The Aspergillus fumigatus sterol regulatory element binding protein (SREBP) SrbA belongs to the basic Helix-Loop-Helix (bHLH) family of transcription factors and is crucial for antifungal drug resistance and virulence. The latter phenotype is especially striking, as loss of SrbA results in complete loss of virulence in murine models of invasive pulmonary aspergillosis (IPA). How fungal SREBPs mediate fungal virulence is unknown, though it has been suggested that lack of growth in hypoxic conditions accounts for the attenuated virulence. To further understand the role of SrbA in fungal infection site pathobiology, chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) was used to identify genes under direct SrbA transcriptional regulation in hypoxia. These results confirmed the direct regulation of ergosterol biosynthesis and iron uptake by SrbA in hypoxia and revealed new roles for SrbA in nitrate assimilation and heme biosynthesis. Moreover, functional characterization of an SrbA target gene with sequence similarity to SrbA identified a new transcriptional regulator of the fungal hypoxia response and virulence, SrbB. SrbB co-regulates genes involved in heme biosynthesis and demethylation of C4-sterols with SrbA in hypoxic conditions. However, SrbB also has regulatory functions independent of SrbA including regulation of carbohydrate metabolism. Loss of SrbB markedly attenuates A. fumigatus virulence, and loss of both SREBPs further reduces in vivo fungal growth. These data suggest that both A. fumigatus SREBPs are critical for hypoxia adaptation and virulence and reveal new insights into SREBPs' complex role in infection site adaptation and fungal virulence.

Sterol regulatory element binding proteins (SREBPs) are a class of basic helix-loop-helix transcription factors that regulate diverse cellular responses in eukaryotes. Adding to the recognized importance of SREBPs in human health, SREBPs in the human fungal pathogens Cryptococcus neoformans and Aspergillus fumigatus are required for fungal virulence and susceptibility to triazole antifungal drugs. To date, the exact mechanism(s) behind the role of SREBP in these observed phenotypes is not clear. Here, we report that A. fumigatus SREBP, SrbA, mediates regulation of iron acquisition in response to hypoxia and low iron conditions. To further define SrbA's role in iron acquisition in relation to previously studied fungal regulators of iron metabolism, SreA and HapX, a series of mutants were generated in the ΔsrbA background. These data suggest that SrbA is activated independently of SreA and HapX in response to iron limitation, but that HapX mRNA induction is partially dependent on SrbA. Intriguingly, exogenous addition of high iron or genetic deletion of sreA in the ΔsrbA background was able to partially rescue the hypoxia growth, triazole drug susceptibility, and decrease in ergosterol content phenotypes of ΔsrbA. Thus, we conclude that the fungal SREBP, SrbA, is critical for coordinating genes involved in iron acquisition and ergosterol biosynthesis under hypoxia and low iron conditions found at sites of human fungal infections. These results support a role for SREBP–mediated iron regulation in fungal virulence, and they lay a foundation for further exploration of SREBP's role in iron homeostasis in other eukaryotes.

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.

