Towards a quantitative prediction of the fluxome from the proteome

Advanced Modeling of Cellular Proliferation: Toward a Multi-scale Framework Coupling Cell Cycle to Metabolism by Integrating Logical and Constraint-Based Models

Biological functions require a coherent cross talk among multiple layers of regulation within the cell. Computational efforts that aim to understand how these layers are integrated across spatial, temporal, and functional scales represent a challenge in Systems Biology. We have developed a computational, multi-scale framework that couples cell cycle and metabolism networks in the budding yeast cell. Here we describe the methodology at the basis of this framework, which integrates on off-the-shelf logical (Boolean) models of a minimal yeast cell cycle with a constraint-based model of metabolism (i.e., the Yeast 7 metabolic network reconstruction). Models are implemented in Python code using the BooleanNet and COBRApy packages, respectively, and are connected through the Boolean logic. The methodology allows for incorporation of interaction data, and validation through -omics data. Furthermore, evolutionary strategies may be incorporated to explore regulatory structures underlying coherent cross talks among regulatory layers.

Handling Complexity in Animal and Plant Science Research-From Single to Functional Traits: Are We There Yet?

The current knowledge of the main factors governing livestock, crop and plant quality as well as yield in different species is incomplete. For example, this can be evidenced by the persistence of benchmark crop varieties for many decades in spite of the gains achieved over the same period. In recent years, it has been demonstrated that molecular breeding based on DNA markers has led to advances in breeding (animal and crops). However, these advances are not in the way that it was anticipated initially by the researcher in the field. According to several scientists, one of the main reasons for this was related to the evidence that complex target traits such as grain yield, composition or nutritional quality depend on multiple factors in addition to genetics. Therefore, some questions need to be asked: are the current approaches in molecular genetics the most appropriate to deal with complex traits such as yield or quality? Are the current tools for phenotyping complex traits enough to differentiate among genotypes? Do we need to change the way that data is collected and analysed?

Forward Individualized Medicine from Personal Genomes to Interactomes

When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.

Drug target identification through systems biology

To rationalise drug target selection, we should understand the role of putative targets in biological pathways quantitatively. We review here how experimental and computational network-based approaches can aid more rational drug target selection and illustrate this with results obtained for microbes and for cancer. Comparison of the drug response of biochemical networks in target cells and (healthy) host cells can reveal network-selective targets.

Robustness analysis of a constraint-based metabolic model links cell growth and proteomics of Thermoanaerobacter tengcongensis under temperature perturbation

The integration of omic data with metabolic networks has been demonstrated to be an effective approach to elucidate the underlying metabolic mechanisms in life. Because the metabolic pathways of Thermoanaerobacter tengcongensis (T. tengcongensis) are incomplete, we used a 1-(13)C-glucose culture to monitor intracellular isotope-labeled metabolites by GC/MS and identified the gap gene in glucose catabolism, Re-citrate synthase. Based on genome annotation and biochemical information, we reconstructed the metabolic network of glucose metabolism and amino acid synthesis in T. tengcongensis, including 253 reactions, 227 metabolites, and 236 genes. Furthermore, we performed constraint based modeling (CBM)-derived robustness analysis on the model to study the dynamic changes of the metabolic network. By perturbing the culture temperature from 75 to 55 °C, we collected the bacterial growth rates and differential proteomes. Assuming that protein abundance changes represent metabolic flux variations, we proposed that the robustness analysis of the CBM model could decipher the effect of proteome change on the bacterial growth under perturbation. For approximately 73% of the reactions, the predicted cell growth changes due to such reaction flux variations matched the observed cell growth data. Our study, therefore, indicates that differential proteome data can be integrated with metabolic network modeling and that robustness analysis is a strong method for representing the dynamic change in cell phenotypes under perturbation.

Systems biology of lactic acid bacteria: a critical review

Understanding the properties of a system as emerging from the interaction of well described parts is the most important goal of Systems Biology. Although in the practice of Lactic Acid Bacteria (LAB) physiology we most often think of the parts as the proteins and metabolites, a wider interpretation of what a part is can be useful. For example, different strains or species can be the parts of a community, or we could study only the chemical reactions as the parts of metabolism (and forgetting about the enzymes that catalyze them), as is done in flux balance analysis. As long as we have some understanding of the properties of these parts, we can investigate whether their interaction leads to novel or unanticipated behaviour of the system that they constitute.

There has been a tendency in the Systems Biology community to think that the collection and integration of data should continue ad infinitum, or that we will otherwise not be able to understand the systems that we study in their details. However, it may sometimes be useful to take a step back and consider whether the knowledge that we already have may not explain the system behaviour that we find so intriguing. Reasoning about systems can be difficult, and may require the application of mathematical techniques. The reward is sometimes the realization of unexpected conclusions, or in the worst case, that we still do not know enough details of the parts, or of the interactions between them.

