Saccharomyces cerevisiae adapted to grow in the presence of low-dose rapamycin exhibit altered amino acid metabolism.

BACKGROUND: Rapamycin is a potent inhibitor of the highly conserved TOR kinase, the nutrient-sensitive controller of growth and aging. It has been utilised as a chemotherapeutic agent due to its anti-proliferative properties and as an immunosuppressive drug, and is also known to extend lifespan in a range of eukaryotes from yeast to mammals. However, the mechanisms through which eukaryotic cells adapt to sustained exposure to rapamycin have not yet been thoroughly investigated. METHODS: Here, S. cerevisiae response to long-term rapamycin exposure was investigated by identifying the physiological, transcriptomic and metabolic differences observed for yeast populations inoculated into low-dose rapamycin-containing environment. The effect of oxygen availability and acidity of extracellular environment on this response was further deliberated by controlling or monitoring the dissolved oxygen level and pH of the culture. RESULTS: Yeast populations grown in the presence of rapamycin reached higher cell densities complemented by an increase in their chronological lifespan, and these physiological adaptations were associated with a rewiring of the amino acid metabolism, particularly that of arginine. The ability to synthesise amino acids emerges as the key factor leading to the major mechanistic differences between mammalian and microbial TOR signalling pathways in relation to nutrient recognition. CONCLUSION: Oxygen levels and extracellular acidity of the culture were observed to conjointly affect yeast populations, virtually acting as coupled physiological effectors; cells were best adapted when maximal oxygenation of the culture was maintained in slightly acidic pH, any deviation necessitated more extensive readjustment to additional stress factors.

CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data.

MOTIVATION: Simple bioinformatic tools are frequently used to analyse time-series datasets regardless of their ability to deal with transient phenomena, limiting the meaningful information that may be extracted from them. This situation requires the development and exploitation of tailor-made, easy-to-use and flexible tools designed specifically for the analysis of time-series datasets. RESULTS: We present a novel statistical application called CLUSTERnGO, which uses a model-based clustering algorithm that fulfils this need. This algorithm involves two components of operation. Component 1 constructs a Bayesian non-parametric model (Infinite Mixture of Piecewise Linear Sequences) and Component 2, which applies a novel clustering methodology (Two-Stage Clustering). The software can also assign biological meaning to the identified clusters using an appropriate ontology. It applies multiple hypothesis testing to report the significance of these enrichments. The algorithm has a four-phase pipeline. The application can be executed using either command-line tools or a user-friendly Graphical User Interface. The latter has been developed to address the needs of both specialist and non-specialist users. We use three diverse test cases to demonstrate the flexibility of the proposed strategy. In all cases, CLUSTERnGO not only outperformed existing algorithms in assigning unique GO term enrichments to the identified clusters, but also revealed novel insights regarding the biological systems examined, which were not uncovered in the original publications. AVAILABILITY AND IMPLEMENTATION: The C++ and QT source codes, the GUI applications for Windows, OS X and Linux operating systems and user manual are freely available for download under the GNU GPL v3 license at http://www.cmpe.boun.edu.tr/content/CnG. CONTACT: sgo24@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomic response of yeast cells to ATX1 deletion under different copper levels.

BACKGROUND: Iron and copper homeostatic pathways are tightly linked since copper is required as a cofactor for high affinity iron transport. Atx1p plays an important role in the intracellular copper transport as a copper chaperone transferring copper from the transporters to Ccc2p for its subsequent insertion into Fet3p, which is required for high affinity iron transport. RESULTS: In this study, genome-wide transcriptional landscape of ATX1 deletants grown in media either lacking copper or having excess copper was investigated. ATX1 deletants were allowed to recover full respiratory capacity in the presence of excess copper in growth environment. The present study revealed that iron ion homeostasis was not significantly affected by the absence of ATX1 either at the transcriptional or metabolic levels, suggesting other possible roles for Atx1p in addition to its function as a chaperone in copper-dependent iron absorption. The analysis of the transcriptomic response of atx1∆/atx1∆ and its integration with the genetic interaction network highlighted for the first time, the possible role of ATX1 in cell cycle regulation, likewise its mammalian counterpart ATOX1, which was reported to play an important role in the copper-stimulated proliferation of non-small lung cancer cells. CONCLUSIONS: The present finding revealed the dispensability of Atx1p for the transfer of copper ions to Ccc2p and highlighted its possible role in the cell cycle regulation. The results also showed the potential of Saccharomyces cerevisiae as a model organism in studying the capacity of ATOX1 as a therapeutic target for lung cancer therapy.

