13C-metabolic flux ratio and novel carbon path analyses confirmed that Trichoderma reesei uses primarily the respirative pathway also on the preferred carbon source glucose

Assessing the intracellular primary metabolic profile of Trichoderma reesei and Aspergillus niger grown on different carbon sources

Trichoderma reesei and Aspergillus niger are efficient biological platforms for the production of various industrial products, including cellulases and organic acids. Nevertheless, despite the extensive research on these fungi, integrated analyses of omics-driven approaches are still missing. In this study, the intracellular metabolic profile of T. reesei RUT-C30 and A. niger N402 strains grown on glucose, lactose, carboxymethylcellulose (CMC), and steam-exploded sugarcane bagasse (SEB) as carbon sources for 48 h was analysed by proton nuclear magnetic resonance. The aim was to verify the changes in the primary metabolism triggered by these substrates and use transcriptomics data from the literature to better understand the dynamics of the observed alterations. Glucose and CMC induced higher fungal growth whereas fungi grown on lactose showed the lowest dry weight. Metabolic profile analysis revealed that mannitol, trehalose, glutamate, glutamine, and alanine were the most abundant metabolites in both fungi regardless of the carbon source. These metabolites are of particular interest for the mobilization of carbon and nitrogen, and stress tolerance inside the cell. Their concomitant presence indicates conserved mechanisms adopted by both fungi to assimilate carbon sources of different levels of recalcitrance. Moreover, the higher levels of galactose intermediates in T. reesei suggest its better adaptation in lactose, whereas glycolate and malate in CMC might indicate activation of the glyoxylate shunt. Glycerol and 4-aminobutyrate accumulated in A. niger grown on CMC and lactose, suggesting their relevant role in these carbon sources. In SEB, a lower quantity and diversity of metabolites were identified compared to the other carbon sources, and the metabolic changes and higher xylanase and pNPGase activities indicated a better utilization of bagasse by A. niger. Transcriptomic analysis supported the observed metabolic changes and pathways identified in this work. Taken together, we have advanced the knowledge about how fungal primary metabolism is affected by different carbon sources, and have drawn attention to metabolites still unexplored. These findings might ultimately be considered for developing more robust and efficient microbial factories.

Engineering of filamentous fungi for efficient conversion of lignocellulose: Tools, recent advances and prospects

Filamentous fungi, as the main producers of lignocellulolytic enzymes in industry, need to be engineered to improve the economy of large-scale lignocellulose conversion. Investigation of the cellular processes involved in lignocellulolytic enzyme production, as well as optimization of enzyme mixtures for higher hydrolysis efficiency, have provided effective targets for the engineering of lignocellulolytic fungi. Recently, the development of efficient genetic manipulation systems in several lignocellulolytic fungi opens up the possibility of systems engineering of these strains. Here, we review the recent progresses made in the engineering of lignocellulolytic fungi and highlight the research gaps in this area.

13 C metabolic flux profiling of Pichia pastoris grown in aerobic batch cultures on glucose revealed high relative anabolic use of TCA cycle and limited incorporation of provided precursors of branched-chain amino acids

Carbon metabolism of Crabtree-negative yeast Pichia pastoris was profiled using ¹³ C nuclear magnetic resonance (NMR) to delineate regulation during exponential growth and to study the import of two precursors for branched-chain amino acid biosynthesis, α-ketoisovalerate and α-ketobutyrate. Cells were grown in aerobic batch cultures containing (a) only glucose, (b) glucose along with the precursors, or (c) glucose and Val. The study provided the following new insights. First, ¹³ C flux ratio analyses of central metabolism reveal an unexpectedly high anaplerotic supply of the tricarboxylic acid cycle for a Crabtree-negative yeast, and show that a substantial fraction of glucose catabolism proceeds through the pentose phosphate pathway. A comparison with previous flux ratio analyses for batch cultures of Crabtree-negative Pichia stipitis and Crabtree-positive Saccharomyces cerevisiae indicate that the overall regulation of central carbon metabolism in P. pastoris is intermediate in between P. stipitis and S. cerevisiae. Second, excess α-ketoisovalerate in the medium is not transported into the cytoplasm indicating that P. pastoris lacks a suitable transporter. In contrast, excess Val is efficiently taken up and largely fulfills demands for both Val and Leu for protein synthesis. Third, excess α-ketobutyrate is transported into the mitochondria for Ile biosynthesis. However, the import does not efficiently inhibit the synthesis of α-ketobutyrate from pyruvate indicating that P. pastoris has not been optimized evolutionarily to take full advantage of this carbon source. These findings have direct implications for preparing uniformly ² H,¹³ C,¹⁵ N-labeled proteins containing protonated Ile, Val, and Leu methyl groups in P. pastoris for NMR-based structural biology.

ENZYMES

Acetohydroxy acid isomeroreductase (EC 1.1.1.86), branched-chain amino acid aminotransferase (BCAT, EC 2.6.1.42), fumarase (EC 4.2.1.2), malic enzyme (EC 1.1.1.39/1.1.1.40), phosphoenolpyruvate carboxykinase (EC 4.1.1.49), pyruvate carboxylase (EC 6.4.1.1), pyruvate kinase (EC 2.7.1.40), l-serine hydroxymethyltransferase (EC 2.1.2.1), threonine aldolase (EC 4.1.2.5), threonine dehydratase (EC 4.3.1.19); transketolase (EC 2.2.1.1), transaldolase (EC 2.2.1.2).

