Acid formation in Aspergillus niger is known to be subjected to tight regulation, and the acid production profiles are fine-tuned to respond to the ambient pH. Based on transcriptome data, putative trans-acting pH responding transcription factors were listed and through knock out studies, mutants exhibiting an oxalate overproducing phenotype were identified. The yield of oxalate was increased up to 158% compared to the wild type and the corresponding transcription factor was therefore entitled Oxalic Acid repression Factor, OafA. Detailed physiological characterization of one of the ΔoafA mutants, compared to the wild type, showed that both strains produced substantial amounts of gluconic acid, but the mutant strain was more efficient in re-uptake of gluconic acid and converting it to oxalic acid, particularly at high pH (pH 5.0). Transcriptional profiles showed that 241 genes were differentially expressed due to the deletion of oafA and this supported the argument of OafA being a trans-acting transcription factor. Furthermore, expression of two phosphoketolases was down-regulated in the ΔoafA mutant, one of which has not previously been described in fungi. It was argued that the observed oxalate overproducing phenotype was a consequence of the efficient re-uptake of gluconic acid and thereby a higher flux through glycolysis. This results in a lower flux through the pentose phosphate pathway, demonstrated by the down-regulation of the phosphoketolases. Finally, the physiological data, in terms of the specific oxygen consumption, indicated a connection between the oxidative phosphorylation and oxalate production and this was further substantiated through transcription analysis.

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species.

The release of the genome sequences of two strains of Aspergillus niger has allowed systems-level investigations of this important microbial cell factory. To this end, tools for doing data integration of multi-ome data are necessary, and especially interesting in the context of metabolism. On the basis of an A. niger bibliome survey, we present the largest model reconstruction of a metabolic network reported for a fungal species. The reconstructed gapless metabolic network is based on the reportings of 371 articles and comprises 1190 biochemically unique reactions and 871 ORFs. Inclusion of isoenzymes increases the total number of reactions to 2240. A graphical map of the metabolic network is presented. All levels of the reconstruction process were based on manual curation. From the reconstructed metabolic network, a mathematical model was constructed and validated with data on yields, fluxes and transcription. The presented metabolic network and map are useful tools for examining systemwide data in a metabolic context. Results from the validated model show a great potential for expanding the use of A. niger as a high-yield production platform.

Hek293 cells are the predominant hosts for transient expression of recombinant proteins and are used for stable expression of proteins where post-translational modifications performed by CHO cells are inadequate. Nevertheless, there is little information available on the key cellular features underpinning recombinant protein production in Hek293 cells. To improve our understanding of recombinant protein production in Hek293 cells and identify targets for the engineering of an improved host cell line, we have compared a stable, recombinant protein producing Hek293 cell line and its parental cell line using a combination of transcriptomics, metabolomics and fluxomics. Producer cultures consumed less glucose than non-producer cultures while achieving the same growth rate, despite the additional burden of recombinant protein production. Surprisingly, there was no indication that producer cultures compensated for the reduction in glycolytic energy by increasing the efficiency of glucose utilization or increasing glutamine consumption. In contrast, glutamine consumption was lower and the majority of genes involved in oxidative phosphorylation were downregulated in producer cultures. We observed an overall downregulation of a large number of genes associated with broad cellular functions (e.g., cell growth and proliferation) in producer cultures, and therefore speculate that a broad adaptation of the cellular network freed up resources for recombinant protein production while maintaining the same growth rate. Increased abundance of genes associated with endoplasmic reticulum stress indicated a possible bottleneck at the point of protein folding and assembly.