Time-dependent re-organization of biological processes by the analysis of the dynamic transcriptional response of yeast cells to doxorubicin.

Doxorubicin is an efficient chemotherapeutic reagent in the treatment of a variety of cancers. However, its underlying molecular mechanism is not fully understood and several severe side effects limit its application. In this study, the dynamic transcriptomic response of Saccharomyces cerevisiae cells to a doxorubicin pulse in a chemostat system was investigated to reveal the underlying molecular mechanism of this drug. The clustering of differentially and significantly expressed genes (DEGs) indicated that the response of yeast cells to doxorubicin is time dependent and may be classified as short-term, mid-term and long-term responses. The cells have started to reorganize their response after the first minute following the injection of the pulse. A modified version of Weighted Gene Co-expression Network Analysis (WGCNA) was used to cluster the positively correlated co-expression profiles, and functional enrichment analysis of these clusters was carried out. DNA replication and DNA repair processes were significantly affected and induced 60 minutes after exposure to doxorubicin. The response to oxidative stress was not identified as a significant term. A transcriptional re-organization of the metabolic pathways seems to be an early event and persists afterwards. The present study reveals for the first time that the RNA surveillance pathway, which is a post-transcriptional regulatory pathway, may be implicated in the short-term reaction of yeast cells to doxorubicin. Integration with regulome revealed the dynamic re-organization of the transcriptomic landscape. Fhl1p, Mbp1p, and Mcm1p were identified as primary regulatory factors responsible for tuning the differentially expressed genes.

Insights Into the Mechanism of Anticancer Drug Imatinib Revealed Through Multi-Omic Analyses in Yeast.

Imatinib mesylate is a receptor tyrosine kinase inhibitor drug with broad applications in cancer therapeutics, for example, in chronic myeloid leukemia and gastrointestinal stromal tumors. In this study, new multi-omics findings in yeast on the mechanism of imatinib are reported, using the model organism Saccharomyces cerevisiae. Whole-genome analysis of the transcriptional response of yeast cells following long-term exposure to imatinib, flux-balance analysis (FBA), and modular analysis of protein/protein interaction network consisting of proteins encoded by differentially expressed genes (DEGs) were performed. DEGs indicated that carbon, nitrogen, starch, sucrose, glyoxylate/dicarboxylate metabolism, valine and leucine degradation, and tricarboxylic acid cycle (TCA) were significantly upregulated. By contrast, ribosome biogenesis, pentose/glucuronate interconversion, tryptophan/pyruvate metabolic pathways, and meiosis were significantly downregulated. FBA revealed that a large set of metabolic pathways was altered by imatinib to compensate cancer-associated metabolic changes. Integration of transcriptome and interactome (protein/protein interactions) data helped to identify the core regulatory genes and pathways through elucidation of the active subnetworks. It appears that imatinib may also contribute to antitumoral immune response in the tumor microenvironment and most of the metabolic rearrangements are at posttranscriptional level. Furthermore, additional support for possible contribution of thiamine/pyridoxal phosphate biosynthesis and mitogen-activated protein kinase pathway to drug resistance is noted. This report advances multi-omics understanding of the mechanism of imatinib, and by extension, offers new molecular avenues toward precision medicine and discovery of novel drug targets in cancer therapeutics.

Dynamic transcriptional response of Saccharomyces cerevisiae cells to copper.

Copper is a crucial trace element for all living systems and any deficiency in copper homeostasis leads to the development of severe diseases in humans. The observation of extensive evolutionary conservation in copper homeostatic systems between human and Saccharomyces cerevisiae made this organism a suitable model organism for elucidating molecular mechanisms of copper transport and homeostasis. In this study, the dynamic transcriptional response of both the reference strain and homozygous deletion mutant strain of CCC2, which encodes a Cu2+-transporting P-type ATPase, were investigated following the introduction of copper impulse to reach a copper concentration which was shown to improve the respiration capacity of CCC2 deletion mutants. The analysis of data by using different clustering algorithms revealed significantly affected processes and pathways in response to a switch from copper deficient environment to elevated copper levels. Sulfur compound, methionine and cysteine biosynthetic processes were identified as significantly affected processes for the first time in this study. Stress response, cellular response to DNA damage, iron ion homeostasis, ubiquitin dependent proteolysis, autophagy and regulation of macroautophagy, DNA repair and replication, as well as organization of mitochondrial respiratory chain complex IV, mitochondrial organization and translation were identified as significantly affected processes in only CCC2 deleted strain. The integration of the transcriptomic data with regulome revealed the differences in the extensive re-wiring of dynamic transcriptional organization and regulation in these strains.

