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1.  Genome-wide expression analysis of genetic networks in Neurospora crassa 
Bioinformation  2007;1(10):390-395.
The products of five structural genes and two regulatory genes of the qa gene cluster of Neurospora crassa control the metabolism of quinic acid (QA) as a carbon source. A detailed genetic network model of this metabolic process has been reported. This investigation is designed to expand the current model of the QA reaction network. The ensemble method of network identification was used to model RNA profiling data on the qa gene cluster. Through microarray and cluster analysis, genome-wide identification of RNA transcripts associated with quinic acid metabolism in N. crassa is described and suggests a connection to other metabolic circuits. More than 100 genes whose products include carbon metabolism, protein degradation and modification, amino acid metabolism and ribosome synthesis appear to be connected to quinic acid metabolism. The core of the qa gene cluster network is validated with respect to RNA profiling data obtained from microarrays.
PMCID: PMC1896053  PMID: 17597928
genetic networks; quinic acid; qa gene cluster; genome; microarray
2.  Transcriptional comparison of the filamentous fungus Neurospora crassa growing on three major monosaccharides D-glucose, D-xylose and L-arabinose 
Background
D-glucose, D-xylose and L-arabinose are the three major monosaccharides in plant cell walls. Complete utilization of all three sugars is still a bottleneck for second-generation cellulolytic bioethanol production, especially for L-arabinose. However, little is known about gene expression profiles during L-arabinose utilization in fungi and a comparison of the genome-wide fungal response to these three major monosaccharides has not yet been reported.
Results
Using next-generation sequencing technology, we have analyzed the transcriptome of N. crassa grown on L-arabinose versus D-xylose, with D-glucose as the reference. We found that the gene expression profiles on L-arabinose were dramatically different from those on D-xylose. It appears that L-arabinose can rewire the fungal cell metabolic pathway widely and provoke the expression of many kinds of sugar transporters, hemicellulase genes and transcription factors. In contrast, many fewer genes, mainly related to the pentose metabolic pathway, were upregulated on D-xylose. The rewired metabolic response to L-arabinose was significantly different and wider than that under no carbon conditions, although the carbon starvation response was initiated on L-arabinose. Three novel sugar transporters were identified and characterized for their substrates here, including one glucose transporter GLT-1 (NCU01633) and two novel pentose transporters, XAT-1 (NCU01132), XYT-1 (NCU05627). One transcription factor associated with the regulation of hemicellulase genes, HCR-1 (NCU05064) was also characterized in the present study.
Conclusions
We conducted the first transcriptome analysis of Neurospora crassa grown on L-arabinose and performed a comparative analysis with cells grown on D-xylose and D-glucose, which deepens the understanding of the utilization of L-arabinose and D-xylose in filamentous fungi. The dataset generated by this research will be useful for mining target genes for D-xylose and L-arabinose utilization engineering and the novel sugar transportes identified are good targets for pentose untilization and biofuels production. Moreover, hemicellulase production by fungi could be improved by modifying the hemicellulase regulator discovered here.
doi:10.1186/1754-6834-7-31
PMCID: PMC4015282  PMID: 24581151
Neurospora crassa; L-arabinose; RNA-seq; GLT-1; XAT-1; XYT-1; HCR-1
3.  Human NCU-G1 can function as a transcription factor and as a nuclear receptor co-activator 
BMC Molecular Biology  2007;8:106.
Background
Novel, uncharacterised proteins represent a challenge in biochemistry and molecular biology. In this report we present an initial functional characterization of human kidney predominant protein, NCU-G1.
Results
NCU-G1 was found to be a highly conserved nuclear protein rich in proline with a molecular weight of approximately 44 kDa. It is localized on chromosome 1 and consists of 6 exons. Analysis of the amino acid sequence revealed no known transcription activation domains or DNA binding regions, however, four nuclear receptor boxes (LXXLL), and four SH3-interaction motives in addition to numerous potential phosphorylation sites were found. Two nuclear export signals were identified, but no nuclear localization signal. In man, NCU-G1 was found to be widely expressed at the mRNA level with especially high levels detected in prostate, liver and kidney. Electrophoretic mobility shift analysis showed specific binding of NCU-G1 to an oligonucleotide representing the footprint 1 element of the human cellular retinol-binding protein 1 gene promoter. NCU-G1 was found to activate transcription from this promoter and required presence of the footprint 1 element. In transiently transfected Drosophila Schneider S2 cells, we demonstrated that NCU-G1 functions as a co-activator for ligand-activated PPAR-alpha, resulting in an increased expression of a CAT reporter gene under control of the peroxisome proliferator-activated receptor-alpha responsive acyl-CoA oxidase promoter.
Conclusion
We propose that NCU-G1 is a dual-function protein capable of functioning as a transcription factor as well as a nuclear receptor co-activator.
doi:10.1186/1471-2199-8-106
PMCID: PMC2233640  PMID: 18021396
4.  Metabolomic and transcriptomic stress response of Escherichia coli 
GC-MS-based analysis of the metabolic response of Escherichia coli exposed to four different stress conditions reveals reduction of energy expensive pathways.Time-resolved response of E. coli to changing environmental conditions is more specific on the metabolite as compared with the transcript level.Cease of growth during stress response as compared with stationary phase response invokes similar transcript but dissimilar metabolite responses.Condition-dependent associations between metabolites and transcripts are revealed applying co-clustering and canonical correlation analysis.
The response of biological systems to environmental perturbations is characterized by a fast and appropriate adjusting of physiology on every level of the cellular and molecular network.
Stress response is usually represented by a combination of both specific responses, aimed at minimizing deleterious effects or repairing damage (e.g. protein chaperones under temperature stress) and general responses which, in part, comprise the downregulation of genes related to translation and ribosome biogenesis. This in turn is reflected by growth cessation or reduction observed under essentially all stress conditions and is an important strategy to adjust cellular physiology to the new condition.
E. coli has been intensively investigated in relation to stress responses. Thus far, however, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. To better understand system response to perturbation, we designed a time-resolved experiment to compare and integrate metabolic and transcript changes of E. coli using four stress conditions including non-lethal temperature shifts, oxidative stress, and carbon starvation relative to cultures grown under optimal conditions covering both states before and directly after stress application, resumption of growth after stress-induced lag phase, and finally the stationary phase.
Metabolic changes occurring after stress application were characterized by a reduction in metabolites of central metabolism (TCA cycle and glycolysis) as well as an increase in free amino acids. Whereas the latter is probably due to protein degradation and stalling of translation, the former supports and extends conclusions based on transcriptome data demonstrating a major decrease in energy-consuming processes as a general stress response. Further comparative analysis of the response on the metabolome and transcriptome, however, revealed in addition to these similarities major differences. Thus, the response on the metabolome displayed a significantly higher specificity towards the specific stress as compared with the transcriptome. Further, when comparing the metabolome of cells ceasing growth due to stress application with cells ceasing growth due to reaching stationary phase the metabolome response differed to a significant extent between both growth arrest phases, whereas the transcriptome response showed significant overlap again, suggesting that the response of E. coli on the metabolome level displays a higher level of significance as compared with the transcriptome level.
Subsequently, both data sets were jointly analyzed using co-clustering and canonical correlation approaches to identify coordinated changes on the transcriptome and the metabolite level indicative metabolite–transcript associations. A first outcome of this study was that no association was preserved during all conditions analyzed but rather condition-specific associations were observed. One set of associations found was between metabolites from the oxidative pentose phosphate pathway such as glc-6-P, 6-P-gluconic acid, ribose-5-P, and E-4-P and metabolites from the glycolytic pathway (3PGA and PEP in addition to glc-6-P with two genes encoding pathway enzymes, that is rpe encoding ribulose phosphate 3-epimerase and pps encoding PEP synthase.
A second example comprises metabolites of the TCA cycle such as pyruvic acid, 2-ketoglutaric acid, fumaric acid, malic acid, and succinic acid and the mqo gene encoding malate-quinone oxidoreductase (MQO). MQO catalyses the irreversible oxidation of malate to oxaloacetate that in turn regulates the activity of citrate synthase, which is a major rate determining enzyme of the TCA cycle. The strong association between mqo gene expression and multiple members of the TCA cycle as well as pyruvate suggest mqo expression to have a major function for the regulation of the TCA cycle, which need to be experimentally validated.
Multiple further associations identified show on the one hand the power of integrative systems oriented approaches for developing new hypothesis, on the other hand their condition-dependent behavior shows the extreme flexibility of the biological systems studied thus requesting a much more intense effort toward parallel analysis of biological systems under several environmental conditions.
