) transcripts are increased, while the remainder of chitin synthase transcripts are unchanged or reduced. Many cell wall polymers require precursors from the glycolysis pathway and thus alterations in glycolytic flux in response to hypoxia may alter available cell wall precursor levels. Taken together, these results suggest that cell wall composition changes occur in response to hypoxia, which has important ramifications for the effect of cell wall-targeting antifungals, host immune system recognition of A. fumigatus, and the development of the inflammatory response to the invading fungus.
In addition to these, several other ergosterol biosynthesis genes are duplicated in the A. fumigatus genome. The three copies of sterol desaturases, erg3A (Afu6g05140), erg3B (Afu2g00320) and erg3C (Afu8g01070), convert episterol to 5,7,24(28)-ergostatrienol, and require oxygen and NADPH . Erg3A and erg3B are significantly increased at all time points, whereas the erg3C transcript is slightly decreased. The two copies of C-24(28) sterol reductase genes, erg4A (Afu5g14350) and erg4B (Afu1g07140), are the last step of ergosterol biosynthesis, and both show decreased transcript levels in response to hypoxia. Three copies of the lanosterol cyclase, erg7A (Afu5g04080), erg7B (Afu4g012040) and erg7C (Afu4g14770), are involved in the transition from epoxysqualene to lanosterol. All transcripts are increased, and most significantly at 6 and 12 hours. Erg7A and erg7B are slightly decreased in expression at 24 hours. The two copies of acetyl-CoA-acetyltransferase, erg10A (Afu6g14200) and erg10B (Afu8g04000) are relatively unchanged in expression over the 24-hour period of hypoxia exposure. These enzymes are at the earliest stage of the terpenoid biosynthesis pathway, converting 2 acetyl-CoA to acetoacetyl-CoA. This important pathway is upstream of the ergosterol pathway and condensation of terpenoid building blocks (five-carbon isoprene units) leads to precursors for sterol biosynthesis. Next, erg13A (Afu8g07210) and erg13B (Afu3g10660) are significantly decreased, with erg13B more so than erg13A. This is the next step in the terpenoid biosynthesis pathway, converting acetoacetyl-CoA to 3-hydroxy-3-methyl-glutaryl-CoA. Finally, the next step in the terpenoid pathway converts 3-hydroxy-3-methyl-glutaryl-CoA to mevalonate. HMG-CoA reductase (HMGR) is the rate-limiting step in ergosterol biosynthesis and both hmgA (Afu2g03700) and hmgB (Afu1g11230) have reductions in transcript levels in response to hypoxia. Generally, we observe that the duplicated genes in the ergosterol pathway share similar, although not identical, patterns of expression in response to hypoxia.
An intriguing question is the mechanism behind the increase of specific ergosterol biosynthesis transcripts in response to hypoxia. One mechanism is thought to be through direct transcriptional regulation by the fungal SREBP transcription factor, which has been shown to be a key transcriptional regulator of ergosterol biosynthesis [10, 13, 14, 27, 43]. For S. pombe, activation of the SREBP Sre1 by proteolytic cleavage has been linked to sensing of total ergosterol levels in the cell . As sterol biosynthesis decreases upon reduction of available oxygen, the SREBP pathway senses the drop in total ergosterol levels and is capable of restoring sterol homeostasis through induction of sterol biosynthesis genes . However, another mechanism may be suggested in our transcript profiling data, which illustrates a reduction in transcripts associated with terpenoid biosynthesis, which lies upstream of ergosterol production.