Identification of Novel Components of Target-of-Rapamycin Signaling Pathway by Network-Based Multi-Omics Integrative Analysis.

Target of rapamycin (TOR) is a major signaling pathway and regulator of cell growth. TOR serves as a hub of many signaling routes, and is implicated in the pathophysiology of numerous human diseases, including cancer, diabetes, and neurodegeneration. Therefore, elucidation of unknown components of TOR signaling that could serve as potential biomarkers and drug targets has a great clinical importance. In this study, our aim is to integrate transcriptomics, interactomics, and regulomics data in Saccharomyces cerevisiae using a network-based multiomics approach to enlighten previously unidentified, potential components of TOR signaling. We constructed the TOR-signaling protein interaction network, which was used as a template to search for TOR-mediated rapamycin and caffeine signaling paths. We scored the paths passing from at least one component of TOR Complex 1 or 2 (TORC1/TORC2) using the co-expression levels of the genes in the transcriptome data of the cells grown in the presence of rapamycin or caffeine. The resultant network revealed seven hitherto unannotated proteins, namely, Atg14p, Rim20p, Ret2p, Spt21p, Ylr257wp, Ymr295cp, and Ygr017wp, as potential components of TOR-mediated rapamycin and caffeine signaling in yeast. Among these proteins, we suggest further deciphering of the role of Ylr257wp will be particularly informative in the future because it was the only protein whose removal from the constructed network hindered the signal transduction to the TORC1 effector kinase Npr1p. In conclusion, this study underlines the value of network-based multiomics integrative data analysis in discovering previously unidentified components of the signaling networks by revealing potential components of TOR signaling for future experimental validation.

Exometabolic and transcriptional response in relation to phenotype and gene copy number in respiration-related deletion mutants of S. cerevisiae.

The transcriptional and metabolic impact of deleting one or both copies of a respiration-related gene has been studied in glucose-limited chemostats. Integration of literature information on phenotype with our exometabolome and transcriptome data enabled the identification of novel relationships between gene copy number, transcriptional regulation and phenotype. We found that the effect of complete respiratory deficiency on transcription was limited to downregulation of genes involved in oxidoreductase activity and iron assimilation. Partial respiratory deficiency had no significant impact on gene transcription. Changes in the copy number of two transcription-factor genes, HAP4 and MIG1, had a major impact on genes involved in mitochondrial function. Regulation of respiratory chain components encoded in the nucleus and mitochondria appears to be divided between Hap4p and Oxa1p, respectively. Similarly, repression of respiration may be imposed by the action of Mig1p and Mba1p on nuclear and mitochondrial gene expression, respectively. However, it is not clear whether Oxa1p and Mba1p regulate mitochondrial gene expression via their interaction with mitochondrial ribosomes or by some indirect means. The phenotype of nuclear petite mutants may not simply be due to the absence of respiration; e.g. Oxa1p or Bcs1p may play a role in the regulation of ribosome assembly in the nucleolus. Integration between respiration and cell growth may also result from the action of a single transcription factor. Thus, Hap4p targets genes that are required for respiration and for fitness in nutrient-limited conditions. This suggests that Hap4p may enable cells to adapt to nutrient limitation as well as diauxy.

Integration of metabolic modeling and phenotypic data in evaluation and improvement of ethanol production using respiration-deficient mutants of Saccharomyces cerevisiae.

