Flux tope analysis: studying the coordination of reaction directions in metabolic networks.

Motivation: Elementary flux mode (EFM) analysis allows an unbiased description of metabolic networks in terms of minimal pathways (involving a minimal set of reactions). To date, the enumeration of EFMs is impracticable in genome-scale metabolic models. In a complementary approach, we introduce the concept of a flux tope (FT), involving a maximal set of reactions (with fixed directions), which allows one to study the coordination of reaction directions in metabolic networks and opens a new way for EFM enumeration. Results: A FT is a (nontrivial) subset of the flux cone specified by fixing the directions of all reversible reactions. In a consistent metabolic network (without unused reactions), every FT contains a 'maximal pathway', carrying flux in all reactions. This decomposition of the flux cone into FTs allows the enumeration of EFMs (of individual FTs) without increasing the problem dimension by reaction splitting. To develop a mathematical framework for FT analysis, we build on the concepts of sign vectors and hyperplane arrangements. Thereby, we observe that FT analysis can be applied also to flux optimization problems involving additional (inhomogeneous) linear constraints. For the enumeration of FTs, we adapt the reverse search algorithm and provide an efficient implementation. We demonstrate that (biomass-optimal) FTs can be enumerated in genome-scale metabolic models of B.cuenoti and E.coli, and we use FTs to enumerate EFMs in models of M.genitalium and B.cuenoti. Availability and implementation: The source code is freely available at https://github.com/mpgerstl/FTA. Supplementary information: Supplementary data are available at Bioinformatics online.

Modulation of mammalian translation by a ribosome-associated tRNA half.

Originally considered futile degradation products, tRNA-derived RNA fragments (tdRs) have been shown over the recent past to be crucial players in orchestrating various cellular functions. Unlike other small non-coding RNA (ncRNA) classes, tdRs possess a multifaceted functional repertoire ranging from regulating transcription, apoptosis, RNA interference, ribosome biogenesis to controlling translation efficiency. A subset of the latter tdRs has been shown to directly target the ribosome, the central molecular machine of protein biosynthesis. Here we describe the function of the mammalian tRNAPro 5' half, a 35 residue long ncRNA associated with ribosomes and polysomes in several mammalian cell lines. Addition of tRNAPro halves to mammalian in vitro translation systems results in global translation inhibition and concomitantly causes the upregulation of a specific low molecular weight translational product. This tRNAPro 5' half-dependent translation product consists of both RNA and amino acids. Transfection of the tRNAPro half into HeLa cells leads to the formation of the same product in vivo. The migration of this product in acidic gels, the insensitivity to copper sulphate treatment, the resistance to 3' polyadenylation, and the association with 80S monosomes indicate that the accumulated product is peptidyl-tRNA. Our data thus suggest that binding of the tRNAPro 5' half to the ribosome leads to ribosome stalling and to the formation of peptidyl-tRNA. Our findings revealed a so far unknown functional role of a tdR thus further enlarging the functional heterogeneity of this emerging class of ribo-regulators.

From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints.

Elementary flux modes (EFMs) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. EFMs are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. EFMs account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis [FBA]), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of EFMs is not directly applicable anymore. For this reason, there has been a conceptual gap between EFM-based (pathway) analysis methods and linear optimization (FBA) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors (EFVs). Only recently has the community started to recognize the potential of EFVs for metabolic network analysis. In fact, EFVs exactly represent the conceptual development required to generalize the idea of EFMs from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to EFVs that is accessible to a broad audience. We highlight the close relationship between EFMs and EFVs and demonstrate that almost all applications of EFMs (in flux cones) are possible for EFVs (in flux polyhedra) as well. In fact, certain properties can only be studied with EFVs. Thus, we conclude that EFVs provide a powerful and unifying framework for constraint-based modeling of metabolic networks.

Exact quantification of cellular robustness in genome-scale metabolic networks.