Environmental fluctuations lead to a rapid adjustment of the physiology of Escherichia coli, necessitating changes on every level of the underlying cellular and molecular network. Thus far, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. Here, we incorporate the metabolite composition together with gene expression data to provide a more comprehensive insight on system level stress adjustments by describing detailed time-resolved E. coli response to five different perturbations (cold, heat, oxidative stress, lactose diauxie, and stationary phase). The metabolite response is more specific as compared with the general response observed on the transcript level and is reflected by much higher specificity during the early stress adaptation phase and when comparing the stationary phase response to other perturbations. Despite these differences, the response on both levels still follows the same dynamics and general strategy of energy conservation as reflected by rapid decrease of central carbon metabolism intermediates coinciding with downregulation of genes related to cell growth. Application of co-clustering and canonical correlation analysis on combined metabolite and transcript data identified a number of significant condition-dependent associations between metabolites and transcripts. The results confirm and extend existing models about co-regulation between gene expression and metabolites demonstrating the power of integrated systems oriented analysis.
doi:10.1038/msb.2010.18
PMCID: PMC2890322  PMID: 20461071
Escherichia coli; metabolomic; response to stress; time course; transcriptomic
5.  Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge 
By recruiting the essential HIS3 gene to the GAL regulatory system and switching to a repressing glucose medium, we confronted yeast cells with a novel challenge they had not encountered before along their history in evolution.Adaptation to this challenge involved a global transcriptional response of a sizeable fraction of the genome, which relaxed on the time scale of the population adaptation, of order of 10 generations.For a large fraction of the responding genes there is no simple biological interpretation, connecting them to the specific cellular demands imposed by the novel challenge.Strikingly, repeating the experiment did not reproduce similar transcription patterns neither in the transient phase nor in the adapted state in glucose.These results suggest that physiological selection operates on the new metabolic configurations generated by the non-specific large scale transcriptional response to eventually stabilize an adaptive state.
Cells adjust their transcriptional state to accommodate environmental and genetic perturbations. Some common perturbations, such as changes in nutrient composition, elicit well-characterized transcriptional responses that can be understood by simple engineering-like design principles as satisfying specific demands imposed by the perturbation. However, cells also have the ability to adapt to novel and unforeseen challenges. This ability is central in realizing the evolvability potential of cells as they respond to dramatic genetic or environmental changes along evolution. Little is known about the mechanisms underlying such adaptations to novel challenges; in particular, the role of the transcriptional regulatory network in such adaptations has not been characterized. Genome-wide measurements have revealed that, in many cases, perturbations lead to a global transcriptional response involving a sizeable fraction of the genome (Gasch et al, 2000; Jelinsky et al, 2000; Causton et al, 2001; Ideker et al, 2001; Lai et al, 2005). Such global behavior suggests that general collective properties of the genetic network, rather than specific pre-designed pathways, determine an important part of the transcriptional response. It is not known however what fraction of genes within such massive transcriptional responses is essential to the specific cellular demands. It is also unknown whether the non-pre-designed part of the response can have a functional role in adaptation to novel challenges.
To study these questions, we confronted yeast cells with a novel challenge they had not encountered before along their history in evolution. A strain of the yeast Saccharomyces cerevisiae was engineered to recruit the gene HIS3, an essential enzyme from the histidine biosynthesis pathway (Hinnebusch, 1992), to the GAL regulatory system, responsible for galactose utilization (Stolovicki et al, 2006). The GAL system is known to be strongly repressed when the cells are exposed to glucose. Therefore, upon switching to a medium containing glucose and lacking histidine, the GAL system and with it HIS3 are highly repressed immediately following the switch and the cells encounter a severe challenge. We have recently shown that a cell population carrying this rewired genome can adapt to grow competitively in a chemostat in a medium containing pure glucose (Stolovicki et al, 2006). This adaptation occurred on a timescale of ∼10 generations; applying a stronger environmental pressure in the form of a competitive inhibitor to HIS3 (3AT) resulted in a similar adaptation albeit with a longer timescale. Figure 1 shows the dynamics of the population's cell density (blue lines, measured by OD) following a medium switch from galactose to glucose in the chemostat without (A) and with (B) 3AT. The experiments revealed that adaptation occurs on physiological timescales (much shorter than required by spontaneous random mutations), but the mechanisms underlying this adaptation have remained unclear (Stolovicki et al, 2006).
Yeast cells had not encountered recruitment of HIS3 to the GAL system along their evolutionary history, and their genome could not possibly have been selected to specifically address glucose repression of HIS3. This experiment, therefore, provides a unique opportunity to characterize the spontaneous transcriptional response during adaptation to a novel challenge and to assess the functional role of the regulatory system in this adaptation. We used DNA microarrays to measure the genome-wide expression levels at time points along the adaptation process, with and without 3AT. These measurements revealed that a sizeable fraction of the genome responded by induction or repression to the switch into glucose. Superimposed on the OD traces, Figure 1 shows the results of a clustering analysis of the expression of genes as measured by the arrays along time in the experiments. This analysis revealed two dominant clusters, each containing hundreds of genes in each experiment, which responded to the medium switch to glucose by a strong transient induction or repression followed by relaxation to steady state on the timescale of the adaptation process, ∼ 10 generations. The two clusters in each experiment show similar but opposite dynamics.
A detailed analysis of the gene content in the two clusters revealed that only a small portion of the response was induced by a change in carbon source (15% overlap between the corresponding clusters in the two experiments, with and without 3AT). Moreover, it revealed a very low overlap with the universal stress response observed for a wide range of environmental stresses (Gasch et al, 2000; Causton et al, 2001) and with the typical response to amino-acid starvation (Natarajan et al, 2001). Additionally, all known specific responses to stress in the literature are characterized by transient induction or repression with relaxation to steady state within a generation time (Gasch et al, 2000; Koerkamp et al, 2002; Wu et al, 2004), whereas in our experiments relaxation of the transcriptional response occurs over many generations. Taken together, these results show that the transcriptional response observed here is neither a metabolic response to the change in carbon source nor is it a standard response to stress or amino-acid starvation. This raises the possibility that it is a spontaneous collective response that is largely composed of genes that do not have a specific function. This possibility was tested directly by repeating the experiment with different populations and comparing their responses. This procedure revealed reproducible adaptation dynamics and steady states in terms of population density, but showed significantly different transcriptional transient responses and steady states for the two repeated experiments. Thus, a significant portion of the genes that changed their expression during the adaptation process do not have a well-defined and reproducible function in the challenging environment.
The application of a stronger environmental pressure in the form of 3AT had a dramatic effect on the global characteristics of the transcriptional response: it induced a markedly higher correlation among the hundreds of responding genes. Figure 3A compares the array data in color code for the two experiments. It is seen that the emergent pattern of transcription exhibits a higher degree of order by the introduction of high external pressure. Observation of the transcriptional patterns for specific metabolic pathways illustrates the different contributions to the correlated dynamics (Figure 3B–D). A general energetic module such as glycolysis exhibited similar patterns of induction and relaxation in experiments with and without 3AT (Figure 3B). However, in general, we found that more than one-third of the known metabolic modules (30 out of 88 modules described in KEGG) exhibited high expression correlation among their genes when the environmental pressure was high but not when it was low. As an example, Figure 3C shows the histidine biosynthesis pathway and Figure 3D the purine pathway. Note the highly ordered trajectories in the lower panels (with 3AT) compared to the disordered ones in the upper panels (no 3AT). This order extends also between genes belonging to different and even distant metabolic modules. It indicates that a global transcriptional regulatory mechanism is in operation, rather than a local specific one. Surprisingly, genes belonging to the same metabolic pathway exhibited simultaneous positively and negatively correlated dynamics. Thus, an important conclusion of this work is that the global transcriptional response to a novel challenge cannot be explained by a simple cellular or metabolic logic. This is to be expected if the response had not been specifically selected in evolution and was not pre-designed for the challenge.
Our data clearly reveal that the massive transcriptional response underlies the adaptation process to a novel challenge. The novelty of the challenge presented to the cells excludes the possibility that this response has been specifically selected toward this challenge. Thus, transcriptional regulation has dynamic properties resulting in a general massive nonspecific response to a novel perturbation. Such a response in turn allows for metabolic rearrangements, which by feeding back on transcription lead to adaptation of the cells to the unforeseen situation. The drastic change in the expression state of the cell opens multiple new metabolic pathways. Physiological selection works then on these multiple metabolic pathways to stabilize an adaptive state that causes relaxation of the perturbed expression pattern. This scenario, involving the creation of a library of possibilities and physiological selection over this library, is compatible with our understanding of a broad class of biological systems, placing the cellular metabolic/regulatory networks on the same footing as the neural or the immune systems (Gerhart and Kirschner, 1997).
Cells adjust their transcriptional state to accommodate environmental and genetic perturbations. An open question is to what extent transcriptional response to perturbations has been specifically selected along evolution. To test the possibility that transcriptional reprogramming does not need to be ‘pre-designed' to lead to an adaptive metabolic state on physiological timescales, we confronted yeast cells with a novel challenge they had not previously encountered. We rewired the genome by recruiting an essential gene, HIS3, from the histidine biosynthesis pathway to a foreign regulatory system, the GAL network responsible for galactose utilization. Switching medium to glucose in a chemostat caused repression of the essential gene and presented the cells with a severe challenge to which they adapted over approximately 10 generations. Using genome-wide expression arrays, we show here that a global transcriptional reprogramming (>1200 genes) underlies the adaptation. A large fraction of the responding genes is nonreproducible in repeated experiments. These results show that a nonspecific transcriptional response reflecting the natural plasticity of the regulatory network supports adaptation of cells to novel challenges.
doi:10.1038/msb4100147
PMCID: PMC1865588  PMID: 17453047
adaptation; cellular metabolism; expression arrays; plasticity; transcriptional response
6.  Exploring the bZIP transcription factor regulatory network in Neurospora crassa 
Microbiology  2011;157(Pt 3):747-759.