Flux balance analysis and phenotypic data were used to provide clues to the relationships between the activities of gene products and the phenotypes resulting from the deletion of genes involved in respiratory function in Saccharomyces cerevisiae. The effect of partial or complete respiratory deficiency on the ethanol production and growth characteristics of hap4Delta/hap4Delta, mig1Delta/mig1Delta, qdr3Delta/qdr3Delta, pdr3Delta/pdr3Delta, qcr7Delta/qcr7Delta, cyt1Delta/cyt1Delta, and rip1Delta/rip1Delta mutants grown in microaerated chemostats was investigated. The study provided additional evidence for the importance of the selection of a physiologically relevant objective function, and it may improve quantitative predictions of exchange fluxes, as well as qualitative estimations of changes in intracellular fluxes. Ethanol production was successfully predicted by flux balance analysis in the case of the qdr3Delta/qdr3Delta mutant, with maximization of ethanol production as the objective function, suggesting an additional role for Qdr3p in respiration. The absence of similar changes in estimated intracellular fluxes in the qcr7Delta/qcr7Delta mutant compared to the rip1Delta/rip1Delta and cyt1Delta/cyt1Delta mutants indicated that the effect of the deletion of this subunit of complex III was somehow compensated for. Analysis of predicted flux distributions indicated self-organization of intracellular fluxes to avoid NAD(+)/NADH imbalance in rip1Delta/rip1Delta and cyt1Delta/cyt1Delta mutants, but not the qcr7Delta/qcr7Delta mutant. The flux through the glycerol efflux channel, Fps1p, was estimated to be zero in all strains under the investigated conditions. This indicates that previous strategies for improving ethanol production, such as the overexpression of the glutamate synthase gene GLT1 in a GDH1 deletion background or deletion of the glycerol efflux channel gene FPS1 and overexpression of GLT1, are unnecessary in a respiration-deficient background.

Doxorubicin induces an extensive transcriptional and metabolic rewiring in yeast cells.

Doxorubicin is one of the most effective chemotherapy drugs used against solid tumors in the treatment of several cancer types. Two different mechanisms, (i) intercalation of doxorubicin into DNA and inhibition of topoisomerase II leading to changes in chromatin structure, (ii) generation of free radicals and oxidative damage to biomolecules, have been proposed to explain the mode of action of this drug in cancer cells. A genome-wide integrative systems biology approach used in the present study to investigate the long-term effect of doxorubicin in Saccharomyces cerevisiae cells indicated the up-regulation of genes involved in response to oxidative stress as well as in Rad53 checkpoint sensing and signaling pathway. Modular analysis of the active sub-network has also revealed the induction of the genes significantly associated with nucleosome assembly/disassembly and DNA repair in response to doxorubicin. Furthermore, an extensive re-wiring of the metabolism was observed. In addition to glycolysis, and sulfate assimilation, several pathways related to ribosome biogenesis/translation, amino acid biosynthesis, nucleotide biosynthesis, de novo IMP biosynthesis and one-carbon metabolism were significantly repressed. Pentose phosphate pathway, MAPK signaling pathway biological processes associated with meiosis and sporulation were found to be induced in response to long-term exposure to doxorubicin in yeast cells.

Integration of metabolome data with metabolic networks reveals reporter reactions.

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.

Sequence variations within the genes related to hemostatic imbalance and their impact on coronary artery disease in Turkish population.

Coronary artery disease (CAD) is reported to be associated with some genetic risk factors. Since identification of genetic risk factors for CAD in different ethnic groups is important for the development of new intervention and prevention programs, we investigated the association between the R353Q and -323ins10 polymorphisms in Factor VII gene, C677T mutation in MTHFR, Factor V Leiden and PT G20210A mutations and CAD in Turkish population. The promoter region of the PAI-1 gene was also screened by SSCA (single-stranded conformation analysis) using specifically designed primers. 137 CAD patients with early onset documented by coronary angiography and 41 individuals who had no significant coronary stenosis by angiography as control group were screened for the identification of the polymorphisms. In conclusion, Factor V Leiden was found to be an independent genetic risk factor for CAD in Turkish population. Combined risk assessment indicated that the coexistence of two other inherited thrombophilia markers, namely MTHFR C677T and PT G20210A with Factor V Leiden may increase the risk of the development of the disease in this population. The results of the present study show that there is no statistically significant association between the two polymorphisms in Factor VII gene, MTHFR C677T polymorphism, PT G20210A polymorphism, 4G/5G polymorphism of PAI-1 and CAD in Turkish population.