MOTIVATION: Robustness, the ability of biological networks to uphold their functionality in spite of perturbations, is a key characteristic of all living systems. Although several theoretical approaches have been developed to formalize robustness, it still eludes an exact quantification. Here, we present a rigorous and quantitative approach for the structural robustness of metabolic networks by measuring their ability to tolerate random reaction (or gene) knockouts. RESULTS: In analogy to reliability theory, based on an explicit consideration of all possible knockout sets, we exactly quantify the probability of failure for a given network function (e.g. growth). This measure can be computed if the network's minimal cut sets (MSCs) are known. We show that even in genome-scale metabolic networks the probability of (network) failure can be reliably estimated from MSCs with lowest cardinalities. We demonstrate the applicability of our theory by analyzing the structural robustness of multiple Enterobacteriaceae and Blattibacteriaceae and show a dramatically low structural robustness for the latter. We find that structural robustness develops from the ability to proliferate in multiple growth environments consistent with experimentally found knowledge. CONCLUSION: The probability of (network) failure provides thus a reliable and easily computable measure of structural robustness and redundancy in (genome-scale) metabolic networks. AVAILABILITY AND IMPLEMENTATION: Source code is available under the GNU General Public License at https://github.com/mpgerstl/networkRobustnessToolbox CONTACT: juergen.zanghellini@boku.ac.at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

tEFMA: computing thermodynamically feasible elementary flux modes in metabolic networks.

UNLABELLED: : Elementary flux modes (EFMs) are important structural tools for the analysis of metabolic networks. It is known that many topologically feasible EFMs are biologically irrelevant. Therefore, tools are needed to find the relevant ones. We present thermodynamic tEFM analysis (tEFMA) which uses the cellular metabolome to avoid the enumeration of thermodynamically infeasible EFMs. Specifically, given a metabolic network and a not necessarily complete metabolome, tEFMA efficiently returns the full set of thermodynamically feasible EFMs consistent with the metabolome. Compared with standard approaches, tEFMA strongly reduces the memory consumption and the overall runtime. Thus tEFMA provides a new way to analyze unbiasedly hitherto inaccessible large-scale metabolic networks. AVAILABILITY AND IMPLEMENTATION: https://github.com/mpgerstl/tEFMA CONTACT: : christian.jungreuthmayer@boku.ac.at or juergen.zanghellini@boku.ac.at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The relevance of interfaces for oxide ion transport in yttria stabilized zirconia (YSZ) thin films.

Thin YSZ films were prepared on magnesia, sapphire and strontium titanate (STO) single crystals using pulsed laser deposition and, for comparison, by a sol-gel method on STO. The bulk and interfacial mass and charge transport properties of these films were investigated by complementary impedance spectroscopy and tracer diffusion measurements. In this context, a novel two-step tracer diffusion experiment is introduced. For YSZ films on sapphire and magnesia, grain bulk conductivities similar to those of polycrystalline samples were measured in most cases. Strongly blocking grain boundaries could be identified by impedance measurements. The films on sapphire and magnesia also exhibited good agreement between effective transport properties of impedance and tracer measurements. YSZ layers on strontium titanate single crystals, on the other hand, showed a strongly increased effective conductivity in impedance studies. However, in tracer diffusion experiments this could be unambiguously attributed to conduction in the substrate while the diffusion coefficient of YSZ on STO was comparable to that of YSZ films on other substrates. Moreover, the tracer diffusion experiments did not indicate any significant increase of oxide ion mobility on a free YSZ surface compared to a Pt|YSZ interface.

Environmental flexibility does not explain metabolic robustness.

Cells show remarkable resilience against genetic and environmental perturbations. However, its evolutionary origin remains obscure. In order to leverage methods of systems biology for examining cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in genome-scale metabolic models based on concepts prevalent in reliability engineering and fault analysis. The probability of failure (PoF) is defined as the (weighted) portion of all possible combinations of loss-of-function mutations that disable network functionality. It can be exactly determined if all essential reactions, synthetic lethal pairs of reactions, synthetic lethal triplets of reactions etc. are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. Herein, we demonstrate that even at the genome scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF with reasonable accuracy. Building on an improved theoretical understanding, we analysed the robustness of 489 E. coli, Shigella, Salmonella, and fungal genome-scale metabolic models (GSMMs). In contrast to the popular "congruence theory", which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between network robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that amino acid synthesis rather than carbon metabolism dominates metabolic robustness.

Genetic and Epigenetic Variation across Genes Involved in Energy Metabolism and Mitochondria of Chinese Hamster Ovary Cell Lines.

The increasingdemandfor biopharmaceutical products drives the search for efficient cell factories that are able to sustainably support rapid growth, high productivity, and product quality. As these depend on energy generation, here the genomic variation in nuclear genes associated with mitochondria and energy metabolism and the mitochondrial genome of 14 cell lines is investigated. The variants called enable reliable tracing of lineages. Unique sequence variations are observed in cell lines adapted to grow in protein-free media, enriched in signaling pathways or mitogen-activated protein kinase 3. High-producing cell lines bear unique mutations in nicotinamide adenine dinucleotide (NADH) dehydrogenase (ND2 and ND4) and in peroxisomal acyl-CoA synthetase (ACSL4), involved in lipid metabolism. As phenotypes are determined not only by functional mutations, but also by the exquisite regulation of expression patterns, it is not surprising that ≈50% of the genes investigated here are found to be differentially methylated and thus epigenetically controlled, enabling a clear distinction of high producers, and cells adapted to a minimal, glutamine (Gln)-free medium. Similar pathways are enriched as those identified by genome variation. This strengthens the hypothesis that these phenomena act together to define cell behavior.