As previously stated, several transcripts in the terpenoid (isoprenoid) backbone biosynthesis pathway are reduced in response to hypoxia (Figure 8B). All three initial steps in the terpenoid/mevalonate biosynthesis pathway (erg10, erg13 and hmg) have reductions in transcript levels in response to hypoxia (Figure 8B). Thus, a decrease in needed precursors, such as mevalonate, for sterol biosynthesis may also stimulate an increase in transcript levels of enzymes further down the pathway in response to the growing reduction in important precursor levels. The terpenoid (isoprenoid) biosynthesis pathway also leads to N-glycan and ubiquinone and other terpenoid-quinone biosynthesis pathways in A. fumigatus. Moreover, mevalonate was shown to be a key intermediate in the biosynthesis of siderophores in A. fumigatus under iron-limited conditions . Transcripts in the N-glycan biosynthesis pathway remain mostly reduced in hypoxia, with the exception of three genes: Afu2g14630, cell wall glycosyl hydrolase; Afu6g04210, mannosyl-oligosaccharide glucosidase; and Afu6g09770, geranylgeranyl diphosphate synthase (Additional files 2 and 5). Transcripts associated with ubiquinone biosynthesis are also slightly reduced, but mostly remain unchanged (Additional file 2). Thus, only transcripts in the ergosterol biosynthesis pathway are significantly increased downstream of the terpenoid biosynthesis pathway. A direct regulatory link between terpenoid and ergosterol biosynthesis remains unclear. Most likely, HmgR plays a significant role in coordinately altering the biosynthetic activity of these pathways .
Iron acquisition and siderophore biosynthesis
Tricarboxylic acid cycle, GABA Shunt, and Respiration
As observed with A. nidulans, there is some evidence of the GABA shunt being utilized by A. fumigatus in response to hypoxia (Figure 10B, C). The GABA shunt is hypothesized to help organisms avoid the accumulation of high NADH levels in the absence of a terminal electron acceptor such as oxygen and also contributes to glutamate formation. Of the six A. nidulans genes identified involved in the GABA shunt , there are five potential homologues in A. fumigatus: Afu4g06620 and Afu2g06000 (glutamate dehydrogenase), Afu6g13490 (glutamate decarboxylase), Afu5g6680 (aminobutyrate transaminase, gatA) and Afu3g07150 (succinate-semialdehyde dehydrogenase). Of these five, four were on the A. fumigatus microarray, missing Afu2g06000. Transcripts for two of these genes were increased in response to hypoxia: glutamate decarboxylase Afu6g13490 and glutamate dehydrogenase Afu4g06620 (Figure 10B, C). In the proteomic data, Afu4g06620 protein levels are increased after 24 hours in hypoxia. However, this pathway has not been specifically studied in A. fumigatus, so it is possible that more distant homologues may function in this pathway. Moreover, with the reduction in TCA cycle transcripts, it is unclear what the available levels of 2-oxoglutarate would be to allow flux through the GABA shunt in A. fumigatus. Investigating the KEGG defined glutamate pathway shows that glutamate decarboxylase (Afu6g13490) is also associated with glutamate biosynthesis, and a great number of transcripts do show increased levels in this pathway in response to hypoxia (Figure 10C). Glutamate decarboxylases have a wide range of functions depending on the organism. For example, in S. cerevisiae, GAD1 is critical for normal tolerance to oxidative stress . Thus, the increased levels of transcripts associated with the GABA shunt and glutamate biosynthesis in A. fumigatus may be a response to the changing redox status of the cell in response to hypoxia.
Novel genes showing positive regulation
Afu2g09590 (udpA), an UDP-N-acetylglucosamine 1-carboxyvinyltransferase family member, was strongly increased in hypoxia, and is a protein generally associated with glycan biosynthesis in bacteria, where this gene is commonly named MurA. This gene is associated with the first step in peptidoglycan biosynthesis in bacteria, and is a target for the antibacterial drug fosfomycin . However, a database query http://aspgd.broadinstitute.org/cgi-bin/asp2_v3/shared/show_protein_cluster.cgi?site=asp2_v3&id=960184 shows a similar gene in several Aspergillus species syntenic within the inspected region. The function of this protein in fungal species is unknown.
