Analysis of metabolic evolution in bacteria using whole-genome metabolic models

Cyanobacterial phylogenetic analysis based on phylogenomics approaches render evolutionary diversification and adaptation: an overview of representative orders

Phylogenetic studies based on a definite set of marker genes usually reconstruct evolutionary relationships among the prokaryotic species. Based on specific target sequences, such studies represent variations and allow identification of similarities or dissimilarities in organisms. With the advent of completely sequenced genomes and accumulation of information on whole prokaryotic genomes, phylogenetic reconstructions should be considered more reliable if they are ideally based on entire genomes to resolve phylogenetic interest. We applied phylogenomics approaches taking into account completely sequenced cyanobacterial genomes to reconstruct underlying species that represented major taxonomic classes and belonged to distinctly different habitats (freshwater, marine, soils, and rocks). We did not rely on describing phylogeny of all representative class of cyanobacterial species on the basis of only ribosomal gene, 16S rDNA gene. In contrast, we analyzed combined molecular marker and phylogenomics approaches (genome alignment, gene content and gene order, composition vector and protein domain content) for accurately inferring phylogenetic relationship of species. We have shown that this approach reflects the impact of evolution on the organisms and considers connects with the ecological adaptation in cyanobacteria in different habitats. Analysis revealed that the members from marine habitat occupy different profile than those from freshwater. Impact of GC content and genomic repetitiveness over the diversification of cyanobacterial species and their possible role in adaptation was also reflected. Members occupying similar habitats cover more evolutionary distance together and also evolve various strategies for adaptation and survival either through genomic repetitiveness or preferences for genes of particular functions or modified GC content. Genomes undergo different changes for their adaptation in diverse habitats.

A Comprehensive Study of Cyanobacterial Morphological and Ecological Evolutionary Dynamics through Deep Geologic Time

Cyanobacteria have exerted a profound influence on the progressive oxygenation of Earth. As a complementary approach to examining the geologic record—phylogenomic and trait evolutionary analyses of extant species can lead to new insights. We constructed new phylogenomic trees and analyzed phenotypic trait data using novel phylogenetic comparative methods. We elucidated the dynamics of trait evolution in Cyanobacteria over billion-year timescales, and provide evidence that major geologic events in early Earth’s history have shaped—and been shaped by—evolution in Cyanobacteria. We identify a robust core cyanobacterial phylogeny and a smaller set of taxa that exhibit long-branch attraction artifacts. We estimated the age of nodes and reconstruct the ancestral character states of 43 phenotypic characters. We find high levels of phylogenetic signal for nearly all traits, indicating the phylogeny carries substantial predictive power. The earliest cyanobacterial lineages likely lived in freshwater habitats, had small cell diameters, were benthic or sessile, and possibly epilithic/endolithic with a sheath. We jointly analyzed a subset of 25 binary traits to determine whether rates of trait evolution have shifted over time in conjunction with major geologic events. Phylogenetic comparative analysis reveal an overriding signal of decreasing rates of trait evolution through time. Furthermore, the data suggest two major rate shifts in trait evolution associated with bursts of evolutionary innovation. The first rate shift occurs in the aftermath of the Great Oxidation Event and “Snowball Earth” glaciations and is associated with decrease in the evolutionary rates around 1.8–1.6 Ga. This rate shift seems to indicate the end of a major diversification of cyanobacterial phenotypes–particularly related to traits associated with filamentous morphology, heterocysts and motility in freshwater ecosystems. Another burst appears around the time of the Neoproterozoic Oxidation Event in the Neoproterozoic, and is associated with the acquisition of traits involved in planktonic growth in marine habitats. Our results demonstrate how uniting genomic and phenotypic datasets in extant bacterial species can shed light on billion-year old events in Earth’s history.

Derivative processes for modelling metabolic fluxes

Motivation: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations.

Results: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli.

Availability and implementation: R code is available from the authors upon request.

Contact:j.norkunaite@imperial.ac.uk; m.stumpf@imperial.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.