Transcription factors (TFs) are key nodes of regulatory networks in eukaryotic organisms, including filamentous fungi such as Neurospora crassa. The 178 predicted DNA-binding TFs in N. crassa are distributed primarily among six gene families, which represent an ancient expansion in filamentous ascomycete genomes; 98 TF genes show detectable expression levels during vegetative growth of N. crassa, including 35 that show a significant difference in expression level between hyphae at the periphery versus hyphae in the interior of a colony. Regulatory networks within a species genome include paralogous TFs and their respective target genes (TF regulon). To investigate TF network evolution in N. crassa, we focused on the basic leucine zipper (bZIP) TF family, which contains nine members. We performed baseline transcriptional profiling during vegetative growth of the wild-type and seven isogenic, viable bZIP deletion mutants. We further characterized the regulatory network of one member of the bZIP family, NCU03905. NCU03905 encodes an Ap1-like protein (NcAp-1), which is involved in resistance to multiple stress responses, including oxidative and heavy metal stress. Relocalization of NcAp-1 from the cytoplasm to the nucleus was associated with exposure to stress. A comparison of the NcAp-1 regulon with Ap1-like regulons in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Aspergillus fumigatus showed both conservation and divergence. These data indicate how N. crassa responds to stress and provide information on pathway evolution.
doi:10.1099/mic.0.045468-0
PMCID: PMC3081083  PMID: 21081763
7.  Transcription Factor CCG-8 as a New Regulator in the Adaptation to Antifungal Azole Stress 
Antifungal azoles are widely used for controlling fungal infections. Fungi are able to change the expression of many genes when they adapt to azole stress, and increased expression of some of these genes can elevate resistance to azoles. However, the regulatory mechanisms behind transcriptional adaption to azoles in filamentous fungi are poorly understood. In this study, we found that deletion of the transcription factor gene ccg-8, which is known to be a clock-controlled gene, made Neurospora crassa hypersensitive to azoles. A comparative genome-wide analysis of the responses to ketoconazole of the wild type and the ccg-8 mutant revealed that the transcriptional responses to ketoconazole of 78 of the 488 transcriptionally ketoconazole-upregulated genes and the 427 transcriptionally ketoconazole-downregulated genes in the wild type were regulated by CCG-8. Ketoconazole sensitivity testing of all available knockout mutants for CCG-8-regulated genes revealed that CCG-8 contributed to azole adaption by regulating the ketoconazole responses of many genes, including the target gene (erg11), an azole transporter gene (cdr4), a hexose transporter gene (hxt13), a stress response gene (locus number NCU06317, named kts-1), two transcription factor genes (NCU01386 [named kts-2] and fsd-1/ndt80), four enzyme-encoding genes, and six unknown-function genes. CCG-8 also regulated phospholipid synthesis in N. crassa in a manner similar to that of its homolog in Saccharomyces cerevisiae, Opi1p. However, there was no cross talk between phospholipid synthesis and azole resistance in N. crassa. CCG-8 homologs are conserved and are common in filamentous fungi. Deletion of the CCG-8 homolog-encoding gene in Fusarium verticillioides (Fvccg-8) also made this fungus hypersensitive to antifungal azoles.
doi:10.1128/AAC.02244-13
PMCID: PMC3957830  PMID: 24342650
8.  Transcriptional Profiling of Cross Pathway Control in Neurospora crassa and Comparative Analysis of the Gcn4 and CPC1 Regulons▿ †  
Eukaryotic Cell  2007;6(6):1018-1029.
Identifying and characterizing transcriptional regulatory networks is important for guiding experimental tests on gene function. The characterization of regulatory networks allows comparisons among both closely and distantly related species, providing insight into network evolution, which is predicted to correlate with the adaptation of different species to particular environmental niches. One of the most intensely studied regulatory factors in the yeast Saccharomyces cerevisiae is the bZIP transcription factor Gcn4p. Gcn4p is essential for a global transcriptional response when S. cerevisiae experiences amino acid starvation. In the filamentous ascomycete Neurospora crassa, the ortholog of GCN4 is called the cross pathway control-1 (cpc-1) gene; it is required for the ability of N. crassa to induce a number of amino acid biosynthetic genes in response to amino acid starvation. Here, we deciphered the CPC1 regulon by profiling transcription in wild-type and cpc-1 mutant strains with full-genome N. crassa 70-mer oligonucleotide microarrays. We observed that at least 443 genes were direct or indirect CPC1 targets; these included 67 amino acid biosynthetic genes, 16 tRNA synthetase genes, and 13 vitamin-related genes. Comparison among the N. crassa CPC1 transcriptional profiling data set and the Gcn4/CaGcn4 data sets from S. cerevisiae and Candida albicans revealed a conserved regulon of 32 genes, 10 of which are predicted to be directly regulated by Gcn4p/CPC1. The 32-gene conserved regulon comprises mostly amino acid biosynthetic genes. The comparison of regulatory networks in species with clear orthology among genes sheds light on how gene interaction networks evolve.
doi:10.1128/EC.00078-07
PMCID: PMC1951524  PMID: 17449655
9.  Unravelling the molecular basis for light modulated cellulase gene expression - the role of photoreceptors in Neurospora crassa 
BMC Genomics  2012;13:127.
Background
Light represents an important environmental cue, which exerts considerable influence on the metabolism of fungi. Studies with the biotechnological fungal workhorse Trichoderma reesei (Hypocrea jecorina) have revealed an interconnection between transcriptional regulation of cellulolytic enzymes and the light response. Neurospora crassa has been used as a model organism to study light and circadian rhythm biology. We therefore investigated whether light also regulates transcriptional regulation of cellulolytic enzymes in N. crassa.
Results
We show that the N. crassa photoreceptor genes wc-1, wc-2 and vvd are involved in regulation of cellulase gene expression, indicating that this phenomenon is conserved among filamentous fungi. The negative effect of VVD on production of cellulolytic enzymes is thereby accomplished by its role in photoadaptation and hence its function in White collar complex (WCC) formation. In contrast, the induction of vvd expression by the WCC does not seem to be crucial in this process. Additionally, we found that WC-1 and WC-2 not only act as a complex, but also have individual functions upon growth on cellulose.
Conclusions
Genome wide transcriptome analysis of photoreceptor mutants and evaluation of results by analysis of mutant strains identified several candidate genes likely to play a role in light modulated cellulase gene expression. Genes with functions in amino acid metabolism, glycogen metabolism, energy supply and protein folding are enriched among genes with decreased expression levels in the wc-1 and wc-2 mutants. The ability to properly respond to amino acid starvation, i. e. up-regulation of the cross pathway control protein cpc-1, was found to be beneficial for cellulase gene expression. Our results further suggest a contribution of oxidative depolymerization of cellulose to plant cell wall degradation in N. crassa.
doi:10.1186/1471-2164-13-127
PMCID: PMC3364853  PMID: 22462823
10.  Oncogenic K-Ras decouples glucose and glutamine metabolism to support cancer cell growth 
A systems approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling was applied to investigate the role of oncogenic K-Ras in metabolic transformation.K-Ras transformed cells exhibit an increased glycolytic rate and lower flux through the oxidative tricarboxylic acid (TCA) cycle.K-Ras transformed cells show a relative increase in glutamine anaplerosis and reductive TCA metabolism.Transcriptional changes driven by oncogenic K-Ras suggest control nodes associated with the metabolic reprogramming of cancer cells.
The ras and myc oncogenes drive pleiotropic changes in cell signaling, nutrient uptake, and intracellular metabolism (Chiaradonna et al, 2006b; Yuneva et al, 2007; Wise et al, 2008; Vander Heiden et al, 2009). Mutated ras proteins, identified in 25% of human cancers (Bos, 1989; Downward, 2003), correlate with an increased rate of glucose consumption, lactate accumulation, altered expression of mitochondrial genes, increased ROS production, and reduced mitochondrial activity (Bos, 1989; Downward, 2003; Vizan et al, 2005; Chiaradonna et al, 2006a; Yun et al, 2009; Baracca et al, 2010; Weinberg et al, 2010). Furthermore, K-Ras transformed cancer cells are dependent upon glucose and glutamine availability, since their withdrawal induces apoptosis and cell-cycle arrest, respectively (Ramanathan et al, 2005; Telang et al, 2006; Yun et al, 2009). However, the precise metabolic effects downstream of oncogenic Ras signaling as well as the mechanisms by which intracellular glucose and glutamine metabolism change have not been completely elucidated.
In this report, we have investigated the reprogramming of central carbon metabolism in cancer cells and its regulation by the K-ras oncogene, applying a systems level approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling. These data reveal a coordinated decoupling of glycolysis and the tricarboxylic acid (TCA) cycle. K-Ras transformed mouse and human cells exhibited a high glucose to lactate flux and relatively lower oxidative metabolism of pyruvate. Such changes were supported by increased expression of glycolytic genes as well as several pyruvate dehydrogenase kinases. In contrast to glucose, the contribution of glutamine carbon to TCA cycle intermediates through both oxidative and reductive metabolism was significantly increased upon K-Ras transformation. Despite this increase in glutamine anaplerosis, oxidative TCA flux was significantly decreased. Additionally, we observed elevated levels of glutamine-derived nitrogen in various biosynthetic metabolites in transformed cells, including amino acids, 5-oxoproline, and the nucleobase adenine. Consistent with these changes, we detected increased transcription of genes associated with glutamine metabolism and nucleotide biosynthesis in cells expressing oncogenic K-Ras.