We will discuss a number of cases, with a focus on LAB-related work, where a typical systems approach has brought new knowledge or perspective, often counterintuitive, and clashing with conclusions from simpler approaches. Also novel types of testable hypotheses may be generated by the systems approach, which we will illustrate. Finally we will give an outlook on the fields of research where the systems approach may point the way for the near future.

Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our interest in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to develop high performance microbes that are capable of producing biofuels with very high efficiency in order to compete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolic engineering, which can be considered as metabolic engineering integrated with systems biology and synthetic biology, have been developed. Systems metabolic engineering allows successful development of microbes that are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodiesel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers by metabolic engineering and systems metabolic engineering approaches are reviewed with relevant example cases. It is expected that systems metabolic engineering will be employed as an essential strategy for the development of microbial strains for industrial applications.

Functional identification in Lactobacillus reuteri of a PocR-like transcription factor regulating glycerol utilization and vitamin B12 synthesis

Background

Lactobacillus reuteri harbors the genes responsible for glycerol utilization and vitamin B₁₂ synthesis within a genetic island phylogenetically related to gamma-Proteobacteria. Within this island, resides a gene (lreu_1750) that based on its genomic context has been suggested to encode the regulatory protein PocR and presumably control the expression of the neighboring loci. However, this functional assignment is not fully supported by sequence homology, and hitherto, completely lacks experimental confirmation.

Results

In this contribution, we have overexpressed and inactivated the gene encoding the putative PocR in L. reuteri. The comparison of these strains provided metabolic and transcriptional evidence that this regulatory protein controls the expression of the operons encoding glycerol utilization and vitamin B₁₂ synthesis.

Conclusions

We provide clear experimental evidence for assigning Lreu_1750 as PocR in Lactobacillus reuteri. Our genome-wide transcriptional analysis further identifies the loci contained in the PocR regulon. The findings reported here could be used to improve the production-yield of vitamin B₁₂, 1,3-propanediol and reuterin, all industrially relevant compounds.

Metabolic adaptation of MDCK cells to different growth conditions: effects on catalytic activities of central metabolic enzymes

Lactate and ammonia are the most important waste products of central carbon metabolism in mammalian cell cultures. In particular during batch and fed-batch cultivations these toxic by-products are excreted into the medium in large amounts, and not only affect cell viability and productivity but often also prevent growth to high cell densities. The most promising approach to overcome such a metabolic imbalance is the replacement of one or several components in the culture medium. It has been previously shown that pyruvate can be substituted for glutamine in cultures of adherent Madin-Darby canine kidney (MDCK) cells. As a consequence, the cells not only released no ammonia but glucose consumption and lactate production were also reduced significantly. In this work, the impact of media changes on glucose and glutamine metabolism was further elucidated by using a high-throughput platform for enzyme activity measurements of mammalian cells. Adherent MDCK cells were grown to stationary and exponential phase in six-well plates in serum-containing GMEM supplemented with glutamine or pyruvate. A total number of 28 key metabolic enzyme activities of cell extracts were analyzed. The overall activity of the pentose phosphate pathway was up-regulated during exponential cell growth in pyruvate-containing medium suggesting that more glucose-6-phosphate was channeled into the oxidative branch. Furthermore, the anaplerotic enzymes pyruvate carboxylase and pyruvate dehydrogenase showed higher cell specific activities with pyruvate. An increase in cell specific activity was also found for NAD(+)-dependent isocitrate dehydrogenase, glutamate dehydrogenase, and glutamine synthetase in MDCK cells grown with pyruvate. It can be assumed that the increase in enzyme activities was required to compensate for the energy demand and to replenish the glutamine pool. On the other hand, the activities of glutaminolytic enzymes (e.g., alanine and aspartate transaminase) were decreased in cells grown with pyruvate, which seems to be related to a decreased glutamine metabolism.

Regulation and control of metabolic fluxes in microbes

After about ten years of research renaissance in metabolism, the present challenge is to understand how metabolic fluxes are controlled by a complex interplay of overlapping regulatory mechanisms. Reconstruction of various regulatory network topologies is steaming, illustrating that we underestimated the broad importance of post-translational modifications such as enzyme phosphorylation or acetylation for microbial metabolism. With the growing topological knowledge, the functional relevance of these regulatory events becomes an even more pressing need. A major knowledge gap resides in the regulatory network of protein-metabolite interactions, simply because we lacked pertinent methods for systematic analyses - but a start has now been made. Perhaps most dramatic was the conceptual shift in our perception of metabolism from an engine of cellular operation to a generator of input and feedback signals for regulatory circuits that govern many important decisions on cell proliferation, differentiation, death, and naturally metabolism.