Robin W. Palfreyman

AraGEM, a Genome-Scale Reconstruction of the Primary Metabolic Network in Arabidopsis

Genome-scale metabolic network models have been successfully used to describe metabolism in a variety of microbial organisms as well as specific mammalian cell types and organelles. This systems-based framework enables the exploration of global phenotypic effects of gene knockouts, gene insertion, and up-regulation of gene expression. We have developed a genome-scale metabolic network model (AraGEM) covering primary metabolism for a compartmentalized plant cell based on the Arabidopsis (Arabidopsis thaliana) genome. AraGEM is a comprehensive literature-based, genome-scale metabolic reconstruction that accounts for the functions of 1,419 unique open reading frames, 1,748 metabolites, 5,253 gene-enzyme reaction-association entries, and 1,567 unique reactions compartmentalized into the cytoplasm, mitochondrion, plastid, peroxisome, and vacuole. The curation process identified 75 essential reactions with respective enzyme associations not assigned to any particular gene in the Kyoto Encyclopedia of Genes and Genomes or AraCyc. With the addition of these reactions, AraGEM describes a functional primary metabolism of Arabidopsis. The reconstructed network was transformed into an in silico metabolic flux model of plant metabolism and validated through the simulation of plant metabolic functions inferred from the literature. Using efficient resource utilization as the optimality criterion, AraGEM predicted the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism.

C4GEM, a Genome-Scale Metabolic Model to Study C4 Plant Metabolism

Leaves of C4 grasses (such as maize [Zea mays], sugarcane [Saccharum officinarum], and sorghum [Sorghum bicolor]) form a classical Kranz leaf anatomy. Unlike C3 plants, where photosynthetic CO2 fixation proceeds in the mesophyll (M), the fixation process in C4 plants is distributed between two cell types, the M cell and the bundle sheath (BS) cell. Here, we develop a C4 genome-scale model (C4GEM) for the investigation of flux distribution in M and BS cells during C4 photosynthesis. C4GEM, to our knowledge, is the first large-scale metabolic model that encapsulates metabolic interactions between two different cell types. C4GEM is based on the Arabidopsis (Arabidopsis thaliana) model (AraGEM) but has been extended by adding reactions and transporters responsible to represent three different C4 subtypes (NADP-ME [for malic enzyme], NAD-ME, and phosphoenolpyruvate carboxykinase). C4GEM has been validated for its ability to synthesize 47 biomass components and consists of 1,588 unique reactions, 1,755 metabolites, 83 interorganelle transporters, and 29 external transporters (including transport through plasmodesmata). Reactions in the common C4 model have been associated with well-annotated C4 species (NADP-ME subtypes): 3,557 genes in sorghum, 11,623 genes in maize, and 3,881 genes in sugarcane. The number of essential reactions not assigned to genes is 131, 135, and 156 in sorghum, maize, and sugarcane, respectively. Flux balance analysis was used to assess the metabolic activity in M and BS cells during C4 photosynthesis. Our simulations were consistent with chloroplast proteomic studies, and C4GEM predicted the classical C4 photosynthesis pathway and its major effect in organelle function in M and BS. The model also highlights differences in metabolic activities around photosystem I and photosystem II for three different C4 subtypes. Effects of CO2 leakage were also explored. C4GEM is a viable framework for in silico analysis of cell cooperation between M and BS cells during photosynthesis and can be used to explore C4 plant metabolism.

AlgaGEM - a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome

BackgroundMicroalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for in silico analysis and design. While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data.ResultsWe have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and identified new targets to further improve H2 yield.ConclusionsAlgaGEM is a viable and comprehensive framework for in silico functional analysis and can be used to derive new, non-trivial hypotheses for exploring this metabolically versatile organism. Flux balance analysis can be used to identify bottlenecks and new targets to metabolically engineer microalgae for production of biofuels.

Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals

BackgroundOur view of host-associated microbiota remains incomplete due to the presence of as yet uncultured constituents. The Bacteroidales family S24-7 is a prominent example of one of these groups. Marker gene surveys indicate that members of this family are highly localized to the gastrointestinal tracts of homeothermic animals and are increasingly being recognized as a numerically predominant member of the gut microbiota; however, little is known about the nature of their interactions with the host.ResultsHere, we provide the first whole genome exploration of this family, for which we propose the name “Candidatus Homeothermaceae,” using 30 population genomes extracted from fecal samples of four different animal hosts: human, mouse, koala, and guinea pig. We infer the core metabolism of “Ca. Homeothermaceae” to be that of fermentative or nanaerobic bacteria, resembling that of related Bacteroidales families. In addition, we describe three trophic guilds within the family, plant glycan (hemicellulose and pectin), host glycan, and α-glucan, each broadly defined by increased abundance of enzymes involved in the degradation of particular carbohydrates.Conclusions“Ca. Homeothermaceae” representatives constitute a substantial component of the murine gut microbiota, as well as being present within the human gut, and this study provides important first insights into the nature of their residency. The presence of trophic guilds within the family indicates the potential for niche partitioning and specific roles for each guild in gut health and dysbiosis.

Saccharopolyspora erythraea’s genome is organised in high-order transcriptional regions mediated by targeted degradation at the metabolic switch

BackgroundActinobacteria form a major bacterial phylum that includes numerous human pathogens. Actinobacteria are primary contributors to carbon cycling and also represent a primary source of industrial high value products such as antibiotics and biopesticides. Consistent with other members of the actinobacterial phylum, Saccharopolyspora erythraea undergo a transitional switch. This switch is characterized by numerous metabolic and morphological changes.ResultsWe performed RNA sequencing to analyze the transcriptional changes that occur during growth of Saccharopolyspora erythraea in batch culture. By sequencing RNA across the fermentation time course, at a mean coverage of 4000X, we found the vast majority of genes to be prominently expressed, showing that we attained close to saturating sequencing coverage of the transcriptome. During the metabolic switch, global changes in gene expression influence the metabolic machinery of Saccharopolyspora erythraea, resetting an entirely novel gene expression program. After the switch, global changes include the broad repression of half the genes regulated by complex transcriptional mechanisms. Paralogous transposon clusters, delineate these transcriptional programs. The new transcriptional program is orchestrated by a bottleneck event during which mRNA levels are severely restricted by targeted mRNA degradation.ConclusionsOur results, which attained close to saturating sequencing coverage of the transcriptome, revealed unanticipated transcriptional complexity with almost one third of transcriptional content originating from un-annotated sequences. We showed that the metabolic switch is a sophisticated mechanism of transcriptional regulation capable of resetting and re-synchronizing gene expression programs at extraordinary speed and scale.

Transcriptome sequencing of and microarray development for a Helicoverpa zea cell line to investigate in vitro insect cell-baculovirus interactions

The Heliothine insect complex contains some of the most destructive pests of agricultural crops worldwide, including the closely related Helicoverpa zea and H. armigera. Biological control using baculoviruses is practiced at a moderate level worldwide. In order to enable more wide spread use, a better understanding of cell-virus interactions is required. While many baculoviruses have been sequenced, none of the Heliothine insect genomes have been available. In this study, we sequenced, assembled and functionally annotated 29,586 transcripts from cultured H. zea cells using Illumina 100 bps and paired-end transcriptome sequencing (RNA-seq). The transcript sequences had high assembly coverage (64.5 times). 23,401 sequences had putative protein functions, and over 13,000 sequences had high similarities to available sequences in other insect species. The sequence database was estimated to cover at least 85% of all H. zea genes. The sequences were used to construct a microarray, which was evaluated on the infection of H. zea cells with H. Armigera single-capsid nucleopolyhedrovirus (HearNPV). The analysis revealed that up-regulation of apoptosis genes is the main cellular response in the early infection phase (18 hours post infection), while genes linked to four major immunological signalling pathways (Toll, IMD, Jak-STAT and JNK) were down-regulated. Only small changes (generally downwards) were observed for central carbon metabolism. The transcriptome and microarray platform developed in this study represent a greatly expanded resource base for H. zea insect- HearNPV interaction studies, in which key cellular pathways such as those for metabolism, immune response, transcription and replication have been identified. This resource will be used to develop better cell culture-based virus production processes, and more generally to investigate the molecular basis of host range and susceptibility, virus infectivity and virulence, and the ecology and evolution of baculoviruses.