Taken together, these findings indicate an important role of oncogenic K-Ras in cancer cell metabolism. The observed decoupling of glucose and glutamine metabolism enables the efficient utilization of both carbon and nitrogen from glutamine for biosynthetic processes. In accord with these alterations, oncogenic K-Ras induces gene expression changes that may drive this metabolic reprogramming. Finally, these results may enable the identification of metabolic and transcriptional targets throughout the network and allow more effective cancer therapies.
Oncogenes such as K-ras mediate cellular and metabolic transformation during tumorigenesis. To analyze K-Ras-dependent metabolic alterations, we employed 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD) of 15N-labeled glutamine, and transcriptomic profiling in mouse fibroblast and human carcinoma cell lines. Stable isotope-labeled glucose and glutamine tracers and computational determination of intracellular fluxes indicated that cells expressing oncogenic K-Ras exhibited enhanced glycolytic activity, decreased oxidative flux through the tricarboxylic acid (TCA) cycle, and increased utilization of glutamine for anabolic synthesis. Surprisingly, a non-canonical labeling of TCA cycle-associated metabolites was detected in both transformed cell lines. Transcriptional profiling detected elevated expression of several genes associated with glycolysis, glutamine metabolism, and nucleotide biosynthesis upon transformation with oncogenic K-Ras. Chemical perturbation of enzymes along these pathways further supports the decoupling of glycolysis and TCA metabolism, with glutamine supplying increased carbon to drive the TCA cycle. These results provide evidence for a role of oncogenic K-Ras in the metabolic reprogramming of cancer cells.
doi:10.1038/msb.2011.56
PMCID: PMC3202795  PMID: 21847114
cancer; metabolic flux analysis; metabolism; Ras; transcriptional analysis
11.  The Aspergillus nidulans Zn(II)2Cys6 transcription factor AN5673/RhaR mediates L-rhamnose utilization and the production of α-L-rhamnosidases 
Microbial Cell Factories  2014;13(1):161.
Background
Various plant-derived substrates contain L-rhamnose that can be assimilated by many fungi and its liberation is catalyzed by α-L-rhamnosidases. Initial data obtained in our laboratory focussing on two Aspergillus nidulans α-L-rhamnosidase genes (rhaA and rhaE) showed α-L-rhamnosidase production to be tightly controlled at the level of transcription by the carbon source available. Whilst induction is effected by L-rhamnose, unlike many other glycosyl hydrolase genes repression by glucose and other carbon sources occurs in a manner independent of CreA. To date regulatory genes affecting L-rhamnose utilization and the production of enzymes that yield L-rhamnose as a product have not been identified in A. nidulans. The purpose of the present study is to characterize the corresponding α-L-rhamnosidase transactivator.
Results
In this study we have identified the rhaR gene in A. nidulans and Neurospora crassa (AN5673, NCU9033) encoding a putative Zn(II)2Cys6 DNA-binding protein. Genetic evidence indicates that its product acts in a positive manner to induce transcription of the A. nidulans L-rhamnose regulon. rhaR-deleted mutants showed reduced ability to induce expression of the α-L-rhamnosidase genes rhaA and rhaE and concomitant reduction in α-L-rhamnosidase production. The rhaR deletion phenotype also results in a significant reduction in growth on L-rhamnose that correlates with reduced expression of the L-rhamnonate dehydratase catabolic gene lraC (AN5672). Gel mobility shift assays revealed RhaR to be a DNA binding protein recognizing a partially symmetrical CGG-X11-CCG sequence within the rhaA promoter. Expression of rhaR alone is insufficient for induction since its mRNA accumulates even in the absence of L-rhamnose, therefore the presence of both functional RhaR and L-rhamnose are absolutely required. In N. crassa, deletion of rhaR also impairs growth on L-rhamnose.
Conclusions
To define key elements of the L-rhamnose regulatory circuit, we characterized a DNA-binding Zn(II)2Cys6 transcription factor (RhaR) that regulates L-rhamnose induction of α-L-rhamnosidases and the pathway for its catabolism in A. nidulans, thus extending the list of fungal regulators of genes encoding plant cell wall polysaccharide degrading enzymes. These findings can be expected to provide valuable information for modulating α-L-rhamnosidase production and L-rhamnose utilization in fungi and could eventually have implications in fungal pathogenesis and pectin biotechnology.
Electronic supplementary material
The online version of this article (doi:10.1186/s12934-014-0161-9) contains supplementary material, which is available to authorized users.
doi:10.1186/s12934-014-0161-9
PMCID: PMC4245848  PMID: 25416526
α-L-rhamnosidases; Aspergillus nidulans; AN5673/rhaR; L-rhamnose metabolic pathway; LRA gene cluster; AN5672/lraC; Neurospora crassa; NCU9033/rhaR; non-radioactive EMSA; TF knockout
12.  Circadian Activation of the Mitogen-Activated Protein Kinase MAK-1 Facilitates Rhythms in Clock-Controlled Genes in Neurospora crassa 
Eukaryotic Cell  2013;12(1):59-69.
The circadian clock regulates the expression of many genes involved in a wide range of biological functions through output pathways such as mitogen-activated protein kinase (MAPK) pathways. We demonstrate here that the clock regulates the phosphorylation, and thus activation, of the MAPKs MAK-1 and MAK-2 in the filamentous fungus Neurospora crassa. In this study, we identified genetic targets of the MAK-1 pathway, which is homologous to the cell wall integrity pathway in Saccharomyces cerevisiae and the extracellular signal-regulated kinase 1/2 (ERK1/2) pathway in mammals. When MAK-1 was deleted from Neurospora cells, vegetative growth was reduced and the transcript levels for over 500 genes were affected, with significant enrichment for genes involved in protein synthesis, biogenesis of cellular components, metabolism, energy production, and transcription. Additionally, of the ∼500 genes affected by the disruption of MAK-1, more than 25% were previously identified as putative clock-controlled genes. We show that MAK-1 is necessary for robust rhythms of two morning-specific genes, i.e., ccg-1 and the mitochondrial phosphate carrier protein gene NCU07465. Additionally, we show clock regulation of a predicted chitin synthase gene, NCU04352, whose rhythmic accumulation is also dependent upon MAK-1. Together, these data establish a role for the MAK-1 pathway as an output pathway of the circadian clock and suggest a link between rhythmic MAK-1 activity and circadian control of cellular growth.
doi:10.1128/EC.00207-12
PMCID: PMC3535850  PMID: 23125351
13.  Coordinations between gene modules control the operation of plant amino acid metabolic networks 
BMC Systems Biology  2009;3:14.
Background
Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results.
Results
Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes.
Conclusion
Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.
doi:10.1186/1752-0509-3-14
PMCID: PMC2646696  PMID: 19171064
14.  Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast 
While typically many expression levels change in transcription factor mutants, only few of these changes lead to functional changes. The predictive capability of expression and DNA binding data for such functional changes in metabolism is very limited.Large-scale 13C-flux data reveal the condition specificity of transcriptional control of metabolic function.Transcription control in yeast focuses on the switch between respiration and fermentation.Follow-up modeling on the basis of transcriptomics and proteomics data suggest the newly discovered Gcn4 control of respiration to be mediated via PKA and Snf1.
Effective control and modulation of cellular behavior is of paramount importance in medicine (Kreeger and Lauffenburger, 2010) and biotechnology (Haynes and Silver, 2009), and requires profound understanding of control mechanisms. In this study, we aim to elucidate the extent to which transcription factors control the operation of yeast metabolism. As a quantitative readout of metabolic function, we monitored the traffic of small molecules through various pathways of central metabolism by 13C-flux analysis (Sauer, 2006). The choosen growth conditions represent two different regulatory states of reduced (galactose) and maximal carbon source repression (glucose), as well as a different nitrogen metabolism and two common, permanent stress conditions.
Depending on the growth condition, between 7 and 13% of the deleted transcription factors altered the determined flux ratios (Figure 3). Of the six quantified flux ratios, only the glycolysis/pentose phosphate pathway split, and the convergent ratio of anaplerosis and TCA cycle were controlled by the deleted transcription factors. Thus, we concluded that 23 transcription factors control flux distributions under at least one of the tested growth conditions, leading to 42 condition-dependent interactions of transcription factors with metabolic pathway activity (Figure 4). With two exceptions, all other identified transcription factors interactions controlled the TCA cycle flux. This condition-specific active control of metabolic function could not have been predicted from DNA binding and expression data; that is, 26.1% false negatives, 48.6% true positives.
Of the 23 transcription factors that controlled TCA cycle flux distributions under the tested conditions, only Bas1, Gcn4, Gcr2 and Pho2 exerted control under more than one condition. We identified Cit1, Mdh1 and Idh1/2 with a proteomics approach as the relevant target enzyme that increase the TCA cycle flux. Next, we asked whether Bas1, Gcr2, Gcn4 and Pho2 act directly on the TCA cycle or mediate their effect indirectly. Based on the transcriptomics data, the pattern of differentially activated transcription factors inferred by the differential expression of their target genes suggested reduced glucose repression in all four mutants as the common mechanism.
