Comparative genomics of metabolic networks of free-living and parasitic eukaryotes

Weighting dissimilarities to detect communities in networks

Many complex systems can be described as networks exhibiting inner organization as communities of nodes. The identification of communities is a key factor to understand community-based functionality. We propose a family of measures based on the weighted sum of two dissimilarity quantifiers that facilitates efficient classification of communities by tuning the quantifiers' relative weight to the network's particularities. Additionally, two new dissimilarities are introduced and incorporated in our analysis. The effectiveness of our approach is tested by examining the Zachary's Karate Club Network and the Caenorhabditis elegans reactions network. The analysis reveals the method's classification power as confirmed by the efficient detection of intrapathway metabolic functions in C. elegans.

The genome of the myxosporean Thelohanellus kitauei shows adaptations to nutrient acquisition within its fish host

Members of Myxozoa, a parasitic metazoan taxon, have considerable detrimental effects on fish hosts and also have been associated with human food-borne illness. Little is known about their biology and metabolism. Analysis of the genome of Thelohanellus kitauei and comparative analysis with genomes of its two free-living cnidarian relatives revealed that T. kitauei has adapted to parasitism, as indicated by the streamlined metabolic repertoire and the tendency toward anabolism rather than catabolism. Thelohanellus kitauei mainly secretes proteases and protease inhibitors for nutrient digestion (parasite invasion), and depends on endocytosis (mainly low-density lipoprotein receptors-mediated type) and secondary carriers for nutrient absorption. Absence of both classic and complementary anaerobic pathways and gluconeogenesis, the lack of de novo synthesis and reduced activity in hydrolysis of fatty acids, amino acids, and nucleotides indicated that T. kitauei in this vertebrate host-parasite system has adapted to inhabit a physiological environment extremely rich in both oxygen and nutrients (especially glucose), which is consistent with its preferred parasitic site, that is, the host gut submucosa. Taking advantage of the genomic and transcriptomic information, 23 potential nutrition-related T. kitauei-specific chemotherapeutic targets were identified. This first genome sequence of a myxozoan will facilitate development of potential therapeutics for efficient control of myxozoan parasites and ultimately prevent myxozoan-induced fish-borne illnesses in humans.

Simple topological properties predict functional misannotations in a metabolic network

Motivation: Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organism’s metabolic network; a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation.

Results: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations. The predictions are tested at three different levels. A random forest using topological features of the metabolic network and trained on curated sets of correct and incorrect enzyme assignments was found to have an accuracy of up to 86% in 5-fold cross-validation experiments. Further cross-validation against unseen enzyme superfamilies indicates that this classifier can successfully extrapolate beyond the classes of enzyme present in the training data. The random forest model was applied to several automated genome annotations, achieving an accuracy of in most cases when validated against recent genome-scale metabolic models. We also observe that when applied to draft metabolic networks for multiple species, a clear negative correlation is observed between predicted annotation quality and phylogenetic distance to the major model organism for biochemistry (Escherichia coli for prokaryotes and Homo sapiens for eukaryotes).

Contact:j.pinney@imperial.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Exploration of the core metabolism of symbiotic bacteria

Background

A large number of genome-scale metabolic networks is now available for many organisms, mostly bacteria. Previous works on minimal gene sets, when analysing host-dependent bacteria, found small common sets of metabolic genes. When such analyses are restricted to bacteria with similar lifestyles, larger portions of metabolism are expected to be shared and their composition is worth investigating. Here we report a comparative analysis of the small molecule metabolism of symbiotic bacteria, exploring common and variable portions as well as the contribution of different lifestyle groups to the reduction of a common set of metabolic capabilities.

Results

We found no reaction shared by all the bacteria analysed. Disregarding those with the smallest genomes, we still do not find a reaction core, however we did find a core of biochemical capabilities. While obligate intracellular symbionts have no core of reactions within their group, extracellular and cell-associated symbionts do have a small core composed of disconnected fragments. In agreement with previous findings in Escherichia coli, their cores are enriched in biosynthetic processes whereas the variable metabolisms have similar ratios of biosynthetic and degradation reactions. Conversely, the variable metabolism of obligate intracellular symbionts is enriched in anabolism.

Conclusion

Even when removing the symbionts with the most reduced genomes, there is no core of reactions common to the analysed symbiotic bacteria. The main reason is the very high specialisation of obligate intracellular symbionts, however, host-dependence alone is not an explanation for such absence. The composition of the metabolism of cell-associated and extracellular bacteria shows that while they have similar needs in terms of the building blocks of their cells, they have to adapt to very distinct environments. On the other hand, in obligate intracellular bacteria, catabolism has largely disappeared, whereas synthetic routes appear to have been selected for depending on the nature of the symbiosis. As more genomes are added, we expect, based on our simulations, that the core of cell-associated and extracellular bacteria continues to diminish, converging to approximately 60 reactions.

The evolution of metabolic enzymes in Plasmodium and trypanosomatids as compared to Saccharomyces and Schizosaccharomyces

Understanding how the biological connectivity of genes and gene products affects evolution is an important aspect of understanding evolution. Genes encoding enzymes are frequently used to carry out such analyses. Interestingly, studies have shown that connectivity in the metabolic networks in parasitic protists, including Plasmodium falciparum and Trypanosoma brucei, have been substantially altered as compared to free living eukaryotes, such as Saccharomyces cerevisiae. Herein, we have determined K(a) values, which are a measure of the non-synonymous substitution rate, and used them to examine the differences between the evolution of genes in T. brucei, P. falciparum, S. cerevisiae, and Schizosaccharomyces pombe. All four organisms share similar traits with respect to the evolution of genes encoding metabolic enzymes. First, genes encoding metabolic enzymes have lower K(a) values than genes encoding non-metabolic proteins. In addition, perturbations of the metabolic network appear to have limited affects on the genes encoding enzymes near the perturbation. In most cases, there is a negative relationship between connectivity in the metabolic network of the gene product and the K(a) value for the gene, i.e. examining how much constraint there is on gene evolution when it is connected to many other genes. In addition, we find that the K(a) values of orthologs encoding for metabolic enzymes in each organism are significantly correlated, indicating similar patterns of non-synonymous substitutions. In total, our results indicate that the evolution of genes encoding metabolic enzymes do not tend to be greatly affected by changes in the metabolic network.