Time-course transcriptomics reveals that amino acids catabolism plays a key role in toxinogenesis and morphology in Clostridium tetani.

Tetanus is a fatal disease caused by Clostridium tetani infections. To prevent infections, a toxoid vaccine, developed almost a century ago, is routinely used in humans and animals. The vaccine is listed in the World Health Organisation list of Essential Medicines and can be produced and administered very cheaply in the developing world for less than one US Dollar per dose. Recent developments in both analytical tools and frameworks for systems biology provide industry with an opportunity to gain a deeper understanding of the parameters that determine C. tetani virulence and physiological behaviour in bioreactors. Here, we compared a traditional fermentation process with a fermentation medium supplemented with five heavily consumed amino acids. The experiment demonstrated that amino acid catabolism plays a key role in the virulence of C. tetani. The addition of the five amino acids favoured growth, decreased toxin production and changed C. tetani morphology. Using time-course transcriptomics, we created a "fermentation map", which shows that the tetanus toxin transcriptional regulator BotR, P21 and the tetanus toxin gene was downregulated. Moreover, this in-depth analysis revealed potential genes that might be involved in C. tetani virulence regulation. We observed differential expression of genes related to cell separation, surface/cell adhesion, pyrimidine biosynthesis and salvage, flagellar motility, and prophage genes. Overall, the fermentation map shows that, mediated by free amino acid concentrations, virulence in C. tetani is regulated at the transcriptional level and affects a plethora of metabolic functions.

A Pan-Genome Guided Metabolic Network Reconstruction of Five Propionibacterium Species Reveals Extensive Metabolic Diversity.

Propionibacteria have been studied extensively since the early 1930s due to their relevance to industry and importance as human pathogens. Still, their unique metabolism is far from fully understood. This is partly due to their signature high GC content, which has previously hampered the acquisition of quality sequence data, the accurate annotation of the available genomes, and the functional characterization of genes. The recent completion of the genome sequences for several species has led researchers to reassess the taxonomical classification of the genus Propionibacterium, which has been divided into several new genres. Such data also enable a comparative genomic approach to annotation and provide a new opportunity to revisit our understanding of their metabolism. Using pan-genome analysis combined with the reconstruction of the first high-quality Propionibacterium genome-scale metabolic model and a pan-metabolic model of current and former members of the genus Propionibacterium, we demonstrate that despite sharing unique metabolic traits, these organisms have an unexpected diversity in central carbon metabolism and a hidden layer of metabolic complexity. This combined approach gave us new insights into the evolution of Propionibacterium metabolism and led us to propose a novel, putative ferredoxin-linked energy conservation strategy. The pan-genomic approach highlighted key differences in Propionibacterium metabolism that reflect adaptation to their environment. Results were mathematically captured in genome-scale metabolic reconstructions that can be used to further explore metabolism using metabolic modeling techniques. Overall, the data provide a platform to explore Propionibacterium metabolism and a tool for the rational design of strains.

RNA-Seq Highlights High Clonal Variation in Monoclonal Antibody Producing CHO Cells.

The development of next-generation sequencing technologies has opened new opportunities to better characterize complex eukaryotic cells. Chinese hamster ovary (CHO) cells play a primary role in therapeutic protein production, with currently five of the top ten blockbuster drugs produced in CHO. However, engineering superior CHO cells with improved production features has had limited success to date and cell lines are still developed through the generation and screening of large strain pools. Here, we applied RNA sequencing to contrast a high and a low monoclonal antibody producing cell line. Rigorous experimental design achieved high reproducibility between biological replicates, remarkably reducing variation to less than 10%. More than 14 000 gene-transcripts are identified and surprisingly 58% are classified as differentially expressed, including 2900 with a fold change higher than 1.5. The largest differences are found for gene-transcripts belonging to regulation of apoptosis, cell death, and protein intracellular transport GO term classifications, which are found to be significantly enriched in the high producing cell line. RNA sequencing is also performed on subclones, where down-regulation of genes encoding secreted glycoproteins is found to be the most significant change. The large number of significant differences even between subclones challenges the notion of identifying and manipulating a few key genes to generate high production CHO cell lines.

Effect of Plasmid Design and Type of Integration Event on Recombinant Protein Expression in Pichia pastoris.