Starting from the currently largest set of 13C-based flux distributions, we identified networks of individual transcription factors that control metabolic pathway activity. These networks of active metabolic control have the following properties. First, they are highly condition dependent, as at most four transcription factors control the same metabolic flux distribution under more than one growth conditions. Second, they focus almost exclusively on the TCA cycle, thereby controlling the switch between respiratory and fermentative metabolism. Third, with four to 14 active transcription factors, they are small compared with gene regulation networks that were obtained from expression and DNA binding data. For the metabolic network studied here, robustness is also apparent from the fact that upregulated TCA cycle fluxes were not sufficient to achieve full respiratory metabolism; that is, absent or low ethanol formation. Several explanations could potentially explain the observed robustness. The most likely explanation is that environmental signals might be transmitted by different signaling pathways to several transcription factors, whose orchestrated action on multiple target genes is necessary to achieve a functional flux response. This hypothesis would explain why several transcription factors exert flux effects on the same pathway, but each flux effect is relatively small, as further, coordinated manipulations would be necessary to further improve the respiratory flux. Our findings demonstrate the importance of identifying and quantifying the extent to which regulatory effectors alter cellular function.
Which transcription factors control the distribution of metabolic fluxes under a given condition? We address this question by systematically quantifying metabolic fluxes in 119 transcription factor deletion mutants of Saccharomyces cerevisiae under five growth conditions. While most knockouts did not affect fluxes, we identified 42 condition-dependent interactions that were mediated by a total of 23 transcription factors that control almost exclusively the cellular decision between respiration and fermentation. This relatively sparse, condition-specific network of active metabolic control contrasts with the much larger gene regulation network inferred from expression and DNA binding data. Based on protein and transcript analyses in key mutants, we identified three enzymes in the tricarboxylic acid cycle as the key targets of this transcriptional control. For the transcription factor Gcn4, we demonstrate that this control is mediated through the PKA and Snf1 signaling cascade. The discrepancy between flux response predictions, based on the known regulatory network architecture and our functional 13C-data, demonstrates the importance of identifying and quantifying the extent to which regulatory effectors alter cellular functions.
doi:10.1038/msb.2010.91
PMCID: PMC3010106  PMID: 21119627
metabolic flux; omics data; regulatory network; transcription factor; transcriptional regulation
15.  Transcription of the Neurospora crassa 70-kDa class heat shock protein genes is modulated in response to extracellular pH changes 
Cell Stress & Chaperones  2009;15(2):225-231.
Heat shock proteins belong to a conserved superfamily of molecular chaperones found in prokaryotes and eukaryotes. These proteins are linked to a myriad of physiological functions. In this study, we show that the N. crassa hsp70-1 (NCU09602.3) and hsp70-2 (NCU08693.3) genes are preferentially expressed in an acidic milieu after 15 h of cell growth in sufficient phosphate at 30°C. No significant accumulation of these transcripts was detected at alkaline pH values. Both genes accumulated to a high level in mycelia that were incubated for 1 h at 45°C, regardless of the phosphate concentration and extracellular pH changes. Transcription of the hsp70-1 and hsp70-2 genes was dependent on the pacC+ background in mycelia cultured under optimal growth conditions or at 45°C. The pacC gene encodes a Zn-finger transcription factor that is involved in the regulation of gene expression by pH. Heat shock induction of these two hsp genes in mycelia incubated in low-phosphate medium was almost not altered in the nuc-1− background under both acidic and alkaline pH conditions. The NUC-1 transcriptional regulator is involved in the derepression of nucleases, phosphatases, and transporters that are necessary for fulfilling the cell's phosphate requirements. Transcription of the hsp70-3 (NCU01499.3) gene followed a different pattern of induction—the gene was depressed under insufficient phosphate conditions but was apparently unaffected by alkalinization of the culture medium. Moreover, this gene was not induced by heat shock. These results reveal novel aspects of the heat-sensing network of N. crassa.
doi:10.1007/s12192-009-0131-z
PMCID: PMC2866986  PMID: 19618296
Neurospora crassa; hsp70; Heat shock; Pi sensing; pH regulation; nuc-1; pacC
16.  Understanding the physiology of Lactobacillus plantarum at zero growth 
The physiology of Lactobacillus plantarum at extremely low growth rates, through cultivation in retentostats, is much closer to carbon-limited growth than to stationary phase, as evidenced from transcriptomics data, metabolic fluxes, and biomass composition and viability.Using a genome-scale metabolic model and constraint-based computational analyses, amino-acid fluxes—in particular, the rather paradoxical excretion of Asp, Arg, Met, and Ala—could be rationalized as a means to allow extensive metabolism of other amino acids, that is, that of branched-chain and aromatic amino acids.Catabolic products from aromatic amino acids are known to have putative plant-hormone action. The metabolism of amino acids, as well as transcription data, strongly suggested a plant environment-like response in slow-growing L. plantarum, which was confirmed by significant effects of fermented medium on plant root formation.
Natural ecosystems are usually characterized by extremely low and fluctuating nutrient availability. Hence, microorganisms in these environments live a ‘feast-and-famine' existence, with famine the most habitual state. As a result, extremely slow or no growth is the most common state of bacteria, and maintenance processes dominate their life.
In the present study, Lactobacillus plantarum was used as a model microorganism to investigate the physiology of slow growth. Besides fermented foods, this microorganism can be observed in a variety of environmental niches, including plants and lakes, in which nutrient supply is limited. To mimic these conditions, L. plantarum was grown in a glucose-limited chemostat with complete biomass retention (retentostat). During cultivation, biomass progressively accumulated, resulting in steadily decreasing specific substrate availability. Less energy was thus available for growth, and the specific growth rate decreased accordingly, with a final calculated doubling time greater than one year. Detailed measurements of metabolic fluxes were used as constraints in a genome-scale metabolic model to precisely calculate the amount of energy used for net biomass synthesis and for maintenance purposes: at the lowest growth rate investigated (μ=0.0002 h−1), maintenance accounted for 94% of all energy expenses.
Genome-scale metabolic analysis was used in combination with transcriptomics to study the adaptation of L. plantarum to extremely slow growth under limited carbon and energy supply. Importantly, slow growth as investigated here was fundamentally different from the widely studied carbon starvation-induced stationary phase: non-growing cells in retentostat conditions were glucose limited rather than starved, and the transition from a growing to a non-growing state under retentostat conditions was progressive, in contrast with the abrupt transition in batch cultures. These differences were reflected in various aspects of the cell physiology.
The metabolic behavior was remarkably stable during adaptation to slow growth. Although carbon catabolite repression was clearly relieved, as indicated by the upregulation of genes for the utilization of alternative carbohydrates, the metabolism remained largely based on the conversion of glucose to lactate.
Stress resistance mechanisms were also not massively induced. In particular, analysis of the biomass composition—which remained similar to fast-growing cells even under virtually non-growing conditions—and of the gene expression profile, failed to reveal clear stringent or general stress responses, which are generally triggered in glucose-starved cells. The observation that genes involved in growth-associated processes were not downregulated suggested that active synthesis of biomass components (RNA, proteins, and membranes) was required to account for the observed stable biomass and that turnover of macromolecules was high in slow-growing cells. Biomass viability or morphology was also not affected, compared with faster growth conditions. The only typical stress response was the induction of an SOS response—in particular, the upregulation of the two error-prone DNA polymerases—suggesting an increased potential for genetic diversity under adverse conditions. Although diversity was not apparent under the conditions studied here, such mechanisms of increased rates of mutagenesis are likely to have an important role in the adaptation of L. plantarum to slow growth.
A surprising response of L. plantarum during adaptation to slow growth was the production of several amino acids (Arg, Asp, Met, and Ala). A priori, this metabolic behavior seemed inefficient in a context of energy limitation. However, reduced cost analysis using the genome-scale metabolic model indicated that it had a positive effect on energy generation. In-depth analysis of metabolic flux distributions showed that biosynthesis of these amino acids was connected to the catabolism of branched-chain and aromatic amino acids (BCAAs and AAAs), under conditions of limited ammonium efflux. At a fixed ammonium efflux—fixed at the measured value—flux balance analysis indicated that BCAAs and AAAs were expensive to metabolize, because the regeneration of 2-ketoglutarate through glutamate dehydrogenase was limited by ammonium dissipation. Therefore, alternative pathways had to be active to supply the necessary pool of 2-ketoglutarate. At low growth rates, amino-acid production (Arg, Asp, Ala, and Met) accounted for most of the 2-ketoglutarate regeneration. Although it came at the expense of ATP, this metabolic alternative to glutamate dehydrogenase was less energy costly than other solutions such as purine biosynthesis. This is thus an excellent example in which precise, quantitative modeling results in new insights in physiology that intuition would never have achieved. It also shows that flux balance analysis can be used to accurately predict energetically inefficient metabolism, provided the appropriate fluxes are constrained (here, ammonium efflux).