Flux balance analysis of a genome-scale yeast model constrained by exometabolomic data allows metabolic system identification of genetically different strains.

A systems approach to biology requires a principled approach to pathway identification. In this study, the two nuclear petite yeast mutants K1Deltapet191a and K1Deltapet191ab and their parental industrial strain K1 were cultured in glucose-containing microaerobic chemostats. Exometabolomic profiles were used to infer the differences in the fermentation characteristics and respiration capacity of the strains. The ability of the metabolite measurement information to describe genetically different strains was investigated using a genome-scale yeast model. Flux balance analysis (FBA) of the model reveals that the objective function of minimal oxygen consumption enables the identification of the effect of genotypic differences when combined with the knowledge of the extracellular state of metabolism. The predicted decrease in oxygen consumption flux of K1Deltapet191a and K1Deltapet191ab strains with respect to the parental strain is about 80% and 100%, respectively, which coincides with the respiratory deficiencies of the strains. The expected increase in ethanol production rates in response to the decrease in the respiratory capacity was also predicted to be very close to the experimental values. This study shows the predictive power of the integrated analysis of genome-scale models with exometabolomic profiles, since accurate predictions could be made without any information about the respiration capacity of the strains. The FBA approach thereby enables identification of responsive pathways and so permits the elucidation of the genetic characteristics of strains in terms of expressed metabolite profiles.

Real imaging and size values of Saccharomyces cerevisiae cells with comparable contrast tuning to two environmental scanning electron microscopy modes.

The combined application of electron microscopy (EM) is frequently used for the microstructural investigation of biological specimens and plays two important roles in the quantification and in gaining an improved understanding of biological phenomena by making use of the highest resolution capability provided by EM. The possibility of imaging wet specimens in their "native" states in the environmental scanning electron microscope (ESEM) at high resolution and large depth of focus in real time is discussed in this paper. It is demonstrated here that new features can be discovered by the elimination of even the least hazardous approaches in some preparation techniques, that destroy the samples. Since the analysis conditions may influence the morphology and the extreme surface sensitivity of living biological systems, the results obtained from the same cultured cell with two different ESEM modes (Lvac mode and wet mode) were compared. This offers new opportunities compared with ESEM-wet/Lvac-mode imaging, since wet-mode imaging involves a real contrast and gives an indication of the changes in cell morphology and structure required for cell viability. In this study, wet-mode imaging was optimized using the unique ability of cell quantities for microcharacterization in situ giving very fine features of topological effects. Accordingly, the progress is reported by comparing the results of these two modes, which demonstrate interesting application details. In general, the functional comparisons have revealed that the fresh unprocessed Saccharomyces cerevisiae cells (ESEM-wet mode) were essentially unaltered with improved and minimal specimen preparation timescales, and the optimal cell viability degree was visualized and also measured quantitatively while the cell size remained unchanged with continuous images.

Integrative investigation of metabolic and transcriptomic data.

BACKGROUND: New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods. RESULTS: Transcriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures--PLS) using PLS toolbox in Matlab. CONCLUSION: The experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model.

A novel topological centrality measure capturing biologically important proteins.

Topological centrality in protein interaction networks and its biological implications have widely been investigated in the past. In the present study, a novel metric of centrality-weighted sum of loads eigenvector centrality (WSL-EC)-based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality. Different metrics were found to selectively identify hub sets that are significantly associated with different biological processes. The widely accepted metrics degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs, whereas WSL-EC is not affected by the presence of super-hubs. WSL-EC outperforms other metrics of centrality in detecting biologically central nodes such as pathogen-interacting, cancer, ageing, HIV-1 or disease-related proteins and proteins involved in immune system processes and autoimmune diseases in the human interactome.

An integrative analysis of transcriptomic response of ethanol tolerant strains to ethanol in Saccharomyces cerevisiae.