Afu3g11590 (atg11) transcript was increased at all time points. The gene is homologous to autophagy related protein 11 (ATG11) in S. cerevisiae, and has been shown to be involved in trafficking of autophagosomes and cytoplasm to vacuole (Cvt) vesicles . ATG genes are conserved among eukaryotes, however the function of the Atg11 protein appears to be fungal specific and depends on the interaction with other proteins in the autophagy pathway . Autophagy is stress induced, and it is thought that it can function to recycle cellular components that are no longer necessary into pathways that are critical for survival. In this way, autophagy helps to reduce the energy costs for significant changes in cell physiology . Thus, it is tempting to speculate that autophagy is one mechanism by which A. fumigatus adapts to hypoxic stress.
Afu5g00900 (rgsA), a G-protein signal regulator with an RGS domain, is predicted to be involved in attenuation of G-protein signaling through activation of the intrinsic GTPases of G-proteins . This gene was one of the most highly induced genes in the microarray analysis. Although this gene has not been investigated in A. fumigatus, it is likely that this gene allows for the rapid response of the cell to environmental changes. In A. nidulans, the homologue of this gene (rgsA) negatively regulates the G protein α subunit GanB, which is involved in the activation of various stress responses and the inhibition of asexual conidiation. During hypoxia RgsA is putatively required to down-regulate these energy-consuming stress responses . The GanB ortholog GpaB in A. fumigatus was shown to regulate the virulence gene pksP and loss of GpaB function caused an increased susceptibility to killing by macrophages . In mammalian solid tumors, Rgs2 is associated with hypoxia and pro-tumor functions . Rgs2 appears to be a critical regulator of the pro-angiogenic function of myeloid derived suppressor cells, and deletion of Rgs2 in a murine model significantly reduced tumor growth. Thus, Rgs2 is an intriguing candidate for future study of its role in fungal hypoxia adaptation and pathogenesis.
Novel genes whose transcripts decrease in response to hypoxia
Analysing only genes in the SAM dataset (Additional file 1) with FungiFun, functional categories with transcripts largely reduced in abundance are similar to the GSEA analysis and include ribosome and RNA processing and the TCA cycle (Additional file 7). An additional selection of transcripts whose levels were significantly reduced in hypoxia was also validated with RT-PCR. First, Afu5g12510 (afeA), an adenylate-forming enzyme, was down in the 2 and 6 hour time points and up at 12 and 24 hours in the microarray experiment, and this was confirmed by PCR (Figure 11B). Adenylating enzymes generally activate otherwise unreactive carboxylic acids and can be used to form a wide array of natural products . This gene is a member of the acyl-CoA synthetases (AMP-forming)/AMP-acid ligases family, and potentially involved in lipid metabolism (COG ontology). It is also identified in PANTHER as an ATP-dependent AMP-binding enzyme family member, with a 4-coumerate CoA ligase sub-family designation. Among the most highly reduced proteins, the level of the acetate activating acetyl-coenzyme-A synthetase FacA decreased most significantly in response to hypoxia. This finding suggests an oxygen-dependent regulation of the acetate-activating enzyme FacA that has also been reported for the yeast orthologue Acs1p . Taken together, these results suggest that fatty acid metabolism may be reduced in A. fumigatus in response to hypoxia, contrary to results in other organisms such as the pathogenic yeast C. neoformans .
Next, Afu6g05160 (azf1) is a zinc-finger C2H2-type transcription factor, which may be involved in the regulation of cell cycle, the G2/M transition, and is activated in non-fermenting conditions in S. cerevisiae . Consistent with this, the transcript level was reduced in microarrays after 2 hours in hypoxia, and PCR results confirmed this observation (Figure 11B). Additionally, this transcription factor is a positive regulator of the G1 cyclin CLN3 in S. cerevisiae in response to glucose . CLN3 is involved in the progression of the cell cycle, and activates Cdc28p kinase which promotes the G1 to S phase transition . In general, the transcriptomics and proteomics data along with the actual growth curve of the fungus in hypoxia all suggest a reduction in cellular metabolism, which likely would correspond to changes in the regulation of cell cycle length.
