I. MARCO CONTEXTUAL
1.3 Revolución Tecnológica y Paradigmas
Chapter 7
This chapter provides a global summary of the research discussions (and main findings) of the preceding chapters, supplemented with recent literature and research perspectives.
The starting point of genome-scale reconstruction of metabolism is the annotation of a sequenced genome. The annotation process includes the assignment of functions to genes and relies on comparative genomics by orthology1. Prediction of
orthology is often done by Bidirectional Best Hit (BBH) approaches that, in general, results in a single gene in one genome being predicted to be the ortholog of a single gene in the other genome (called one-to-one orthology). This approach can easily be applied at large-scale, in light of computational feasibility and interpretation, but the down-side is that recent gene duplication events are ignored. To study the effect of gene duplication on function prediction, we analyzed cases where in one species the orthologous gene has duplicated after the speciation of the two species (called inparalogs2). Thus, both inparalogs are orthologous to the single gene in the
other species. Although the most similar genes at the sequence level (i.e. one-to- one orthology) conserved gene neighborhood in the majority of cases, a substantial fraction showed the opposite. As gene neighborhood conservation often indicates functional equivalency, it suggests that ignoring gene duplicates could affect gene function prediction. In line with the original definition of orthology one should in principle include inparalogs and take both genes as equal likely candidates for function prediction. Given our findings it is beneficial to combine orthology with contextual information (e.g. gene neighborhood conservation) to classify functionally equivalent genes. This has recently been initialized for prokaryotes at large-scale3. A suitable
method for sequence similarity is the INPARANOID algorithm4, especially for large-
scale orthology predictions, because it includes inparalog detection (in combination with BBH). Recently, it has been shown that the definition of orthologs (and inparalogs) on the basis of phylogenetic trees of homologous genes can be automated5.
The strength of phylogenetic tree reconstructions is that sequence information of many species is considered which could lead to higher resolution orthology predictions. It could be analyzed to what extent large-scale phylogenetic tree reconstructions differ with algorithms like INPARANOID. Such a study might be relevant to select the most suitable orthology prediction approach.
On the basis of orthology prediction we developed a strategy to reconstruct genome-scale metabolic networks. These reconstructions are species-specific and provide a measure of the metabolic capability. Unfortunately the process of network reconstruction can be time-consuming. First, all genes encoding enzymes have to be determined. Secondly, the correct metabolic reaction catalyzed by the enzyme has to be assigned. Thirdly, the collection of reactions should form a network that converts nutrients into biomass components (amino acids, nucleotides, lipids, etc). This allows the organism to grow and reproduce. We presented a method to accelerate the process of network reconstruction for a query species. Metabolic reactions are predicted based
on well-curated networks of other species using orthology. In our study we focused on the prediction of the Lactococcus lactis IL1403 metabolic network which we compared to a published version6. We recovered most of the gene-reaction associations that were
present in the published version and we predicted many additional genes associated to reactions. A major fraction of these additional gene-reaction associations involved specific metabolic reactions, which could be useful additions to the metabolic network (see chapter 3 for details). Even though the actual choice whether or not these specific gene-reaction associations should be included, might depends on the exact goal of the (modeling) study, new associations are useful information for further improvement of the model. Especially in the context of integrative bioinformatics, any new gene- reaction association allows that specific gene to be studied in its metabolic context. In addition, we reconstructed the metabolism of L. lactis MG13637,8. This is a food-related
bacterium and model organism in the field of lactic acid bacteria. Currently, there is a rapid increase of metabolic reconstructions9, including high quality versions like the
one of E. coli (experienced 15 years of development and improvement)10. We developed
the method with the goal to accelerate the process of metabolic reconstruction, by minimizing the adjustments needed during curation, and by giving the curator an overview of the decisions made previously by other curators. We are aware of the fact that the quality of a metabolic network derived from our method depends on the availability and quality of manually curated metabolic networks, and on the orthology detection (see above). Our approach has been used to reconstruct the metabolisms of various organisms, such as lactic acid bacteria within the “Top Institute Food and Nutrition”1 and the “Kluyver Center for Genomics of Industrial Fermentation”11. The
practical relevance of the method is best illustrated by comparing the time-scale of the first and latest reconstruction. The first reconstruction (Lactobacillus plantarum) took about 2 years while the latest reconstruction (Lactobacullis bulgaricus) took a couple of days.
