Reconstructing phylogeny from metabolic substrate-product relationships

<p> Abstract</p> <p>Background</p> <p>Many approaches utilize metabolic pathway information to reconstruct the phyletic tree of fully sequenced organisms, but how metabolic networks can add information to original genomic annotations has remained open.</p> <p>Methods</p> <p>We translated enzyme reactions assigned in 1075 organisms into substrate-product relationships to represent the metabolic information at a finer resolution than enzymes and compounds. Each organism was represented as a vector of substrate-product relationships and the phyletic tree was reconstructed by a simple hierarchical method. Obtained results were compared with several other approaches that use genome information and network properties.</p> <p>Results</p> <p>Phyletic trees without consideration of network properties can already extract organisms in anomalous environments. This efficient method can add insights to traditional genome-based phylogenetic reconstruction.</p> <p>Conclusions</p> <p>Structural relationship among metabolites can highlight parasitic or symbiont species such as spirochaete and clamydia. The method assists understanding of species-environment interaction when used in combination with traditional phylogenetic methods.</p

Reconstruction of phyletic trees by global alignment of multiple metabolic networks

Background: In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information. Results: We explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis. Conclusions: We demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.National Institutes of Health (U.S.) (Grant GM081871

PhyloPars: estimation of missing parameter values using phylogeny

A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments on the species of interest are not feasible. The PhyloPars web server provides a statistically consistent method that combines an incomplete set of empirical observations with the species phylogeny to produce a complete set of parameter estimates for all species. It builds upon a state-of-the-art evolutionary model, extended with the ability to handle missing data. The resulting approach makes optimal use of all available information to produce estimates that can be an order of magnitude more accurate than ad-hoc alternatives. Uploading a phylogeny and incomplete feature matrix suffices to obtain estimates of all missing values, along with a measure of certainty. Real-time cross-validation provides further insight in the accuracy and bias expected for estimated values. The server allows for easy, efficient estimation of metabolic parameters, which can benefit a wide range of fields including systems biology and ecology. PhyloPars is available at: http://www.ibi.vu.nl/programs/phylopars/

Reconstructing the functions of endosymbiotic Mollicutes in fungus-growing ants

International audienceMollicutes, a widespread class of bacteria associated with animals and plants, were recently identified as abundant abdominal endosymbionts in healthy workers of attine fungus-farming leaf-cutting ants. We obtained draft genomes of the two most common strains harbored by Panamanian fungus-growing ants. Reconstructions of their functional significance showed that they are independently acquired symbionts, most likely to decompose excess arginine consistent with the farmed fungal cultivars providing this nitrogen-rich amino-acid in variable quantities. Across the attine lineages, the relative abundances of the two Mollicutes strains are associated with the substrate types that foraging workers offer to fungus gardens. One of the symbionts is specific to the leaf-cutting ants and has special genomic machinery to catabolize citrate/glucose into acetate, which appears to deliver direct metabolic energy to the ant workers. Unlike other Mollicutes associated with insect hosts, both attine ant strains have complete phage-defense systems, underlining that they are actively maintained as mutualistic symbionts

Ancestral sequence reconstruction as an accessible tool for the engineering of biocatalyst stability

Synthetic biology is the engineering of life to imbue non-natural functionality. As such, synthetic biology has considerable commercial potential, where synthetic metabolic pathways are utilised to convert low value substrates into high value products. High temperature biocatalysis offers several system-level benefits to synthetic biology, including increased dilution of substrate, increased reaction rates and decreased contamination risk. However, the current gamut of tools available for the engineering of thermostable proteins are either expensive, unreliable, or poorly understood, meaning their adoption into synthetic biology workflows is treacherous. This thesis focuses on the development of an accessible tool for the engineering of protein thermostability, based on the evolutionary biology tool ancestral sequence reconstruction (ASR). ASR allows researchers to walk back in time along the branches of a phylogeny and predict the most likely representation of a protein family’s ancestral state. It also has simple input requirements, and its output proteins are often observed to be thermostable, making ASR tractable to protein engineering. Chapter 2 explores the applicability of multiple ASR methods to the engineering of a carboxylic acid reductase (CAR) biocatalyst. Despite the family emerging only 500 million years ago, ancestors presented considerable improvements in thermostability over their modern counterparts. We proceed to thoroughly characterise the ancestral enzymes for their inclusion into the CAR biocatalytic toolbox. Chapter 3 explores why ASR derived proteins may be thermostable despite a mesophilic history. An in silico toolbox for tracking models of protein stability over simulated evolutionary time at the sequence, protein and population level is built. We provide considerable evidence that the sequence alignments of simulated protein families that evolved at marginal stability are saturated with stabilising residues. ASR therefore derives sequences from a dataset biased toward stabilisation. Importantly, while ASR is accessible, it still requires a steep learning curve based on its requirements of phylogenetic expertise. In chapter 4, we utilise the evolutionary model produced in chapter 3 to develop a highly simplified and accessible ASR protocol. This protocol was then applied to engineer CAR enzymes that displayed dramatic increases in thermostability compared to both modern CARs and the thermostable AncCARs presented in chapter 2