Evolutionary Engineering Improves Tolerance for Replacement Jet Fuels in Saccharomyces cerevisiae

ABSTRACT Monoterpenes are liquid hydrocarbons with applications ranging from flavor and fragrance to replacement jet fuel. Their toxicity, however, presents a major challenge for microbial synthesis. Here we evolved limonene-tolerant Saccharomyces cerevisiae strains and sequenced six strains across the 200-generation evolutionary time course. Mutations were found in the tricalbin proteins Tcb2p and Tcb3p. Genomic reconstruction in the parent strain showed that truncation of a single protein (tTcb3p1-989), but not its complete deletion, was sufficient to recover the evolved phenotype improving limonene fitness 9-fold. tTcb3p1-989 increased tolerance toward two other monoterpenes (β-pinene and myrcene) 11- and 8-fold, respectively, and tolerance toward the biojet fuel blend AMJ-700t (10% cymene, 50% limonene, 40% farnesene) 4-fold. tTcb3p1-989 is the first example of successful engineering of phase tolerance and creates opportunities for production of the highly toxic C10 alkenes in yeast.

Re-annotation of the Saccharopolyspora erythraea genome using a systems biology approach

BackgroundAccurate bacterial genome annotations provide a framework to understanding cellular functions, behavior and pathogenicity and are essential for metabolic engineering. Annotations based only on in silico predictions are inaccurate, particularly for large, high G + C content genomes due to the lack of similarities in gene length and gene organization to model organisms.ResultsHere we describe a 2D systems biology driven re-annotation of the Saccharopolyspora erythraea genome using proteogenomics, a genome-scale metabolic reconstruction, RNA-sequencing and small-RNA-sequencing. We observed transcription of more than 300 intergenic regions, detected 59 peptides in intergenic regions, confirmed 164 open reading frames previously annotated as hypothetical proteins and reassigned function to open reading frames using the genome-scale metabolic reconstruction. Finally, we present a novel way of mapping ribosomal binding sites across the genome by sequencing small RNAs.ConclusionsThe work presented here describes a novel framework for annotation of the Saccharopolyspora erythraea genome. Based on experimental observations, the 2D annotation framework greatly reduces errors that are commonly made when annotating large-high G + C content genomes using computational prediction algorithms.

Improved production of propionic acid using genome shuffling

Traditionally derived from fossil fuels, biological production of propionic acid has recently gained interest. Propionibacterium species produce propionic acid as their main fermentation product. Production of other organic acids reduces propionic acid yield and productivity, pointing to by-products gene-knockout strategies as a logical solution to increase yield. However, removing by-product formation has seen limited success due to our inability to genetically engineer the best producing strains (i.e. Propionibacterium acidipropionici). To overcome this limitation, random mutagenesis continues to be the best path towards improving strains for biological propionic acid production. Recent advances in next generation sequencing opened new avenues to understand improved strains. In this work, we use genome shuffling on two wild type strains to generate a better propionic acid producing strain. Using next generation sequencing, we mapped the genomic changes leading to the improved phenotype. The best strain produced 25% more propionic acid than the wild type strain. Sequencing of the strains showed that genomic changes were restricted to single point mutations and gene duplications in well-conserved regions in the genomes. Such results confirm the involvement of gene conversion in genome shuffling as opposed to long genomic insertions.

Plant genome-scale modeling and implementation

Considerable progress has been made in plant genome-scale metabolic reconstruction and modeling in recent years. Such reconstructions made it possible to explore metabolic phenotypes through appropriate model formulation and optimization methods. As a result, plant genome-scale modeling has increasingly attracted interest from the plant research community. In this chapter, the first generation of plant genome-scale metabolic reconstructions is presented, along with the important concepts behind model and constraint formulation. A brief protocol describing the use of constraint-based reconstruction and analysis (COBRA) Toolbox in flux simulation and model modification is provided. This is followed by a presentation of metabolic constraints required to generate fluxes in AraGEM using COBRA that describe photosynthesis, photorespiration, and respiration, respectively. Overall, plant genome-scale modeling is a powerful approach that is accessible and readily adopted.