There are ongoing projects with the aim to automate the reconstruction of all sequenced genomes12. This raises the question what the additional value will be of
having all these networks. Several purposes can be considered. First, they can be used to visualize genomics data (e.g. gene expression) in data-driven research for any species of interest. Second, when one is interested in modeling a particular species it could form the basis for (rapid) manual curation. Third, in line with comparative genome analysis it becomes possible to infer similarities and differences in network structure (e.g. by network alignment). The latter is of interest in the analysis of evolutionary adaptations of networks, i.e. properties that increase the fit of the organism to their environment. A question that can be raised is whether differences in network structures reflect adaptation or chance historical events? Comparative approaches of (optimal) network structures in context of ecological / environmental characteristics may shed light on this question (see chapter 6 for details).
Chapter 7
Global functional properties of metabolism can be investigated by stoichiometric modeling13. Flux coupling14 is a large scale modeling framework to
infer the extent of flux coupling (or dependency) between reactions in the context of the entire network function (i.e. growth). It therefore provides a way to question why certain genes in the network show tight transcriptional co-regulation while others are more or less independently regulated. Prior studies showed that the degree of co- regulation between enzymatic genes decreases with their distance in the metabolic network15. Distance is defined as the minimal number of metabolites that separates
any two reactions in the network. It is nevertheless unexplained how a pure graph- theoretical measure of network distance relates to physiologically relevant functional associations. For the metabolism of Escherichia coli we examined whether gene co-regulation can be better explained by flux coupling than by network distance. We confirmed that genes coupled by their enzymatic fluxes show similar expression patterns, share transcriptional regulators and reside frequently in the same operon. Genes that show complete coupling in terms of their enzymatic fluxes (referred to as full coupling) are, however, not strictly organized within the same operon. This might be explained by the existence of specific expression mechanisms. ‘Just-in-time’16 is
such a mechanism in which genes are expressed within time intervals and this could be favored by gene organization in different operons. Furthermore, it is known that many levels of regulation, such as post-transcriptional and -translational modifications, exist between transcription and flux. Together with the fact that regulation can take place at individual enzyme level17 it cannot be expected, even for fully coupled genes, to observe
a strict correlation with transcriptional co-regulation. Although our results show that network distance has much less influence on co-regulation when considering flux coupling, it should be mentioned that network distance is not the only possible graph- theoretical measure. Therefore future studies may address whether more sophisticated measures could provide refined insights into gene co-regulation.
Besides a comparison of types of flux coupling with respect to gene co- regulation, a systematic study of directional flux coupling and its effect on genomic properties, such as genome evolution, was performed. Directional coupling represents an asymmetric relation between two enzymes, A and B, where the function of A depends on B, but B does not depend on A. As this relationship arises from the systems properties of metabolism, we expected this to be reflected in species-specific cellular properties, such as gene dispensability and expression, and also in genome evolution. This is important not only because asymmetric relations occur frequently (relative to full coupling), but also because asymmetry is largely ignored in the analysis of genomic data (main focus is on symmetry18,19). Interestingly, gene B is more frequently
gained across evolution at (ancestral) stages where both genes were not present. In addition, when one of the two genes is lost in evolution, it is in most cases gene A. These evolutionary events lead to asymmetric patterns in the presence and absence of genes across present day species which can be interpreted by the asymmetric coupling
of enzymatic fluxes. The predicted asymmetry was not only reflected in the evolution of genomes, but also in the genomics data of gene dispensability and expression. Future studies may address to what extent asymmetry is reflected in other cell properties, such as protein complex formation. For example, protein A functions only in complex with B, but B can function without A.
Taken together, modeling of genome-scale metabolic networks, using concepts like flux coupling, is beneficial for genomics to explain function and evolution of biological systems. It is useful in bioinformatics with the aim to evaluate function predictions from genomics data. The quality of benchmarks, used to test function predictions by computational tools, could be increased. This will also lead to a better estimate of prediction accuracy. Most importantly, integrative functional genomics and bioinformatics of metabolism will become more and more feasible given the rapid increase of genome-scale metabolic reconstructions (see above) and the availability of modeling tools9,20. This also includes future integration of high-
throughput metabolomics data which provides, through metabolite concentrations, a way to infer the direction of the reactions in the network21. The latter will influence
modeling since it constrains the space of possible flux states of the network. References
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3. Wu, H., Mao, F., Olman, V. & Xu, Y. (2007) Hierarchical classification of functionally equivalent genes in prokaryotes. Nucleic Acids Res, 35, 2125-40.
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17. Rossell, S., van der Weijden, C.C., Lindenbergh, A., van Tuijl, A., Francke, C., Bakker, B.M. & Westerhoff, H.V. (2006) Unraveling the complexity of flux regulation: a new method demonstrated for nutrient starvation in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A, 103, 2166-71.
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