Yarrowia lipolytica is a dimorphic yeast that efficiently secretes various heterologous proteins and is classified as “generally recognized as safe.” Therefore, it is an attractive protein production host. However, yeasts modify glycoproteins with non-human high mannose-type N-glycans. These structures reduce the protein half-life in vivo and can be immunogenic in man. Here, we describe how we genetically engineered N-glycan biosynthesis in Yarrowia lipolytica so that it produces Man3GlcNAc2 structures on its glycoproteins. We obtained unprecedented levels of homogeneity of this glycanstructure. This is the ideal starting point for building human-like sugars. Disruption of the ALG3 gene resulted in modification of proteins mainly with Man5GlcNAc2 and GlcMan5GlcNAc2 glycans, and to a lesser extent with Glc2Man5GlcNAc2 glycans. To avoid underoccupancy of glycosylation sites, we concomitantly overexpressed ALG6. We also explored several approaches to remove the terminal glucose residues, which hamper further humanization of N-glycosylation; overexpression of the heterodimeric Apergillus niger glucosidase II proved to be the most effective approach. Finally, we overexpressed an α-1,2-mannosidase to obtain Man3GlcNAc2 structures, which are substrates for the synthesis of complex-type glycans. The final Yarrowia lipolytica strain produces proteins glycosylated with the trimannosyl core N-glycan (Man3GlcNAc2), which is the common core of all complex-type N-glycans. All these glycans can be constructed on the obtained trimannosyl N-glycan using either in vivo or in vitro modification with the appropriate glycosyltransferases. The results demonstrate the high potential of Yarrowia lipolytica to be developed as an efficient expression system for the production of glycoproteins with humanized glycans.

Pseudoperonospora cubensis, an oomycete, is the causal agent of cucurbit downy mildew, and is responsible for significant losses on cucurbit crops worldwide. While other oomycete plant pathogens have been extensively studied at the molecular level, Ps. cubensis and the molecular basis of its interaction with cucurbit hosts has not been well examined. Here, we present the first large-scale global gene expression analysis of Ps. cubensis infection of a susceptible Cucumis sativus cultivar, ‘Vlaspik’, and identification of genes with putative roles in infection, growth, and pathogenicity. Using high throughput whole transcriptome sequencing, we captured differential expression of 2383 Ps. cubensis genes in sporangia and at 1, 2, 3, 4, 6, and 8 days post-inoculation (dpi). Additionally, comparison of Ps. cubensis expression profiles with expression profiles from an infection time course of the oomycete pathogen Phytophthora infestans on Solanum tuberosum revealed similarities in expression patterns of 1,576–6,806 orthologous genes suggesting a substantial degree of overlap in molecular events in virulence between the biotrophic Ps. cubensis and the hemi-biotrophic P. infestans. Co-expression analyses identified distinct modules of Ps. cubensis genes that were representative of early, intermediate, and late infection stages. Collectively, these expression data have advanced our understanding of key molecular and genetic events in the virulence of Ps. cubensis and thus, provides a foundation for identifying mechanism(s) by which to engineer or effect resistance in the host.

The oomycete pathogen, Pseudoperonospora cubensis, is the causal agent of downy mildew on cucurbits, and at present, no effective resistance to this pathogen is available in cultivated cucumber (Cucumis sativus). To better understand the host response to a virulent pathogen, we performed expression profiling throughout a time course of a compatible interaction using whole transcriptome sequencing. As described herein, we were able to detect the expression of 15,286 cucumber genes, of which 14,476 were expressed throughout the infection process from 1 day post-inoculation (dpi) to 8 dpi. A large number of genes, 1,612 to 3,286, were differentially expressed in pair-wise comparisons between time points. We observed the rapid induction of key defense related genes, including catalases, chitinases, lipoxygenases, peroxidases, and protease inhibitors within 1 dpi, suggesting detection of the pathogen by the host. Co-expression network analyses revealed transcriptional networks with distinct patterns of expression including down-regulation at 2 dpi of known defense response genes suggesting coordinated suppression of host responses by the pathogen. Comparative analyses of cucumber gene expression patterns with that of orthologous Arabidopsis thaliana genes following challenge with Hyaloperonospora arabidopsidis revealed correlated expression patterns of single copy orthologs suggesting that these two dicot hosts have similar transcriptional responses to related pathogens. In total, the work described herein presents an in-depth analysis of the interplay between host susceptibility and pathogen virulence in an agriculturally important pathosystem.