Designing Optimized Production Hosts by Metabolic Modeling.

Many of the complex and expensive production steps in the chemical industry are readily available in living cells. In order to overcome the metabolic limits of these cells, the optimal genetic intervention strategies can be computed by the use of metabolic modeling. Elementary flux mode analysis (EFMA) is an ideal tool for this task, as it does not require defining a cellular objective function. We present two EFMA-based methods to optimize production hosts: (1) the standard approach that can only be used for small and medium scale metabolic networks and (2) the advanced dual system approach that can be utilized to directly compute intervention strategies in a genome-scale metabolic model.

CHOmine: an integrated data warehouse for CHO systems biology and modeling.

The last decade has seen a surge in published genome-scale information for Chinese hamster ovary (CHO) cells, which are the main production vehicles for therapeutic proteins. While a single access point is available at www.CHOgenome.org, the primary data is distributed over several databases at different institutions. Currently research is frequently hampered by a plethora of gene names and IDs that vary between published draft genomes and databases making systems biology analyses cumbersome and elaborate. Here we present CHOmine, an integrative data warehouse connecting data from various databases and links to other ones. Furthermore, we introduce CHOmodel, a web based resource that provides access to recently published CHO cell line specific metabolic reconstructions. Both resources allow to query CHO relevant data, find interconnections between different types of data and thus provides a simple, standardized entry point to the world of CHO systems biology. Database URL: http://www.chogenome.org.

What can mathematical modelling say about CHO metabolism and protein glycosylation?

Chinese hamster ovary cells have been in the spotlight for process optimization in recent years, due to being the major, long established cell factory for the production of recombinant proteins. A deep, quantitative understanding of CHO metabolism and mechanisms involved in protein glycosylation has proven to be attainable through the development of high throughput technologies. Here we review the most notable accomplishments in the field of modelling CHO metabolism and protein glycosylation.

Which sets of elementary flux modes form thermodynamically feasible flux distributions?

Elementary flux modes (EFMs) are non-decomposable steady-state fluxes through metabolic networks. Every possible flux through a network can be described as a superposition of EFMs. The definition of EFMs is based on the stoichiometry of the network, and it has been shown previously that not all EFMs are thermodynamically feasible. These infeasible EFMs cannot contribute to a biologically meaningful flux distribution. In this work, we show that a set of thermodynamically feasible EFMs need not be thermodynamically consistent. We use first principles of thermodynamics to define the feasibility of a flux distribution and present a method to compute the largest thermodynamically consistent sets (LTCSs) of EFMs. An LTCS contains the maximum number of EFMs that can be combined to form a thermodynamically feasible flux distribution. As a case study we analyze all LTCSs found in Escherichia coli when grown on glucose and show that only one LTCS shows the required phenotypical properties. Using our method, we find that in our E. coli model < 10% of all EFMs are thermodynamically relevant.

The Sulphur Poisoning Behaviour of Gadolinia Doped Ceria Model Systems in Reducing Atmospheres.

An array of analytical methods including surface area determination by gas adsorption using the Brunauer, Emmett, Teller (BET) method, combustion analysis, XRD, ToF-SIMS, TEM and impedance spectroscopy has been used to investigate the interaction of gadolinia doped ceria (GDC) with hydrogen sulphide containing reducing atmospheres. It is shown that sulphur is incorporated into the GDC bulk and might lead to phase changes. Additionally, high concentrations of silicon are found on the surface of model composite microelectrodes. Based on these data, a model is proposed to explain the multi-facetted electrochemical degradation behaviour encountered during long term electrochemical measurements. While electrochemical bulk properties of GDC stay largely unaffected, the surface polarisation resistance is dramatically changed, due to silicon segregation and reaction with adsorbed sulphur.

Metabolomics integrated elementary flux mode analysis in large metabolic networks.