Finally, transcript levels of Afu1g03210 (flbD), homologous to a Myb family conidiophore development gene in A. nidulans [67–69], were reduced at all time points, and this was verified by PCR (Figure 11B). Therefore, it is likely that in response to hypoxia, asexual reproduction is inhibited. Consistent with this hypothesis, the target of FlbD, BrlA (Afu1g16590), is also repressed in expression in the microarrays at all time points. A bZIP protein, FlbB (Afu2g14680), which acts upstream of FlbD, is also repressed in expression, almost 2-fold by 24-hour time point (Additional file 5) . Interestingly, the FlbB locus is also associated with gliotoxin production. Moreover, it is well known that in vivo during invasive aspergillosis that asexual reproduction generally does not occur with A. fumigatus. Thus, the in vivo hypoxic microenvironment might repress the transcriptional program needed for asexual development in response to the host environment.
Transcriptome analysis of transcription factors
Transcription factors involved in regulation of iron homeostasis were also responsive to hypoxic cultivation. The GATA type transcription factor SreA (Afu5g11260) has increased transcript levels at 2, 6, and 12 hours hypoxia, while HapX (Afu5g03920) transcripts are correspondingly slightly reduced. This is consistent with SreA's role as a repressor of HapX [73, 74]. The transcript profile of these two key iron regulators suggests that HapX may be slowly released from SreA repression as the exposure to hypoxia increases. This would be consistent with hypoxic cells initially being iron replete, but as the iron supplies become utilized by key pathways required for hypoxia adaptation (ergosterol biosynthesis, respiration), SreA repression is alleviated and transcripts critical for iron acquisition, including HapX, become more abundant. Also consistent with the observed SreA and HapX transcript profiles, AcuM (Afu2g12330) transcript levels were reduced at 2 and 6 hours growth in hypoxia and slightly increased at 12 and 24 hours. AcuM, a transcriptional regulator of gluconeogenic genes, has recently been suggested to repress SreA and thereby induce HapX transcripts via transcriptional profiling experiments . It is unclear, however, whether acuM transcript levels were affected by changes in glucose levels between early and late time points or hypoxia itself. Importantly, the transcript profile of all the iron acquisition genes does not exactly mimic the expected profile based on what is known about the SreA and HapX regulons. Thus, it seems apparent that additional regulators of iron acquisition and homeostasis are present in A. fumigatus and operative under hypoxia.
In conclusion, we present the first comprehensive examination of the transcriptional and proteomic response to hypoxia adaptation in the human fungal pathogen A. fumigatus. Recent data suggest that overcoming or tolerating hypoxia may be a key component of the virulence arsenal of this important human pathogen [7, 10, 27]. Overall, similar to the pathogenic yeast C. albicans and C. neoformans, we observed that the hypoxia response in A. fumigatus is characterized by both positive and negative changes in transcript and protein levels. Major themes of the hypoxia response in A. fumigatus observed in our study include: transcriptional and proteomic decreases in the TCA cycle in contrast with previous observations in A. nidulans and C. neoformans, ribosome biogenesis and purine metabolism and a concomitant increase in the oxidative stress response, glycolysis and fermentation, cell wall biosynthesis, and iron metabolism. A major positive response seen in transcript levels of genes involved in the biosynthesis of ergosterol was a major observation of the A. fumigatus hypoxia response and is consistent with similar observations in other fungi. Thus, changes in sterol levels in response to hypoxia are a major cellular response to oxygen limitation in the fungal Kingdom. A substantial number of transcription factors were positively regulated in response to hypoxia, including the known regulator of ergosterol biosynthesis, SrbA, and this was consistent with a strong positive effect of hypoxia on overall transcript levels. However, we also observed a core set of genes whose transcript levels decreased while protein levels increased in response to hypoxia. Thus, regulation of cellular hypoxia responses is likely multifaceted and future studies examining the key cellular regulatory responses during adaptation to hypoxia in A. fumigatus are thus warranted. Importantly, our analysis here will allow further investigation of the link between hypoxia adaptation and A. fumigatus pathogenesis. While the strong hypoxia growth deficit and virulence attenuation of the A. fumigatus SrbA null mutant strongly suggests a key link between hypoxia, sterol biosynthesis, and fungal virulence, our studies here suggest several potential sterol independent pathways that may be critical for hypoxia adaptation and thus potentially fungal virulence.