An ensemble of genetic networks that describe how the model fungal system, Neurospora crassa, utilizes quinic acid (QA) as a sole carbon source has been identified previously. A genetic network for QA metabolism involves the genes, qa-1F and qa-1S, that encode a transcriptional activator and repressor, respectively and structural genes, qa-2, qa-3, qa-4, qa-x, and qa-y. By a series of 4 separate and independent, model-guided, microarray experiments a total of 50 genes are identified as QA-responsive and hypothesized to be under QA-1F control and/or the control of a second QA-responsive transcription factor (NCU03643) both in the fungal binuclear Zn(II)2Cys6 cluster family. QA-1F regulation is not sufficient to explain the quantitative variation in expression profiles of the 50 QA-responsive genes. QA-responsive genes include genes with products in 8 mutually connected metabolic pathways with 7 of them one step removed from the tricarboxylic (TCA) Cycle and with 7 of them one step removed from glycolysis: (1) starch and sucrose metabolism; (2) glycolysis/glucanogenesis; (3) TCA Cycle; (4) butanoate metabolism; (5) pyruvate metabolism; (6) aromatic amino acid and QA metabolism; (7) valine, leucine, and isoleucine degradation; and (8) transport of sugars and amino acids. Gene products both in aromatic amino acid and QA metabolism and transport show an immediate response to shift to QA, while genes with products in the remaining 7 metabolic modules generally show a delayed response to shift to QA. The additional QA-responsive cutinase transcription factor-1β (NCU03643) is found to have a delayed response to shift to QA. The series of microarray experiments are used to expand the previously identified genetic network describing the qa gene cluster to include all 50 QA-responsive genes including the second transcription factor (NCU03643). These studies illustrate new methodologies from systems biology to guide model-driven discoveries about a core metabolic network involving carbon and amino acid metabolism in N. crassa.

Background

Soil fungi face a variety of environmental stresses such as UV light, high temperature, and heavy metals. Adaptation of gene expression through transcriptional regulation is a key mechanism in fungal response to environmental stress. In Saccharomyces cerevisiae, the transcription factors Msn2/4 induce stress-mediated gene expression by binding to the stress response element. Previous studies have demonstrated that the expression of extracellular proteases is up-regulated in response to heat shock in fungi. However, the physiological significance of regulation of these extracellular proteases by heat shock remains unclear. The nematophagous fungus Clonostachys rosea can secret an extracellular serine protease PrC during the infection of nematodes. Since the promoter of prC has three copies of the stress response element, we investigated the effect of environmental stress on the expression of prC.

Methodology/Principal Findings

Our results demonstrated that the expression of prC was up-regulated by oxidants (H2O2 or menadione) and heat shock, most likely through the stress response element. After oxidant treatment or heat shock, the germination of conidia in the wild type strain was significantly higher than that in the prC mutant strain in the presence of nematode cuticle. Interestingly, the addition of nematode cuticle significantly attenuated the production of reactive oxygen species (ROS) induced by oxidants and heat shock in the wild type strain, but not in prC mutant strain. Moreover, low molecule weight (<3 kD) degradation products of nematode cuticle suppressed the inhibitory effect of conidial germination induced by oxidants and heat shock.

Conclusions/Significance

These results indicate that PrC plays a protective role in oxidative stress in C. rosea. PrC degrades the nematode cuticle to produce degradation products, which in turn offer a protective effect against oxidative stress by scavenging ROS. Our study reveals a novel strategy for fungi to adapt to environmental stress.

Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG's regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov.

Glycosylation is a highly complex process to produce a diverse repertoire of cellular glycans that are attached to proteins and lipids. Glycans are involved in fundamental biological processes, including protein folding and clearance, cell proliferation and apoptosis, development, immune responses, and pathogenesis. One of the major types of glycans, N-linked glycans, is formed by sequential attachments of monosaccharides to proteins by a limited number of enzymes. Many of these enzymes can accept multiple N-linked glycans as substrates, thereby generating a large number of glycan intermediates and their intermingled pathways. Motivated by the quantitative methods developed in complex network research, we investigated the large-scale organization of such N-linked glycosylation pathways in mammalian cells. The N-linked glycosylation pathways are extremely modular, and are composed of cohesive topological modules that directly branch from a common upstream pathway of glycan synthesis. This unique structural property allows the glycan production between modules to be controlled by the upstream region. Although the enzymes act on multiple glycan substrates, indicating cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis may be confined within a moderate range by transcription-level control. The findings of the present study provide experimentally-testable predictions for glycosylation processes, and may be applicable to therapeutic glycoprotein engineering.