Labelling analysis for ¹³C MFA using NMR spectroscopy

NMR spectroscopy is an efficient method for analyzing (13)C labelling of cellular metabolites. The strength of it is especially the ability to provide direct quantitative positional information on the (13)C labelling status of carbon atoms in metabolites. NMR spectroscopic methods allow also for detection of contiguously (13)C-labelled fragments in the carbon backbones of the metabolites. Furthermore, the recent developments of NMR spectroscopy hardware have substantially improved the sensitivity of the methods. In this chapter we describe a method for analyzing the (13)C labelling of the biomass amino acids for metabolic flux analysis, sample preparation for NMR spectroscopy, acquiring and processing the NMR spectra, and extracting the (13)C labelling information from the NMR data. Different NMR methods are applied depending on the (13)C labelling strategy chosen. These strategies include uniform (13)C labelling, positional (13)C labelling, or a combination of both. Not only the preparation of sample for analysis of (13)C labelling in proteinogenic amino acids in biomass is described, but also the necessary modifications to the method when analysis of (13)C labelling in free metabolic intermediates is of interest. Finally the strategies for using the different NMR-detected (13)C labelling data in (13)C MFA are discussed.

Development of highly efficient, low-cost lignocellulolytic enzyme systems in the post-genomic era

The current high cost of lignocellulolytic enzymes is a major bottleneck in the economic bioconversion of lignocellulosic biomass to fuels and chemicals. Fungal lignocellulolytic enzyme systems are secreted at high levels, making them the most promising starting points for further development of highly efficient lignocellulolytic enzyme systems. In this paper, recent advances in improvement of fungal lignocellulolytic enzyme systems are reviewed, with an emphasis on the achievements made using genomic approaches. A general strategy for lignocellulolytic enzyme system development is proposed, including the improvement of the hydrolysis efficiencies and productivities of current enzyme systems. The applications of genomic, transcriptomic and proteomic analysis methods in examining the composition of native enzyme systems, discovery of novel enzymes and synergistic proteins from natural sources, and understanding of regulatory mechanisms for lignocellulolytic enzyme biosynthesis are summarized. By combining systems biology and synthetic biology tools, engineered fungal strains are expected to produce high levels of optimized lignocellulolytic enzyme systems.

Computational tools for guided discovery and engineering of metabolic pathways

With a high demand for increasingly diverse chemicals, as well as sustainable synthesis for many existing chemicals, the chemical industry is increasingly looking to biosynthesis. The majority of biosynthesis examples of useful chemicals are either native metabolites made by an organism or the heterologous expression of known metabolic pathways into a more amenable host. For chemicals that no known biosynthetic route exists, engineers are increasingly relying on automated computational algorithms, as described here, to identify potential metabolic pathways. In this chapter, we review a broad range of approaches to predict novel metabolic pathways. Broadly, these can rely on biochemical databases to assemble known reactions into a new pathway or rely on generalized biochemical rules to predict unobserved enzymatic reactions that are likely feasible. Many programs are freely available and immediately useable by non-computationally experienced scientists.

Metabolic flux ratio analysis and multi-objective optimization revealed a globally conserved and coordinated metabolic response of E. coli to paraquat-induced oxidative stress

The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response. In this study, the intracellular metabolic flux ratios involved in the central carbon metabolism were determined by fractional (13)C-labeling and metabolic flux ratio analysis (MetaFoR) of the wild-type E. coli strain JM101 at an oxidative environment in a chemostat. We observed a significant increase in the flux through phosphoenolpyruvate carboxykinase (PEPCK), phosphoenolpyruvate carboxylase (PEPC), malic enzyme (MEZ) and serine hydroxymethyltransferase (SHMT). We applied an ε-constraint based multi-objective optimization to investigate the trade-off relationships between the biomass yield and the generation of reductive power using the in silico iJR904 genome-scale model of E. coli K-12. The theoretical metabolic redistribution supports that the trans-hydrogenase pathway should not play a direct role in the defence mounted by E. coli against oxidative stress. The agreement between the measured ratio and the theoretical redistribution established the significance of NADPH synthesis as the goal of the metabolic reprogramming that occurs in response to oxidative stress. Our work presents a framework that combines metabolic flux ratio analysis and multi-objective optimization to investigate the metabolic trade-offs that occur under varied environmental conditions. Our results led to the proposal that the metabolic response of E. coli to paraquat-induced oxidative stress is globally conserved and coordinated.