Strain and culture conditions
Aspergillus fumigatus wild-type strains ATCC 46645 and CBS144.89 were used for proteomic and transcriptomic analysis of the hypoxic response, respectively. A. fumigatus was grown in a fermenter (Biostat B-DCU-5l, Braun, Melsungen, Germany) as a batch culture (starting culture volume: 3l) at 37°C with constant stirring (550 rpm). The dissolved molecular oxygen was measured with a pO2 electrode (InPro6800/12/320, Mettler Toledo, Steinbach, Germany) connected to a measuring amplifier in a range of 0 - 10% pO2 (8842698 Braun, Melsungen, Germany). This setting allowed detection of low oxygen concentrations of 0.2% O2 and above. Aerobic culture conditions were established (21% O2) by aerating with 0.65 l/min of air. After an initial aerobic growth phase (between 12.5 h and 14 h) the oxygen concentration was set to a low oxygen partial pressure (hypoxic conditions of 0.2% O2). The O2 concentration was kept constant by aeration of the medium with a mixture of nitrogen and air at constant rate (0.65 l/min). The influx of air was controlled manually. The fungus was cultivated in Aspergillus minimal medium (AMM) as described previously  with slight modifications. The medium contained 16.65 mM glucose as sole carbon and energy source. The fermenter was inoculated with conidia to give a final concentration of 0.67 × 106 conidia/ml. Samples were taken after 0 h (after 12.5 h or 14 h aerobic growth), 2 h (for transcriptome analysis) or 3 h (for proteome analysis), 6 h, 12 h and 24 h cultivation at hypoxic growth conditions. In addition, the pH-value, glucose concentration (BIOSEN C-Line, EKF Diagnostic, Barleben, Germany) and dry weight biomass (HR73 Halogen Moisture Analyzer, Mettler Toledo, Steinbach, Germany) was determined. The concentrations of ethanol, D/L-lactate and acetate in the culture supernatant were quantified by enzymatic detection kits according to the manufacturer's instruction (UV-test for ethanol, D/L-lactate and acetate, R-Biopharm, Darmstadt, Germany). Harvested mycelium was filtered through miracloth (Merck KGaA, Darmstadt, Germany), rinsed with tap water and pressed to remove any liquid and immediately frozen in liquid nitrogen.
Isolation of nucleic acids
Frozen mycelium was ground to a fine powder. 100 mg of tissue was used for total RNA isolation using the Ambion RNA isolation kit according to the manufacturer's instructions. The amount of RNA was determined spectrophotometrically with a nano-drop and RNA quality was interrogated with an Agilent Bioanalyzer.
cDNA preparation and probe labelling
Ambion (Austin, TX) aRNA kit (AM1751) was used according to manufacturer's recommendations to amplify 1.5 ug of total RNA template. A final amount of 5 ug of aRNA was used for cDNA synthesis using SuperScriptIII (Invitrogen), following the "Microbial RNA aminoallyl labeling for microarrays" (SOP# M007 Rev. 2) protocol detailed at http://pfgrc.jcvi.org/index.php/microarray/protocols.html. Briefly, samples were RNaseH treated and concentration checked on Nanodrop. cDNA was purified with Qiagen QIAquick PCR purification kit. Samples were dried completely with speed-vac (Eppendorf). Pellet was suspended with 4.5 μL of 0.1 M Na2CO3. 4.5 uL of Cy3 or Cy5 dye was added to appropriate tubes, and incubated at 28°C for two hours. Uncoupled dye was removed with NaOAc-modified QIAquick PCR kit (Qiagen). Dye ratio was calculated with microarray analysis function on the Nanodrop-1000 (Thermo). The two differentially labelled probes (Cy3 vs. Cy5) that were hybridized to the same microarray slide are mixed with equal cDNA volumes. The Cy3/Cy5 probe mixture was dried to completion. Pellet was suspended in 10 μL of dH2O.