The observation that BCAAs and AAAs were catabolized at the expense of energy was intriguing. However, several end products of these catabolic pathways can serve as signaling molecules for interactions with other organisms. In particular, precursors of plant hormones were predicted as possible end products in the model simulations. Accordingly, the production of compounds interfering with plant root development was demonstrated in slow-growing L. plantarum. The metabolic analysis thus suggested that slow-growing L. plantarum produced plant hormones—or precursors thereof—as a strategy to divert the plant metabolism towards its own interest. In support of this view, transcriptome analysis indicated the upregulation of genes involved in the catabolism of β-glucosides—typical sugars from plant cell wall—as well as a very high induction of six gene clusters encoding cell-surface protein complexes predicted to have a role in the utilization of plant polysaccharides (csc clusters). In such a plant context, limited ammonium production would also make sense, because of the well-documented toxicity of ammonium for plants: production of amino acids could represent an alternative to ammonium excretion while keeping both parties satisfied.
In conclusion, the physiology of L. plantarum at extremely low growth rates, as studied by genome-scale metabolic modeling and transcriptomics, is fundamentally different from that of starvation-induced stationary phase cells. Excitingly, these conditions seem to trigger responses that favor interactions with the environment, more specifically with plants. The reported observations were made in the absence of any plant-derived material, suggesting that this response might constitute a hardwired behavior.
Situations of extremely low substrate availability, resulting in slow growth, are common in natural environments. To mimic these conditions, Lactobacillus plantarum was grown in a carbon-limited retentostat with complete biomass retention. The physiology of extremely slow-growing L. plantarum—as studied by genome-scale modeling and transcriptomics—was fundamentally different from that of stationary-phase cells. Stress resistance mechanisms were not massively induced during transition to extremely slow growth. The energy-generating metabolism was remarkably stable and remained largely based on the conversion of glucose to lactate. The combination of metabolic and transcriptomic analyses revealed behaviors involved in interactions with the environment, more particularly with plants: production of plant hormones or precursors thereof, and preparedness for the utilization of plant-derived substrates. Accordingly, the production of compounds interfering with plant root development was demonstrated in slow-growing L. plantarum. Thus, conditions of slow growth and limited substrate availability seem to trigger a plant environment-like response, even in the absence of plant-derived material, suggesting that this might constitute an intrinsic behavior in L. plantarum.
doi:10.1038/msb.2010.67
PMCID: PMC2964122  PMID: 20865006
Lactobacillus plantarum; metabolic modeling; retentostat; slow growth; transcriptome analysis
17.  Evolution and Diversity of a Fungal Self/Nonself Recognition Locus 
PLoS ONE  2010;5(11):e14055.
Background
Self/nonself discrimination is an essential feature for pathogen recognition and graft rejection and is a ubiquitous phenomenon in many organisms. Filamentous fungi, such as Neurospora crassa, provide a model for analyses of population genetics/evolution of self/nonself recognition loci due to their haploid nature, small genomes and excellent genetic/genomic resources. In N. crassa, nonself discrimination during vegetative growth is determined by 11 heterokaryon incompatibility (het) loci. Cell fusion between strains that differ in allelic specificity at any of these het loci triggers a rapid programmed cell death response.
Methodology/Principal Findings
In this study, we evaluated the evolution, population genetics and selective mechanisms operating at a nonself recognition complex consisting of two closely linked loci, het-c (NCU03493) and pin-c (NCU03494). The genomic position of pin-c next to het-c is unique to Neurospora/Sordaria species, and originated by gene duplication after divergence from other species within the Sordariaceae. The het-c pin-c alleles in N. crassa are in severe linkage disequilibrium and consist of three haplotypes, het-c1/pin-c1, het-c2/pin-c2 and het-c3/pin-c3, which are equally frequent in population samples and exhibit trans-species polymorphisms. The absence of recombinant haplotypes is correlated with divergence of the het-c/pin-c intergenic sequence. Tests for positive and balancing selection at het-c and pin-c support the conclusion that both of these loci are under non-neutral balancing selection; other regions of both genes appear to be under positive selection. Our data show that the het-c2/pin-c2 haplotype emerged by a recombination event between the het-c1/pin-c1 and het-c3/pin-c3 approximately 3–12 million years ago.
Conclusions/Significance
These results support models by which loci that confer nonself discrimination form by the association of polymorphic genes with genes containing HET domains. Distinct allele classes can emerge by recombination and positive selection and are subsequently maintained by balancing selection and divergence of intergenic sequence resulting in recombination blocks between haplotypes.
doi:10.1371/journal.pone.0014055
PMCID: PMC2988816  PMID: 21124910
18.  Metabolic transcription analysis of engineered Escherichia coli strains that overproduce L-phenylalanine 
Background
The rational design of L-phenylalanine (L-Phe) overproducing microorganisms has been successfully achieved by combining different genetic strategies such as inactivation of the phosphoenolpyruvate: phosphotransferase transport system (PTS) and overexpression of key genes (DAHP synthase, transketolase and chorismate mutase-prephenate dehydratase), reaching yields of 0.33 (g-Phe/g-Glc), which correspond to 60% of theoretical maximum. Although genetic modifications introduced into the cell for the generation of overproducing organisms are specifically targeted to a particular pathway, these can trigger unexpected transcriptional responses of several genes. In the current work, metabolic transcription analysis (MTA) of both L-Phe overproducing and non-engineered strains using Real-Time PCR was performed, allowing the detection of transcriptional responses to PTS deletion and plasmid presence of genes related to central carbon metabolism. This MTA included 86 genes encoding enzymes of glycolysis, gluconeogenesis, pentoses phosphate, tricarboxylic acid cycle, fermentative and aromatic amino acid pathways. In addition, 30 genes encoding regulatory proteins and transporters for aromatic compounds and carbohydrates were also analyzed.
Results
MTA revealed that a set of genes encoding carbohydrate transporters (galP, mglB), gluconeogenic (ppsA, pckA) and fermentative enzymes (ldhA) were significantly induced, while some others were down-regulated such as ppc, pflB, pta and ackA, as a consequence of PTS inactivation. One of the most relevant findings was the coordinated up-regulation of several genes that are exclusively gluconeogenic (fbp, ppsA, pckA, maeB, sfcA, and glyoxylate shunt) in the best PTS- L-Phe overproducing strain (PB12-ev2). Furthermore, it was noticeable that most of the TCA genes showed a strong up-regulation in the presence of multicopy plasmids by an unknown mechanism. A group of genes exhibited transcriptional responses to both PTS inactivation and the presence of plasmids. For instance, acs-ackA, sucABCD, and sdhABCD operons were up-regulated in PB12 (PTS mutant that carries an arcB- mutation). The induction of these operons was further increased by the presence of plasmids in PB12-ev2. Some genes involved in the shikimate and specific aromatic amino acid pathways showed down-regulation in the L-Phe overproducing strains, might cause possible metabolic limitations in the shikimate pathway.
Conclusion
The identification of potential rate-limiting steps and the detection of transcriptional responses in overproducing microorganisms may suggest "reverse engineering" strategies for the further improvement of L-Phe production strains.
doi:10.1186/1475-2859-6-30
PMCID: PMC2089068  PMID: 17880710
19.  An integrated genetic, genomic and systems approach defines gene networks regulated by the interaction of light and carbon signaling pathways in Arabidopsis 
BMC Systems Biology  2008;2:31.
Background
Light and carbon are two important interacting signals affecting plant growth and development. The mechanism(s) and/or genes involved in sensing and/or mediating the signaling pathways involving these interactions are unknown. This study integrates genetic, genomic and systems approaches to identify a genetically perturbed gene network that is regulated by the interaction of carbon and light signaling in Arabidopsis.
Results
Carbon and light insensitive (cli) mutants were isolated. Microarray data from cli186 is analyzed to identify the genes, biological processes and gene networks affected by the integration of light and carbon pathways. Analysis of this data reveals 966 genes regulated by light and/or carbon signaling in wild-type. In cli186, 216 of these light/carbon regulated genes are misregulated in response to light and/or carbon treatments where 78% are misregulated in response to light and carbon interactions. Analysis of the gene lists show that genes in the biological processes "energy" and "metabolism" are over-represented among the 966 genes regulated by carbon and/or light in wild-type, and the 216 misregulated genes in cli186. To understand connections among carbon and/or light regulated genes in wild-type and the misregulated genes in cli186, the microarray data is interpreted in the context of metabolic and regulatory networks. The network created from the 966 light/carbon regulated genes in wild-type, reveals that cli186 is affected in the light and/or carbon regulation of a network of 60 connected genes, including six transcription factors. One transcription factor, HAT22 appears to be a regulatory "hub" in the cli186 network as it shows regulatory connections linking a metabolic network of genes involved in "amino acid metabolism", "C-compound/carbohydrate metabolism" and "glycolysis/gluconeogenesis".
Conclusion
The global misregulation of gene networks controlled by light and carbon signaling in cli186 indicates that it represents one of the first Arabidopsis mutants isolated that is specifically disrupted in the integration of both carbon and light signals to control the regulation of metabolic, developmental and regulatory genes. The network analysis of misregulated genes suggests that CLI186 acts to integrate light and carbon signaling interactions and is a master regulator connecting the regulation of a host of downstream metabolic and regulatory processes.
doi:10.1186/1752-0509-2-31
PMCID: PMC2335094  PMID: 18387196
20.  Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions 
A human alveolar macrophage genome-scale metabolic reconstruction was reconstructed from tailoring a global human metabolic network, Recon 1, by using computational algorithms and manual curation.A genome-scale host–pathogen network of the human alveolar macrophage and Mycobacterium tuberculosis is presented. This involved integrating two genome-scale network reconstructions.The reaction activity and gene essentiality predictions of the host–pathogen model represent a more accurate depiction of infection.Integration of high-throughput data into a host-pathogen model followed by systems analysis was performed in order to elucidate major metabolic differences under different types of M. tuberculosis infection.