BackgroundPhaeodactylum tricornutum is a unicellular diatom in the class Bacillariophyceae. The full genome has been sequenced (<30 Mb), and approximately 20 to 30% triacylglyceride (TAG) accumulation on a dry cell basis has been reported under different growth conditions. To elucidate P. tricornutum gene expression profiles during nutrient-deprivation and lipid-accumulation, cell cultures were grown with a nitrate to phosphate ratio of 20:1 (N:P) and whole-genome transcripts were monitored over time via RNA-sequence determination.ResultsThe specific Nile Red (NR) fluorescence (NR fluorescence per cell) increased over time; however, the increase in NR fluorescence was initiated before external nitrate was completely exhausted. Exogenous phosphate was depleted before nitrate, and these results indicated that the depletion of exogenous phosphate might be an early trigger for lipid accumulation that is magnified upon nitrate depletion. As expected, many of the genes associated with nitrate and phosphate utilization were up-expressed. The diatom-specific cyclins cyc7 and cyc10 were down-expressed during the nutrient-deplete state, and cyclin B1 was up-expressed during lipid-accumulation after growth cessation. While many of the genes associated with the C3 pathway for photosynthetic carbon reduction were not significantly altered, genes involved in a putative C4 pathway for photosynthetic carbon assimilation were up-expressed as the cells depleted nitrate, phosphate, and exogenous dissolved inorganic carbon (DIC) levels. P. tricornutum has multiple, putative carbonic anhydrases, but only two were significantly up-expressed (2-fold and 4-fold) at the last time point when exogenous DIC levels had increased after the cessation of growth. Alternative pathways that could utilize HCO3- were also suggested by the gene expression profiles (e.g., putative propionyl-CoA and methylmalonyl-CoA decarboxylases).ConclusionsThe results indicate that P. tricornutum continued carbon dioxide reduction when population growth was arrested and different carbon-concentrating mechanisms were used dependent upon exogenous DIC levels. Based upon overall low gene expression levels for fatty acid synthesis, the results also suggest that the build-up of precursors to the acetyl-CoA carboxylases may play a more significant role in TAG synthesis rather than the actual enzyme levels of acetyl-CoA carboxylases per se. The presented insights into the types and timing of cellular responses to inorganic carbon will help maximize photoautotrophic carbon flow to lipid accumulation.

BackgroundAspergillus 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.ResultsSignificant 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.ConclusionsTaken 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-...

BackgroundTranscriptome analysis was applied to characterize the physiological activities of Pseudomonas aeruginosa grown for three days in drip-flow biofilm reactors. Conventional applications of transcriptional profiling often compare two paired data sets that differ in a single experimentally controlled variable. In contrast this study obtained the transcriptome of a single biofilm state, ranked transcript signals to make the priorities of the population manifest, and compared ranki ngs for a priori identified physiological marker genes between the biofilm and published data sets.ResultsBiofilms tolerated exposure to antibiotics, harbored steep oxygen concentration gradients, and exhibited stratified and heterogeneous spatial patterns of protein synthetic activity. Transcriptional profiling was performed and the signal intensity of each transcript was ranked to gain insight into the physiological state of the biofilm population. Similar rankings were obtained from data sets published in the GEO database http://www.ncbi.nlm.nih.gov/geo. By comparing the rank of genes selected as markers for particular physiological activities between the biofilm and comparator data sets, it was possible to infer qualitative features of the physiological state of the biofilm bacteria. These biofilms appeared, from their transcriptome, to be glucose nourished, iron replete, oxygen limited, and growing slowly or exhibiting stationary phase character. Genes associated with elaboration of type IV pili were strongly expressed in the biofilm. The biofilm population did not indicate oxidative stress, homoserine lactone mediated quorum sensing, or activation of efflux pumps. Using correlations with transcript ranks, the average specific growth rate of biofilm cells was estimated to be 0.08 h-1.ConclusionsCollectively these data underscore the oxygen-limited, slow-growing nature of the biofilm population and are consistent with antimicrobial tolerance due to low metabolic activity.

Understanding of viral assemblage structure in natural environments remains a daunting task. Total viral assemblage sequencing (for example, viral metagenomics) provides a tractable approach. However, even with the availability of next-generation sequencing technology it is usually only possible to obtain a fragmented view of viral assemblages in natural ecosystems. In this study, we applied a network-based approach in combination with viral metagenomics to investigate viral assemblage structure in the high temperature, acidic hot springs of Yellowstone National Park, USA. Our results show that this approach can identify distinct viral groups and provide insights into the viral assemblage structure. We identified 110 viral groups in the hot springs environment, with each viral group likely representing a viral family at the sub-family taxonomic level. Most of these viral groups are previously unknown DNA viruses likely infecting archaeal hosts. Overall, this study demonstrates the utility of combining viral assemblage sequencing approaches with network analysis to gain insights into viral assemblage structure in natural ecosystems.

BackgroundComparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints.ResultsWe used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya), from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints.ConclusionsCombining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell. This approach allows the identification and quantification of those changes, and provides an overview of the evolution of intracellular systems.