Spotted arrays (Aspergillus fumigatus Af293, version 3) from JCVI were used for the entire experiment http://pfgrc.jcvi.org/index.php/microarray/array_description/aspergillus_fumigatus/version3.html. The protocol "Microbial Hybridization of labelled probes" (SOP# M008 Rev 2.1) can be found at: http://pfgrc.jcvi.org/index.php/microarray/protocols.html. Briefly, the slides were soaked in sterile-filtered 5xSSC, 1%BSA, 0.2%SDS for two hours, washed and dried by centrifugation in mini slide spinner (LabNet) prior to hybridization. 45 μL of 50% formamide, 5xSSC, 0.1%SDS, 0.001 M DTT and 6 μL of salmon sperm DNA were added to previously rehydrated Cy3/Cy5 mix. Lifter slip (Erie Scientific) was washed in 100% EtOH and dried. The slide and lifter slip were placed in hybridization chamber (Corning) and 60 μL of probe mixture was pipeted under lifter slip. Chambers were sealed and incubated in 42°C water bath for 18 hours. Slides were washed twice in 2xSSC, 0.2% SDS, 0.02 M DTT, twice in 0.1× SSC, 0.1% SDS, 0.02 M DTT, twice in 0.1× SSC, 0.02 M DTT, and once with dH2O and 0.02 M DTT. Slides were dried completely in slide spinner and protected from UV exposure.
Slides were scanned with GenePix 4000 B dual wavelength scanner (Axon Instruments, Molecular Devices Co.), adjusting PMT gain ratio to ~1.0, 100% laser power, and pixel size of 10. The resulting images were checked by eye for misaligned regions or false signals using GenePixPro 6.0 (Axon Instruments, Molecular Devices Co.). A GenePix report file was generated with raw data reads for each spot.
All data are available at EMBL MIAMExpress (accession #E-MEXP-3251). Data were processed using TM4 software and protocol recommendations for microarray analysis http://www.tm4.org/. Briefly, GenePix files were converted to MeV files using Expressconverter 2.1. MeV files were analyzed with MIDAS 2.21 to normalize data, according to the recommended settings from TM4. Flip-dye pairs were read into MIDAS using a generous setting for one bad channel, and A and B channel flag check selected. LOWESS was used to minimize effect of intensity dependent bias, with default settings. Standard deviation regularization was used to minimize the effect of slide printing errors, with Cy3 as the reference. Flip-dye pairs were then checked for consistency and merged into a single MeV file. Biological replicates were then assigned a single median value for each gene and time point. Pathway analysis was then completed using gene set enrichment analysis GSEA. Briefly, the functional categories (metabolic pathways, protein families, protein domains) each gene belongs to were retrieved from the DAVID database . For each time point genes were sorted by decreasing fold change. The method described previously , for which an implementation is available http://github.com/ajmazurie/xstats.enrichment, was then used to evaluate how enriched the top of each list (i.e., the most perturbed genes at each time point) was in any of the functional categories listed. The resulting p-values were then corrected for multiple testing using the FDR method . SAM analysis was conducted in MeV, setting FDR at 0.05%. Functional category analysis was completed at the FungiFun website https://sbi.hki-jena.de/FungiFun/FungiFun.cgi to identify KEGG, GO and FunCat associated pathways for genes identified in the SAM included in the additional files . Self Organizing Tree Algorithm (SOTA) analysis was completed in MeV with default settings to determine clusters. Self Organizing Map Algorithm (SOMA) was completed using the Cluster 1.5 library . Self-organizing maps (also called Kohonen maps, see ) organize items into clusters on a two-dimensional grid in which two adjacent clusters are more similar than two distant clusters. As for other clustering methods such as K-means, self-organizing maps must be provided with the number of clusters to group the items into. However the spatial organization of these clusters allows for a visual validation of the number of clusters. Too many clusters will result in the centroid of neighbouring clusters to be nearly indistinguishable. As such the expression data were clustered with a grid of size 10 × 10 (100 clusters) down to 3x3 (9 clusters) using the Pearson correlation coefficient as the metric between expression profiles. The self-organizing map maximizing the number of clusters while limiting redundancies was the one of size 8 × 8 (64 clusters).