Elementary flux modes (EFMs) are non-decomposable steady-state pathways in metabolic networks. They characterize phenotypes, quantify robustness or identify engineering targets. An EFM analysis (EFMA) is currently restricted to medium-scale models, as the number of EFMs explodes with the network's size. However, many topologically feasible EFMs are biologically irrelevant. We present thermodynamic EFMA (tEFMA), which calculates only the small(er) subset of thermodynamically feasible EFMs. We integrate network embedded thermodynamics into EFMA and show that we can use the metabolome to identify and remove thermodynamically infeasible EFMs during an EFMA without losing biologically relevant EFMs. Calculating only the thermodynamically feasible EFMs strongly reduces memory consumption and program runtime, allowing the analysis of larger networks. We apply tEFMA to study the central carbon metabolism of E. coli and find that up to 80% of its EFMs are thermodynamically infeasible. Moreover, we identify glutamate dehydrogenase as a bottleneck, when E. coli is grown on glucose and explain its inactivity as a consequence of network embedded thermodynamics. We implemented tEFMA as a Java package which is available for download at https://github.com/mpgerstl/tEFMA.

Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs.

Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle.

Methylation of ribosomal RNA by NSUN5 is a conserved mechanism modulating organismal lifespan.

Several pathways modulating longevity and stress resistance converge on translation by targeting ribosomal proteins or initiation factors, but whether this involves modifications of ribosomal RNA is unclear. Here, we show that reduced levels of the conserved RNA methyltransferase NSUN5 increase the lifespan and stress resistance in yeast, worms and flies. Rcm1, the yeast homologue of NSUN5, methylates C2278 within a conserved region of 25S rRNA. Loss of Rcm1 alters the structural conformation of the ribosome in close proximity to C2278, as well as translational fidelity, and favours recruitment of a distinct subset of oxidative stress-responsive mRNAs into polysomes. Thus, rather than merely being a static molecular machine executing translation, the ribosome exhibits functional diversity by modification of just a single rRNA nucleotide, resulting in an alteration of organismal physiological behaviour, and linking rRNA-mediated translational regulation to modulation of lifespan, and differential stress response.

Controversial role of gamma-glutamyl transferase activity in cisplatin nephrotoxicity.

Nephrotoxicity of chemotherapeutics is a major hindrance in the treatment of various tumors. Therefore, test systems that reflect mechanisms of human kidney toxicity are necessary, and to reduce animal testing cell culture based systems have to be developed. One cell type that is of specific interest in this regard are renal proximal tubular epithelial cells, as they reabsorb substances from human primary urine filtrates and thus are exposed to urinary excreted xenobiotics and are a major target of cisplatin toxicity. While animal studies using gamma glutamyl transferase (GGT) knock-out mice or GGT inhibitors show that GGT activity increases kidney toxicity of cisplatin, the use of various cell models gives contradictory results. We therefore used a cell panel of immortalized human renal proximal tubular epithelial (RPTECs) cell lines differing in GGT activity. Low GGT activity resulted in high cisplatin sensitivity, as observed in RPTEC-SV40 cells or after siRNA mediated knock-down of GGT in RPTEC/TERT1 cells that have high GGT activity. However, the addition of GGT did not rescue, but also increased cisplatin sensitivity and adding GGT inhibitor as well as substrate (glutathione) or product (cysteinyl-glycine) of GGT resulted in decreased sensitivity. While our data suggest that the use of cell panels are of value in toxicology and toxicogenomics, they also emphasize on the complex interplay of toxins with the intracellular and extracellular microenvironment. In addition, we hypothesize that especially epithelial barrier formation and polarity of RPTECs need to be considered in toxicity models to validly predict the in vivo situation.

Prediction of transcribed PIWI-interacting RNAs from CHO RNAseq data.

Chinese hamster ovary (CHO) cells are currently the most important mammalian host for the manufacture of biopharmaceuticals. To enhance our understanding of cellular processes, pathways, and the genetic setup of CHO cell lines, we predicted PIWI interacting RNAs (piRNAs) from small RNA sequencing data. Although piRNAs are the least understood class of small non-coding RNAs that mediate RNA silencing, it is believed that they play a pivotal role in protecting genome integrity by repressing transposable elements. Since genomic integrity is the key to prolonged stability of recombinant CHO cell lines, we characterized piRNA sequences and expression in six CHO cell lines by computational analysis of an existing small RNA sequencing dataset using proTRAC and the published CHO genome as reference. Here we present the result of this analysis consisting of 25,626 piRNAs and 540 piRNA clusters. Moreover we provide first evidence for differential piRNA expression in adherent and suspension-adapted CHO-K1 and DUKXB11 host cell lines as well as their recombinant derivatives, indicating that piRNAs might be tools for cell line development and engineering.