The accumulation of ethanol is one of the main environmental stresses that Saccharomyces cerevisiae cells are exposed to in industrial alcoholic beverage and bioethanol production processes. Despite the known impacts of ethanol, the molecular mechanisms underlying ethanol tolerance are still not fully understood. Novel gene targets leading to ethanol tolerance were previously identified via a network approach and the investigations of the deletions of these genes resulted in the improved ethanol tolerance of pmt7Δ/pmt7Δ and yhl042wΔ/yhl042wΔ strains. In the present study, an integrative system based approach was used to investigate the global transcriptional changes in these two ethanol tolerant strains in response to ethanol and hence to elucidate the mechanisms leading to the observed tolerant phenotypes. In addition to strain specific biological processes, a number of common and already reported biological processes were found to be affected in the reference and both ethanol tolerant strains. However, the integrative analysis of the transcriptome with the transcriptional regulatory network and the ethanol tolerance network revealed that each ethanol tolerant strain had a specific organization of the transcriptomic response. Transcription factors around which most important changes occur were determined and active subnetworks in response to ethanol and functional clusters were identified in all strains.

Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations.

Cells respond to environmental and/or genetic perturbations in order to survive and proliferate. Characterization of the changes after various stimuli at different -omics levels is crucial to comprehend the adaptation of cells to the changing conditions. Genome-wide quantification and analysis of transcript levels, the genes affected by perturbations, extends our understanding of cellular metabolism by pointing out the mechanisms that play role in sensing the stress caused by those perturbations and related signaling pathways, and in this way guides us to achieve endeavors, such as rational engineering of cells or interpretation of disease mechanisms. Saccharomyces cerevisiae as a model system has been studied in response to different perturbations and corresponding transcriptional profiles were followed either statically or/and dynamically, short and long term. This review focuses on response of yeast cells to diverse stress inducing perturbations, including nutritional changes, ionic stress, salt stress, oxidative stress, osmotic shock, and to genetic interventions such as deletion and overexpression of genes. It is aimed to conclude on common regulatory phenomena that allow yeast to organize its transcriptomic response after any perturbation under different external conditions.

Effect of wastewater composition on archaeal population diversity.

Distribution and occurrence of Archaea and methanogenic activity in a laboratory scale, completely mixed anaerobic reactor treating pharmaceutical wastewaters were investigated and associated with reactor performance. The reactor was initially seeded with anaerobic digester sludge from an alcohol distillery wastewater treatment plant and was subjected to a three step feeding strategy. The feeding procedure involved gradual transition from a glucose containing feed to a solvent stripped pharmaceutical wastewater and then raw pharmaceutical wastewater. During the start-up period, over 90% COD removal efficiency at an organic loading rate (OLR) of 6 kg COD m(-3)d(-1) was achieved with glucose feeding, and acetoclastic methanogenic activity was 336 ml CH4 gTVS(-1)d(-1). At the end of the primary loading, when the feed contained solvent stripped pharmaceutical wastewater at full composition, 71% soluble COD removal efficiency was obtained and acetoclastic methanogenic activity decreased to half of the rate under glucose feed (166 ml CH4 gTVS(-1)d(-1)). At the end of secondary loading with 60% (w/v) raw pharmaceutical wastewater, COD removal dropped to zero and acetoclastic methanogenic activity fell to less than 10 ml CH4 gTVS(-1)d(-1). Throughout the course of the experiment, microbial community structure was monitored by DGGE analysis of 16S rRNA gene fragments. Five different archaeal taxa were identified and the predominant archaeal sequences belonged to methanogenic Archaea. Two of these showed greatest sequence identity with Methanobacterium formicicum and Methanosaeta concilii. The types of Archaea present changed little in response to changing feed composition but the relative contribution of different organisms identified in the archaeal DGGE profiles did change.

Biomass composition: the "elephant in the room" of metabolic modelling.

Genome-scale stoichiometric models, constrained to optimise biomass production are often used to predict mutant phenotypes. However, for Saccharomyces cerevisiae, the representation of biomass in its metabolic model has hardly changed in over a decade, despite major advances in analytical technologies. Here, we use the stoichiometric model of the yeast metabolic network to show that its ability to predict mutant phenotypes is particularly poor for genes encoding enzymes involved in energy generation. We then identify apparently inefficient energy-generating pathways in the model and demonstrate that the network suffers from the high energy burden associated with the generation of biomass. This is tightly connected to the availability of phosphate since this macronutrient links energy generation and structural biomass components. Variations in yeast's biomass composition, within experimentally-determined bounds, demonstrated that flux distributions are very sensitive to such changes and to the identity of the growth-limiting nutrient. The predictive accuracy of the yeast metabolic model is, therefore, compromised by its failure to represent biomass composition in an accurate and context-dependent manner.