RNA from the microarray experiment was DNase treated with DNA-free kit (Ambion) and reverse transcribed with QuantiTect reverse transcription kit (Qiagen, USA). Primers for all genes of interest were designed with PrimerQuest (IDT) and manufactured by IDT, USA and sequences are listed in Additional file 8. All reactions were performed on BioRad MyIQ real-time PCR detection system with IQ SYBR green supermix (Bio-Rad, Hercules, CA). The ΔΔCt method was used to combine all datasets, using β-tubulin as the housekeeping gene .
Sample Preparation for 2-D Gel Electrophoresis
Mycelial protein of A. fumigatus were cleaned up by trichloroacetic acid (TCA)/acetone precipitation as described previously, with slight modifications . Frozen mycelium was ground in a precooled mortar in the presence of liquid nitrogen. About 100 mg of homogenate were precipitated over night with 300 μl 13.3% (w/v) TCA/0.3% (w/v) dithiothreitol (DTT)/acetone at -20°C. After centrifugation for 15 min at 12,000 × g at 4°C the supernatant was removed and the pellet was rinsed twice in ice-cold acetone containing 0.3% (w/v) DTT. The suspension was centrifuged again and the pellet was air-dried for 15 min at room temperature and subsequently resuspended in 300 μl 2D-lysis buffer (7 M urea, 2 M thiourea, 2% [w/v] CHAPS(3-[(3- cholamidopropyl)-dimethylammonio]-1- propanesulfonate), 1% [w/v] Zwittergent 3-10), 30 mM Tris). To improve protein solubility the samples were sonicated for 10 min in an ultrasonic bath and incubated for 1 h at -70°C. After centrifugation at 20,000 × g for 30 min at 16°C, the supernatant was collected. The pH of the samples was adjusted to 8.5 by the addition of a few microliters of a 100 mM NaOH stock solution. The protein concentration was determined according to the Bradford method  using the BIO-RAD protein assay (BIORAD Lab., Hertfordshire, U.K.).
2-D Gel Electrophoresis Analysis
The DIGE (difference in gel electrophoresis) technique was used to analyze cytosolic protein samples of A. fumigatus cultivated under normoxic and hypoxic conditions and carried out as described previously . 15 Samples from three independent hypoxic cultivations were labeled with CyDye minimal dyes according to the manufacturer's protocol with slight modifications (GE Healthcare Bio-Sciences, Munich Germany). 50 μg of protein of each sample were labeled with 300 pmol of CyDye DIGE flourophores (dissolved in dimethyl formamide). Samples obtained at different time points 0, 3, 6, 12 and 24 h of hypoxic (0.2% pO2) conditions were labeled either with Cy3 or Cy5. A pool of all 15 samples (5 time points of 3 biological replicates) was prepared, labeled with Cy2, and used as a global internal standard. Samples were mixed and incubated for 30 min in the dark on ice. The reaction was stopped by adding 1 μL of 10 mM L-lysine. An equal volume of 4× sample buffer (composition described above for the lysis buffer, plus 3.2% [v/v] SERVALYT ampholytes [SERVA Electrophoresis, Heidelberg, Germany] and 40 mM DTT) was added.
For the separation of proteins in the first dimension 24 cm IPG strips with a nonlinear pH range from both pH 3 to 7 and pH 7 to 11 (GE Healthcare Bio- Sciences) which had been rehydrated overnight (7 M urea, 2 M thiourea, 2% [w/v] CHAPS, 1% [w/v] Zwittergent 3_10, 0.002% [w/v] bromophenol blue, 0.5% [v/v] IPG buffer, 1.2% [v/v] De-Streak reagent [GE Healthcare Bio-Sciences]) were used as described . Equal amounts of protein samples from two time points and the internal standard preparations were combined and mixed with 100 μg unlabeled protein extract of the samples (to increase the protein amount for subsequent mass spectrometry analysis) and applied via anodic cup loading to IPG strips. Isoelectric focusing of 24 cm strips was carried out according to the following protocol: 4 h at 300 V (gradient), 4 h at 600 V (gradient), 4 h at 1,000 V (gradient), 5 h at 8,000 V (gradient) and 48,000 V h at 8,000 V (step).