Integration of in vivo and in silico metabolic fluxes for improvement of recombinant protein production

The filamentous fungus Aspergillus niger is an efficient host for the recombinant production of the glycosylated enzyme fructofuranosidase, a biocatalyst of commercial interest for the synthesis of pre-biotic sugars. In batch culture on a minimal glucose medium, the recombinant strain A. niger SKAn1015, expressing the fructofuranosidase encoding suc1 gene secreted 45U/mL of the target enzyme, whereas the parent wild type SKANip8 did not exhibit production. The production of the recombinant enzyme induced a significant change of in vivo fluxes in central carbon metabolism, as assessed by (13)C metabolic flux ratio analysis. Most notably, the flux redistribution enabled an elevated supply of NADPH via activation of the cytosolic pentose phosphate pathway (PPP) and mitochondrial malic enzyme, whereas the flux through energy generating TCA cycle was reduced. In addition, the overall possible flux space of fructofuranosidase producing A. niger was investigated in silico by elementary flux mode analysis. This provided theoretical flux distributions for multiple scenarios with differing production capacities. Subsequently, the measured flux changes linked to improved production performance were projected into the in silico flux space. This provided a quantitative evaluation of the achieved optimization and a priority ranked target list for further strain engineering. Interestingly, the metabolism was shifted largely towards the optimum flux pattern by sole expression of the recombinant enzyme, which seems an inherent attractive property of A. niger. Selected fluxes, however, changed contrary to the predicted optimum and thus revealed novel targets-including reactions linked to NADPH metabolism and gluconate formation.

Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions

Background

Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution.

Results

The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated ¹³C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells.

Conclusions

The analysis of compartmentation of metabolic networks based on the measured ¹³C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured ¹³C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution indicates the presence of a separate pool of hexose phosphates that is channeled towards glycogen synthesis.

Trichoderma reesei RUT-C30--thirty years of strain improvement

The hypersecreting mutant Trichoderma reesei RUT-C30 (ATCC 56765) is one of the most widely used strains of filamentous fungi for the production of cellulolytic enzymes and recombinant proteins, and for academic research. The strain was obtained after three rounds of random mutagenesis of the wild-type QM6a in a screening program focused on high cellulase production and catabolite derepression. Whereas RUT-C30 achieves outstanding levels of protein secretion and high cellulolytic activity in comparison to the wild-type QM6a, recombinant protein production has been less successful. Here, we bring together and discuss the results from biochemical-, microscopic-, genomic-, transcriptomic-, glycomic- and proteomic-based research on the RUT-C30 strain published over the last 30 years.

Finding metabolic pathways using atom tracking

MOTIVATION

Finding novel or non-standard metabolic pathways, possibly spanning multiple species, has important applications in fields such as metabolic engineering, metabolic network analysis and metabolic network reconstruction. Traditionally, this has been a manual process, but the large volume of metabolic data now available has created a need for computational tools to automatically identify biologically relevant pathways.

RESULTS

We present new algorithms for finding metabolic pathways, given a desired start and target compound, that conserve a given number of atoms by tracking the movement of atoms through metabolic networks containing thousands of compounds and reactions. First, we describe an algorithm that identifies linear pathways. We then present a new algorithm for finding branched metabolic pathways. Comparisons to known metabolic pathways demonstrate that atom tracking enables our algorithms to avoid many unrealistic connections, often found in previous approaches, and return biologically meaningful pathways. Our results also demonstrate the potential of the algorithms to find novel or non-standard pathways that may span multiple organisms.

AVAILABILITY

The software is freely available for academic use at: http://www.kavrakilab.org/atommetanet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Inferring branching pathways in genome-scale metabolic networks

Background

A central problem in computational metabolic modelling is how to find biochemically plausible pathways between metabolites in a metabolic network. Two general, complementary frameworks have been utilized to find metabolic pathways: constraint-based modelling and graph-theoretical path finding approaches. In constraint-based modelling, one aims to find pathways where metabolites are balanced in a pseudo steady-state. Constraint-based methods, such as elementary flux mode analysis, have typically a high computational cost stemming from a large number of steady-state pathways in a typical metabolic network. On the other hand, graph-theoretical approaches avoid the computational complexity of constraint-based methods by solving a simpler problem of finding shortest paths. However, while scaling well with network size, graph-theoretic methods generally tend to return more false positive pathways than constraint-based methods.

Results

In this paper, we introduce a computational method, ReTrace, for finding biochemically relevant, branching metabolic pathways in an atom-level representation of metabolic networks. The method finds compact pathways which transfer a high fraction of atoms from source to target metabolites by considering combinations of linear shortest paths. In contrast to current steady-state pathway analysis methods, our method scales up well and is able to operate on genome-scale models. Further, we show that the pathways produced are biochemically meaningful by an example involving the biosynthesis of inosine 5'-monophosphate (IMP). In particular, the method is able to avoid typical problems associated with graph-theoretic approaches such as the need to define side metabolites or pathways not carrying any net carbon flux appearing in results. Finally, we discuss an application involving reconstruction of amino acid pathways of a recently sequenced organism demonstrating how measurement data can be easily incorporated into ReTrace analysis. ReTrace is licensed under GPL and is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/retrace/.

Conclusion

ReTrace is a useful method in metabolic path finding tasks, combining some of the best aspects in constraint-based and graph-theoretic methods. It finds use in a multitude of tasks ranging from metabolic engineering to metabolic reconstruction of recently sequenced organisms.