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Transcriptomic and proteomic analyses of the Aspergillus fumigatus hypoxia response using an oxygen-controlled fermenter.

Barker BM, Kroll K, Vödisch M, Mazurie A, Kniemeyer O, Cramer RA - BMC Genomics (2012)

Bottom Line: To survive in the human body, A. fumigatus must adapt to microenvironments that are often characterized by low nutrient and oxygen availability.A concomitant reduction in transcripts was observed with ribosome and terpenoid backbone biosynthesis, TCA cycle, amino acid metabolism and RNA degradation.However, a good correlation overall (R(2) = 0.2, p < 0.05) existed between the transcriptomic and proteomics datasets for all time points.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Immunology and Infectious Disease, Montana State University, Bozeman, MT, USA.

ABSTRACT

Background: 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.

Results: 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 (R(2) = 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.

Conclusions: 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.

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Heat map comparison of abundance levels for both transcriptomic and proteomic data. Differences and similarities between protein and mRNA levels are shown, blue indicates decreased levels, and yellow indicates increased levels. Data are sorted in the same order as additional file 4 in order of function. Highlighted with brackets are transcripts and proteins associated with glycolysis and amino acid metabolism, which showed different values for transcript and protein abundance. Additional differences are highlighted with # (transcript higher than protein) or * (protein higher that transcript).
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Figure 6: Heat map comparison of abundance levels for both transcriptomic and proteomic data. Differences and similarities between protein and mRNA levels are shown, blue indicates decreased levels, and yellow indicates increased levels. Data are sorted in the same order as additional file 4 in order of function. Highlighted with brackets are transcripts and proteins associated with glycolysis and amino acid metabolism, which showed different values for transcript and protein abundance. Additional differences are highlighted with # (transcript higher than protein) or * (protein higher that transcript).

Mentions: Of note, in the correlation of proteomics and transcriptomics data (Figure 4), we observed very few points located in the top-left quadrant, and therefore very few instances occurred where mRNA levels increased and protein levels decreased. Two examples of this are represented by transcripts that were increased for the entire time course, yet the proteins showed decreased levels: a nuclear segregation protein Brf1 (Afu1g14120) and a hypothetical protein (Afu7g00350) (marked with # in Figure 6). In general, if the mRNA abundance was enhanced, the protein levels were also high. However, there were exceptions with genes that had reduced mRNA levels and enhanced protein abundance. Comparing the fold change values for proteins with the microarray data, 25 of the genes show different patterns of transcript and protein levels (Additional file 4). Two functional categories, glycolysis and amino acid metabolism, are noted on the heat map of comparison (Figure 6). Several of the transcripts in these groups are decreased in abundance, while the protein levels are increased. Three additional pairs are noted with an asterisk, a 40S ribosomal protein (Afu6g12660), the Asp-hemolysin (Afu3g00590) and the proteasome component Pre2 (Afu6g08310). It is possible that enhanced protein levels could be achieved through mRNA stabilization and differences in turnover rates, and it seems clear that in addition to the apparent transcript regulation of the hypoxia response, other post-transcriptional and post-translational regulatory mechanisms are active. Further identification of these regulatory mechanisms could identify new targets for gene replacement studies to determine whether these mechanisms are critical for in vivo growth of the fungus during hypoxia adaptation and pathogenesis.


Transcriptomic and proteomic analyses of the Aspergillus fumigatus hypoxia response using an oxygen-controlled fermenter.

Barker BM, Kroll K, Vödisch M, Mazurie A, Kniemeyer O, Cramer RA - BMC Genomics (2012)

Heat map comparison of abundance levels for both transcriptomic and proteomic data. Differences and similarities between protein and mRNA levels are shown, blue indicates decreased levels, and yellow indicates increased levels. Data are sorted in the same order as additional file 4 in order of function. Highlighted with brackets are transcripts and proteins associated with glycolysis and amino acid metabolism, which showed different values for transcript and protein abundance. Additional differences are highlighted with # (transcript higher than protein) or * (protein higher that transcript).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3293747&req=5

Figure 6: Heat map comparison of abundance levels for both transcriptomic and proteomic data. Differences and similarities between protein and mRNA levels are shown, blue indicates decreased levels, and yellow indicates increased levels. Data are sorted in the same order as additional file 4 in order of function. Highlighted with brackets are transcripts and proteins associated with glycolysis and amino acid metabolism, which showed different values for transcript and protein abundance. Additional differences are highlighted with # (transcript higher than protein) or * (protein higher that transcript).
Mentions: Of note, in the correlation of proteomics and transcriptomics data (Figure 4), we observed very few points located in the top-left quadrant, and therefore very few instances occurred where mRNA levels increased and protein levels decreased. Two examples of this are represented by transcripts that were increased for the entire time course, yet the proteins showed decreased levels: a nuclear segregation protein Brf1 (Afu1g14120) and a hypothetical protein (Afu7g00350) (marked with # in Figure 6). In general, if the mRNA abundance was enhanced, the protein levels were also high. However, there were exceptions with genes that had reduced mRNA levels and enhanced protein abundance. Comparing the fold change values for proteins with the microarray data, 25 of the genes show different patterns of transcript and protein levels (Additional file 4). Two functional categories, glycolysis and amino acid metabolism, are noted on the heat map of comparison (Figure 6). Several of the transcripts in these groups are decreased in abundance, while the protein levels are increased. Three additional pairs are noted with an asterisk, a 40S ribosomal protein (Afu6g12660), the Asp-hemolysin (Afu3g00590) and the proteasome component Pre2 (Afu6g08310). It is possible that enhanced protein levels could be achieved through mRNA stabilization and differences in turnover rates, and it seems clear that in addition to the apparent transcript regulation of the hypoxia response, other post-transcriptional and post-translational regulatory mechanisms are active. Further identification of these regulatory mechanisms could identify new targets for gene replacement studies to determine whether these mechanisms are critical for in vivo growth of the fungus during hypoxia adaptation and pathogenesis.

Bottom Line: To survive in the human body, A. fumigatus must adapt to microenvironments that are often characterized by low nutrient and oxygen availability.A concomitant reduction in transcripts was observed with ribosome and terpenoid backbone biosynthesis, TCA cycle, amino acid metabolism and RNA degradation.However, a good correlation overall (R(2) = 0.2, p < 0.05) existed between the transcriptomic and proteomics datasets for all time points.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Immunology and Infectious Disease, Montana State University, Bozeman, MT, USA.

ABSTRACT

Background: 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.

Results: 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 (R(2) = 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.

Conclusions: 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.

Show MeSH
Related in: MedlinePlus