Mycobacterium tuberculosis (M. tb) is an insidious and highly persistent pathogen that affects one-third of the world's population (WHO, 2009). Metabolism is foundational to M. tb's infection ability and the ensuing host–pathogen interactions. In addition, M. tb has a heterogeneous clinical presentation and can infect virtually every tissue. Depending on the location of the infection, different metabolic pathways are active and inactive in both the host and pathogen cells. In this study, we sought to model the host–pathogen interactions of the human alveolar macrophage and M. tb as well as detail the metabolic differences in specific infection types using genome-scale metabolic reconstructions (Figure 4A).
Genome-scale metabolic reconstructions are knowledge bases of all known metabolic reactions of a given organism. Reconstructions have been shown to elucidate the mechanistic genotype-to-phenotype relationship through the integration of high-throughput and physiological data (Oberhardt et al, 2009). Genome-scale reconstructions are converted into mathematical models under the constraints-based reconstruction and analysis (COBRA) platform (Becker et al, 2007). COBRA models use network stoichiometry and steady-state mass balances to define a solution space of potential flux states that a network can take. Thus, the COBRA approach does not require kinetic parameters.
Recently, the global human metabolic network, Recon 1, has been reconstructed (Duarte et al, 2007). To understand the metabolic host–pathogen integrations of M. tb with its human host, we first tailored the global human metabolic network into a cell-specific metabolic reconstruction of the human alveolar macrophage. This was carried out using established computational algorithms (Becker and Palsson, 2008; Shlomi et al, 2008) and manual curation to confirm the included and excluded reactions. The human alveolar macrophage reconstruction, iAB-AMØ-1410, accounts for 1410 genes, 3012 intracellular reactions, and 2572 metabolites (Figure 4C). iAB-AMØ-1410 was able to accurately predict maximum ATP and NO production rates obtained from experimental data (Griscavage et al, 1993; Newsholme et al, 1999).
The second step to studying host–pathogen interactions was integration of the human alveolar macrophage reconstruction with an existing genome-scale metabolic model of M. tb, iNJ661 (Jamshidi and Palsson, 2007). Interfacial constraints were set to create a phagosomal environment that was hypoxic, nitrosative, rich in fatty acids, and poor in carbohydrates. From the onset, it was apparent that some oxygen (<15% of in vitro uptake) was required for proper simulations. In addition, algorithmic tailoring of the M. tb biomass objective function was performed to better represent an infectious state. The integrated host–pathogen metabolic reconstruction was dubbed iAB-AMØ-1410-Mt-661.
Analysis of the integrated host–pathogen metabolic reconstruction resulted in three main findings. First, by setting interfacial constraints and tailoring the biomass objective function, the solution space better represents an infectious state. Without adding artificial constraints to the host portion of the integrated model, the iAB-AMØ-1410 solution space is greatly reduced (Figure 4B). Macrophage glycolysis and nitric oxide production are up-regulated and macrophage ATP production, nucleotide synthesis, and amino-acid metabolism are suppressed. In addition, M. tb glycolysis is suppressed and isocitrate lyase is up-regulated for generation of acetyl-CoA. Fatty acid oxidation pathways and production of mycolic acids are increased, while production of nucleotides, peptidoglycans, and phenolic glycolipids are reduced. The modified solution space of the alveolar macrophage and M. tb better represents the infectious state.
Second, the host-pathogen model more accurately predicts M. tb gene deletion tests than the current in vitro model, iNJ661. The host-pathogen model predicted 11 essential genes and 37 unessential genes differently than iNJ661. A total of 22 of the differentially predicted genes have been experimentally characterized (Sassetti and Rubin, 2003; Sohaskey, 2008). The host-pathogen model correctly predicted 18 of the 22 genes. Thus, iAB-AMØ-1410-Mt-661 is a more accurate platform for studying infectious states of M. tb.
Finally, we sought to determine metabolic differences in both the macrophage and M. tb between three different types of infection: latent, pulmonary, and meningeal. Transcription profiling data of the macrophage for the three infections (Thuong et al, 2008) were integrated in the context of the host–pathogen network to elucidate the reaction activity of the three infections. There was wide heterogeneity in the three infection states; some of these differences are highlighted. Macrophage hyaluronan synthase and export were only active in the pulmonary infection. This is potentially interesting from a pharmaceutical viewpoint as hyaluronan has been implicated as a potential carbon source for extracellular M. tb (Hirayama et al, 2009). In addition, we detected metabolic activity differences in M. tb pathways that have been previously discussed as potential drug targets (Eoh et al, 2007; Boshoff et al, 2008). Polyprenyl metabolic reactions were only active in the latent state infection, while de novo synthesis of nicotinamide cofactors was only active in latent and meningeal M. tb infections.
Host-pathogen modeling represents a novel approach for studying metabolic interactions during infection. iAB-AMØ-1410-Mt-661 is a more accurate platform for understanding the biology and pathophysiology of M. tb infection. Most importantly, genome-scale metabolic reconstructions can act as scaffolds for integrating high-throughput data. Particularly, in this study we were able to discern reaction activity differences between different infection types.
Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host–pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host–pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host–pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host–pathogen network and were able to describe three distinct pathological states. Integrated host–pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.
doi:10.1038/msb.2010.68
PMCID: PMC2990636  PMID: 20959820
computational biology; host–pathogen; Mycobacterium tuberculosis; systems biology; macrophage
21.  Systems Biology of the Clock in Neurospora crassa 
PLoS ONE  2008;3(8):e3105.
A model-driven discovery process, Computing Life, is used to identify an ensemble of genetic networks that describe the biological clock. A clock mechanism involving the genes white-collar-1 and white-collar-2 (wc-1 and wc-2) that encode a transcriptional activator (as well as a blue-light receptor) and an oscillator frequency (frq) that encodes a cyclin that deactivates the activator is used to guide this discovery process through three cycles of microarray experiments. Central to this discovery process is a new methodology for the rational design of a Maximally Informative Next Experiment (MINE), based on the genetic network ensemble. In each experimentation cycle, the MINE approach is used to select the most informative new experiment in order to mine for clock-controlled genes, the outputs of the clock. As much as 25% of the N. crassa transcriptome appears to be under clock-control. Clock outputs include genes with products in DNA metabolism, ribosome biogenesis in RNA metabolism, cell cycle, protein metabolism, transport, carbon metabolism, isoprenoid (including carotenoid) biosynthesis, development, and varied signaling processes. Genes under the transcription factor complex WCC ( = WC-1/WC-2) control were resolved into four classes, circadian only (612 genes), light-responsive only (396), both circadian and light-responsive (328), and neither circadian nor light-responsive (987). In each of three cycles of microarray experiments data support that wc-1 and wc-2 are auto-regulated by WCC. Among 11,000 N. crassa genes a total of 295 genes, including a large fraction of phosphatases/kinases, appear to be under the immediate control of the FRQ oscillator as validated by 4 independent microarray experiments. Ribosomal RNA processing and assembly rather than its transcription appears to be under clock control, suggesting a new mechanism for the post-transcriptional control of clock-controlled genes.
doi:10.1371/journal.pone.0003105
PMCID: PMC2518617  PMID: 18769678
22.  In-Depth Profiling of Lysine-Producing Corynebacterium glutamicum by Combined Analysis of the Transcriptome, Metabolome, and Fluxome 
Journal of Bacteriology  2004;186(6):1769-1784.
An in-depth analysis of the intracellular metabolite concentrations, metabolic fluxes, and gene expression (metabolome, fluxome, and transcriptome, respectively) of lysine-producing Corynebacterium glutamicum ATCC 13287 was performed at different stages of batch culture and revealed distinct phases of growth and lysine production. For this purpose, 13C flux analysis with gas chromatography-mass spectrometry-labeling measurement of free intracellular amino acids, metabolite balancing, and isotopomer modeling were combined with expression profiling via DNA microarrays and with intracellular metabolite quantification. The phase shift from growth to lysine production was accompanied by a decrease in glucose uptake flux, the redirection of flux from the tricarboxylic acid (TCA) cycle towards anaplerotic carboxylation and lysine biosynthesis, transient dynamics of intracellular metabolite pools, such as an increase of lysine up to 40 mM prior to its excretion, and complex changes in the expression of genes for central metabolism. The integrated approach was valuable for the identification of correlations between gene expression and in vivo activity for numerous enzymes. The glucose uptake flux closely corresponded to the expression of glucose phosphotransferase genes. A correlation between flux and expression was also observed for glucose-6-phosphate dehydrogenase, transaldolase, and transketolase and for most TCA cycle genes. In contrast, cytoplasmic malate dehydrogenase expression increased despite a reduction of the TCA cycle flux, probably related to its contribution to NADH regeneration under conditions of reduced growth. Most genes for lysine biosynthesis showed a constant expression level, despite a marked change of the metabolic flux, indicating that they are strongly regulated at the metabolic level. Glyoxylate cycle genes were continuously expressed, but the pathway exhibited in vivo activity only in the later stage. The most pronounced changes in gene expression during cultivation were found for enzymes at entry points into glycolysis, the pentose phosphate pathway, the TCA cycle, and lysine biosynthesis, indicating that these might be of special importance for transcriptional control in C. glutamicum.
doi:10.1128/JB.186.6.1769-1784.2004
PMCID: PMC355958  PMID: 14996808
23.  Discovering Functions of Unannotated Genes from a Transcriptome Survey of Wild Fungal Isolates 
mBio  2014;5(2):e01046-13.