After isoelectric focusing the IPG strips were equilibrated for 15 min in 10 mL of equilibration buffer (6 M urea, 30% [v/v] glycerol, 2% [w/v] SDS (sodium dodecyl sulfate), 75 mM Tris, 0.002% [w/v] bromophenol blue) containing 1% (w/v) DTT and subsequently for 15 min in 10 mL of equilibration buffer containing 2.5% (w/v) iodoacetamide. For the separation of proteins in the second dimension, the Ettan DALT System (GE Healthcare Bio-Sciences) was used. SDS polyacrylamide gels (11-16% [w/v]) of 1.0 mm thickness were casted with the a 2DEoptimizer (Biometra, Göttingen, Germany). Separation conditions were as follows: 1 W/gel for 1 h followed by 15 W/gel for 4 h. Proteins were visualized by analyzing the gels with a Typhoon 9410 scanner (GE Healthcare Bio-Sciences) using a resolution of 100 μm.
Spot detection of cropped images was performed with the DeCyder software package (version 7.0). The following parameters were applied: detection sensitivity, estimated number of spots: 2000; Process exclude filter set: slope > 1.6 and volume < 10 000. Changes in the abundance of protein spots were regarded as significant with a threshold of 2-fold standard deviation difference. Gels of three independent experiments (each 5 time points, technical duplicates) were analyzed with the BVA software, and average ratios as well as t-test values for difference in protein expression were calculated for each spot. Only spots with a t-test value of below 0.05 were regarded as significantly regulated. In order to identify the differently expressed proteins by mass spectrometry (MS), the gels were post-stained with colloidal Coomassie Brilliant Blue according to published protocol  and protein spots were excised manually.
Protein spots were tryptically digested according to published protocol  with slight modifications. Extracted peptides were measured and identified on an Ultraflex I and Ultraflextreme MALDI-TOF/TOF device using flexControl 3.3 for data collection and flexAnalysis 3.3 spectra analysis/peak list generation (Bruker Daltonics, Germany) as described previously . Peptide mass fingerprint (PMF) and peptide fragmentation fingerprint (PFF) spectra were submitted to the MASCOT server (MASCOT 2.3, Matrix Science, U.K.), searching the NCBInr (monthly update) database limited to the taxon Fungi. With respect to the sample preparation, fixed modification of cysteine thiols to S-carbamidomethyl derivatives and variable methionine oxidation were defined for the database search. Further, up to one missed cleavage, and a peptide mass tolerance of 100 ppm was allowed. Results were regarded as significant with an allowed likelihood for a random hit of p ≤ 0.05, according to the MASCOT score (> 54). All proteome data (gel images, spot information) including mzML data files were imported into our in-house data warehouse Omnifung http://www.omnifung.hki-jena.de and are publicly accessible . Identified proteins were classified with the FungiFun annotation tool .
This work was supported by the National Institute of Health grant RR020185 (M. Quinn PI, RAC project 2 leader), NIH/NIAID grant R01AI81838, and the Montana State University Agricultural Experiment Station (RAC). Research of OK, KK and MV was supported by the Hans-Knoell-Institute, the German-Israeli Foundation for Scientific Research and Development (GIF Grant No. 996-47.12/2008) and the International Leibniz Research School for Microbial and Biomolecular Interactions Jena (ILRS) as part of the excellence graduate school Jena School for Microbial Communication (JSMC). We also thank Silke Steinbach and Michael Cyrulies (Jena) and Kate McInnerney (MSU) for their excellent technical assistance and Fabian Horn (Jena) for converting MS-data files.
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