Toward systems biology in brown algae to explore acclimation and adaptation to the shore environment.

International audienceBrown algae belong to a phylogenetic lineage distantly related to land plants and animals. They are almost exclusively found in the intertidal zone, a harsh and frequently changing environment where organisms are submitted to marine and terrestrial constraints. In relation with their unique evolutionary history and their habitat, they feature several peculiarities, including at the level of their primary and secondary metabolism. The establishment of Ectocarpus siliculosus as a model organism for brown algae has represented a framework in which several omics techniques have been developed, in particular, to study the response of these organisms to abiotic stresses. With the recent publication of medium to high throughput profiling data, it is now possible to envision integrating observations at the cellular scale to apply systems biology approaches. As a first step, we propose a protocol focusing on integrating heterogeneous knowledge gained on brown algal metabolism. The resulting abstraction of the system will then help understanding how brown algae cope with changes in abiotic parameters within their unique habitat, and to decipher some of the mechanisms underlying their (1) acclimation and (2) adaptation, respectively consequences of (1) the behavior or (2) the topology of the system resulting from the integrative approach

Metabolic classification of microbial genomes using functional probes

<p>Abstract</p> <p>Background</p> <p>Microorganisms able to grow under artificial culture conditions comprise only a small proportion of the biosphere's total microbial community. Until recently, scientists have been unable to perform thorough analyses of difficult-to-culture microorganisms due to limitations in sequencing technology. As modern techniques have dramatically increased sequencing rates and rapidly expanded the number of sequenced genomes, in addition to traditional taxonomic classifications which focus on the evolutionary relationships of organisms, classifications of the genomes based on alternative points of view may help advance our understanding of the delicate relationships of organisms.</p> <p>Results</p> <p>We have developed a proteome-based method for classifying microbial species. This classification method uses a set of probes comprising short, highly conserved amino acid sequences. For each genome, <it>in silico </it>translation is performed to obtained its proteome, based on which a probe-set frequency pattern is generated. Then, the probe-set frequency patterns are used to cluster the proteomes/genomes.</p> <p>Conclusions</p> <p>Features of the proposed method include a high running speed in challenge of a large number of genomes, and high applicability for classifying organisms with incomplete genome sequences. Moreover, the probe-set clustering method is sensitive to the metabolic phenotypic similarities/differences among species and is thus supposed potential for the classification or differentiation of closely-related organisms.</p

The secondary metabolite bioinformatics portal:Computational tools to facilitate synthetic biology of secondary metabolite production

AbstractNatural products are among the most important sources of lead molecules for drug discovery. With the development of affordable whole-genome sequencing technologies and other ‘omics tools, the field of natural products research is currently undergoing a shift in paradigms. While, for decades, mainly analytical and chemical methods gave access to this group of compounds, nowadays genomics-based methods offer complementary approaches to find, identify and characterize such molecules. This paradigm shift also resulted in a high demand for computational tools to assist researchers in their daily work. In this context, this review gives a summary of tools and databases that currently are available to mine, identify and characterize natural product biosynthesis pathways and their producers based on ‘omics data. A web portal called Secondary Metabolite Bioinformatics Portal (SMBP at http://www.secondarymetabolites.org) is introduced to provide a one-stop catalog and links to these bioinformatics resources. In addition, an outlook is presented how the existing tools and those to be developed will influence synthetic biology approaches in the natural products field