ABSTRACT
Most fungal genomes are poorly annotated, and many fungal traits of industrial and biomedical relevance are not well suited to classical genetic screens. Assigning genes to phenotypes on a genomic scale thus remains an urgent need in the field. We developed an approach to infer gene function from expression profiles of wild fungal isolates, and we applied our strategy to the filamentous fungus Neurospora crassa. Using transcriptome measurements in 70 strains from two well-defined clades of this microbe, we first identified 2,247 cases in which the expression of an unannotated gene rose and fell across N. crassa strains in parallel with the expression of well-characterized genes. We then used image analysis of hyphal morphologies, quantitative growth assays, and expression profiling to test the functions of four genes predicted from our population analyses. The results revealed two factors that influenced regulation of metabolism of nonpreferred carbon and nitrogen sources, a gene that governed hyphal architecture, and a gene that mediated amino acid starvation resistance. These findings validate the power of our population-transcriptomic approach for inference of novel gene function, and we suggest that this strategy will be of broad utility for genome-scale annotation in many fungal systems.
IMPORTANCE
Some fungal species cause deadly infections in humans or crop plants, and other fungi are workhorses of industrial chemistry, including the production of biofuels. Advances in medical and industrial mycology require an understanding of the genes that control fungal traits. We developed a method to infer functions of uncharacterized genes by observing correlated expression of their mRNAs with those of known genes across wild fungal isolates. We applied this strategy to a filamentous fungus and predicted functions for thousands of unknown genes. In four cases, we experimentally validated the predictions from our method, discovering novel genes involved in the metabolism of nutrient sources relevant for biofuel production, as well as colony morphology and starvation resistance. Our strategy is straightforward, inexpensive, and applicable for predicting gene function in many fungal species.
doi:10.1128/mBio.01046-13
PMCID: PMC3977361  PMID: 24692637
24.  Transcriptomic and proteomic analyses of the Aspergillus fumigatus hypoxia response using an oxygen-controlled fermenter 
BMC Genomics  2012;13:62.
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 (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.
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.
doi:10.1186/1471-2164-13-62
PMCID: PMC3293747  PMID: 22309491
25.  Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism 
A comprehensive genome-scale metabolic network of Chlamydomonas reinhardtii, including a detailed account of light-driven metabolism, is reconstructed and validated. The model provides a new resource for research of C. reinhardtii metabolism and in algal biotechnology.
The genome-scale metabolic network of Chlamydomonas reinhardtii (iRC1080) was reconstructed, accounting for >32% of the estimated metabolic genes encoded in the genome, and including extensive details of lipid metabolic pathways.This is the first metabolic network to explicitly account for stoichiometry and wavelengths of metabolic photon usage, providing a new resource for research of C. reinhardtii metabolism and developments in algal biotechnology.Metabolic functional annotation and the largest transcript verification of a metabolic network to date was performed, at least partially verifying >90% of the transcripts accounted for in iRC1080. Analysis of the network supports hypotheses concerning the evolution of latent lipid pathways in C. reinhardtii, including very long-chain polyunsaturated fatty acid and ceramide synthesis pathways.A novel approach for modeling light-driven metabolism was developed that accounts for both light source intensity and spectral quality of emitted light. The constructs resulting from this approach, termed prism reactions, were shown to significantly improve the accuracy of model predictions, and their use was demonstrated for evaluation of light source efficiency and design.
Algae have garnered significant interest in recent years, especially for their potential application in biofuel production. The hallmark, model eukaryotic microalgae Chlamydomonas reinhardtii has been widely used to study photosynthesis, cell motility and phototaxis, cell wall biogenesis, and other fundamental cellular processes (Harris, 2001). Characterizing algal metabolism is key to engineering production strains and understanding photobiological phenomena. Based on extensive literature on C. reinhardtii metabolism, its genome sequence (Merchant et al, 2007), and gene functional annotation, we have reconstructed and experimentally validated the genome-scale metabolic network for this alga, iRC1080, the first network to account for detailed photon absorption permitting growth simulations under different light sources. iRC1080 accounts for 1080 genes, associated with 2190 reactions and 1068 unique metabolites and encompasses 83 subsystems distributed across 10 cellular compartments (Figure 1A). Its >32% coverage of estimated metabolic genes is a tremendous expansion over previous algal reconstructions (Boyle and Morgan, 2009; Manichaikul et al, 2009). The lipid metabolic pathways of iRC1080 are considerably expanded relative to existing networks, and chemical properties of all metabolites in these pathways are accounted for explicitly, providing sufficient detail to completely specify all individual molecular species: backbone molecule and stereochemical numbering of acyl-chain positions; acyl-chain length; and number, position, and cis–trans stereoisomerism of carbon–carbon double bonds. Such detail in lipid metabolism will be critical for model-driven metabolic engineering efforts.
We experimentally verified transcripts accounted for in the network under permissive growth conditions, detecting >90% of tested transcript models (Figure 1B) and providing validating evidence for the contents of iRC1080. We also analyzed the extent of transcript verification by specific metabolic subsystems. Some subsystems stood out as more poorly verified, including chloroplast and mitochondrial transport systems and sphingolipid metabolism, all of which exhibited <80% of transcripts detected, reflecting incomplete characterization of compartmental transporters and supporting a hypothesis of latent pathway evolution for ceramide synthesis in C. reinhardtii. Additional lines of evidence from the reconstruction effort similarly support this hypothesis including lack of ceramide synthetase and other annotation gaps downstream in sphingolipid metabolism. A similar hypothesis of latent pathway evolution was established for very long-chain fatty acids (VLCFAs) and their polyunsaturated analogs (VLCPUFAs) (Figure 1C), owing to the absence of this class of lipids in previous experimental measurements, lack of a candidate VLCFA elongase in the functional annotation, and additional downstream annotation gaps in arachidonic acid metabolism.
The network provides a detailed account of metabolic photon absorption by light-driven reactions, including photosystems I and II, light-dependent protochlorophyllide oxidoreductase, provitamin D3 photoconversion to vitamin D3, and rhodopsin photoisomerase; this network accounting permits the precise modeling of light-dependent metabolism. iRC1080 accounts for effective light spectral ranges through analysis of biochemical activity spectra (Figure 3A), either reaction activity or absorbance at varying light wavelengths. Defining effective spectral ranges associated with each photon-utilizing reaction enabled our network to model growth under different light sources via stoichiometric representation of the spectral composition of emitted light, termed prism reactions. Coefficients for different photon wavelengths in a prism reaction correspond to the ratios of photon flux in the defined effective spectral ranges to the total emitted photon flux from a given light source (Figure 3B). This approach distinguishes the amount of emitted photons that drive different metabolic reactions. We created prism reactions for most light sources that have been used in published studies for algal and plant growth including solar light, various light bulbs, and LEDs. We also included regulatory effects, resulting from lighting conditions insofar as published studies enabled. Light and dark conditions have been shown to affect metabolic enzyme activity in C. reinhardtii on multiple levels: transcriptional regulation, chloroplast RNA degradation, translational regulation, and thioredoxin-mediated enzyme regulation. Through application of our light model and prism reactions, we were able to closely recapitulate experimental growth measurements under solar, incandescent, and red LED lights. Through unbiased sampling, we were able to establish the tremendous statistical significance of the accuracy of growth predictions achievable through implementation of prism reactions. Finally, application of the photosynthetic model was demonstrated prospectively to evaluate light utilization efficiency under different light sources. The results suggest that, of the existing light sources, red LEDs provide the greatest efficiency, about three times as efficient as sunlight. Extending this analysis, the model was applied to design a maximally efficient LED spectrum for algal growth. The result was a 677-nm peak LED spectrum with a total incident photon flux of 360 μE/m2/s, suggesting that for the simple objective of maximizing growth efficiency, LED technology has already reached an effective theoretical optimum.
In summary, the C. reinhardtii metabolic network iRC1080 that we have reconstructed offers insight into the basic biology of this species and may be employed prospectively for genetic engineering design and light source design relevant to algal biotechnology. iRC1080 was used to analyze lipid metabolism and generate novel hypotheses about the evolution of latent pathways. The predictive capacity of metabolic models developed from iRC1080 was demonstrated in simulating mutant phenotypes and in evaluation of light source efficiency. Our network provides a broad knowledgebase of the biochemistry and genomics underlying global metabolism of a photoautotroph, and our modeling approach for light-driven metabolism exemplifies how integration of largely unvisited data types, such as physicochemical environmental parameters, can expand the diversity of applications of metabolic networks.
Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology.
doi:10.1038/msb.2011.52
PMCID: PMC3202792  PMID: 21811229
Chlamydomonas reinhardtii; lipid metabolism; metabolic engineering; photobioreactor

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