Pichia pastoris (syn. Komagataella phaffii) is one of the most common eukaryotic expression systems for heterologous protein production. Expression cassettes are typically integrated in the genome to obtain stable expression strains. In contrast to Saccharomyces cerevisiae, where short overhangs are sufficient to target highly specific integration, long overhangs are more efficient in P. pastoris and ectopic integration of foreign DNA can occur. Here, we aimed to elucidate the influence of ectopic integration by high-throughput screening of >700 transformants and whole-genome sequencing of 27 transformants. Different vector designs and linearization approaches were used to mimic the most common integration events targeted in P. pastoris Fluorescence of an enhanced green fluorescent protein (eGFP) reporter protein was highly uniform among transformants when the expression cassettes were correctly integrated in the targeted locus. Surprisingly, most nonspecifically integrated transformants showed highly uniform expression that was comparable to specific integration, suggesting that nonspecific integration does not necessarily influence expression. However, a few clones (<10%) harboring ectopically integrated cassettes showed a greater variation spanning a 25-fold range, surpassing specifically integrated reference strains up to 6-fold. High-expression strains showed a correlation between increased gene copy numbers and high reporter protein fluorescence levels. Our results suggest that for comparing expression levels between strains, the integration locus can be neglected as long as a sufficient numbers of transformed strains are compared. For expression optimization of highly expressible proteins, increasing copy number appears to be the dominant positive influence rather than the integration locus, genomic rearrangements, deletions, or single-nucleotide polymorphisms (SNPs).IMPORTANCE Yeasts are commonly used as biotechnological production hosts for proteins and metabolites. In the yeast Saccharomyces cerevisiae, expression cassettes carrying foreign genes integrate highly specifically at the targeted sites in the genome. In contrast, cassettes often integrate at random genomic positions in nonconventional yeasts, such as Pichia pastoris (syn. Komagataella phaffii). Hence, cells from the same transformation event often behave differently, with significant clonal variation necessitating the screening of large numbers of strains. The importance of this study is that we systematically investigated the influence of integration events in more than 700 strains. Our findings provide novel insight into clonal variation in P. pastoris and, thus, how to avoid pitfalls and obtain reliable results. The underlying mechanisms may also play a role in other yeasts and hence could be generally relevant for recombinant yeast protein production strains.

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.

Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses.

The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated that this systems approach is powerful enough to complement the functional metabolic annotation of bioenergy grasses.

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

BACKGROUND: Our 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. RESULTS: Here, 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.

Systems biology and metabolic modelling unveils limitations to polyhydroxybutyrate accumulation in sugarcane leaves; lessons for C4 engineering.

In planta production of the bioplastic polyhydroxybutyrate (PHB) is one important way in which plant biotechnology can address environmental problems and emerging issues related to peak oil. However, high biomass C4 plants such as maize, switch grass and sugarcane develop adverse phenotypes including stunting, chlorosis and reduced biomass as PHB levels in leaves increase. In this study, we explore limitations to PHB accumulation in sugarcane chloroplasts using a systems biology approach, coupled with a metabolic model of C4 photosynthesis. Decreased assimilation was evident in high PHB-producing sugarcane plants, which also showed a dramatic decrease in sucrose and starch content of leaves. A subtle decrease in the C/N ratio was found which was not associated with a decrease in total protein content. An increase in amino acids used for nitrogen recapture was also observed. Based on the accumulation of substrates of ATP-dependent reactions, we hypothesized ATP starvation in bundle sheath chloroplasts. This was supported by mRNA differential expression patterns. The disruption in ATP supply in bundle sheath cells appears to be linked to the physical presence of the PHB polymer which may disrupt photosynthesis by scattering photosynthetically active radiation and/or physically disrupting thylakoid membranes.

Evolutionary Engineering Improves Tolerance for Replacement Jet Fuels in Saccharomyces cerevisiae.

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 (tTcb3p(1-989)), but not its complete deletion, was sufficient to recover the evolved phenotype improving limonene fitness 9-fold. tTcb3p(1-989) increased tolerance toward two other monoterpenes (ß-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. tTcb3p(1-989) is the first example of successful engineering of phase tolerance and creates opportunities for production of the highly toxic C10 alkenes in yeast.

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.

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

BACKGROUND: Accurate 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. RESULTS: Here 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. CONCLUSIONS: The 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.

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

BACKGROUND: Actinobacteria 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. RESULTS: We 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. CONCLUSIONS: Our 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.

Deep sequencing-based transcriptome analysis of Plutella xylostella larvae parasitized by Diadegma semiclausum.

BACKGROUND: Parasitoid insects manipulate their hosts' physiology by injecting various factors into their host upon parasitization. Transcriptomic approaches provide a powerful approach to study insect host-parasitoid interactions at the molecular level. In order to investigate the effects of parasitization by an ichneumonid wasp (Diadegma semiclausum) on the host (Plutella xylostella), the larval transcriptome profile was analyzed using a short-read deep sequencing method (Illumina). Symbiotic polydnaviruses (PDVs) associated with ichneumonid parasitoids, known as ichnoviruses, play significant roles in host immune suppression and developmental regulation. In the current study, D. semiclausum ichnovirus (DsIV) genes expressed in P. xylostella were identified and their sequences compared with other reported PDVs. Five of these genes encode proteins of unknown identity, that have not previously been reported. RESULTS: De novo assembly of cDNA sequence data generated 172,660 contigs between 100 and 10000 bp in length; with 35% of > 200 bp in length. Parasitization had significant impacts on expression levels of 928 identified insect host transcripts. Gene ontology data illustrated that the majority of the differentially expressed genes are involved in binding, catalytic activity, and metabolic and cellular processes. In addition, the results show that transcription levels of antimicrobial peptides, such as gloverin, cecropin E and lysozyme, were up-regulated after parasitism. Expression of ichnovirus genes were detected in parasitized larvae with 19 unique sequences identified from five PDV gene families including vankyrin, viral innexin, repeat elements, a cysteine-rich motif, and polar residue rich protein. Vankyrin 1 and repeat element 1 genes showed the highest transcription levels among the DsIV genes. CONCLUSION: This study provides detailed information on differential expression of P. xylostella larval genes following parasitization, DsIV genes expressed in the host and also improves our current understanding of this host-parasitoid interaction.

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

BACKGROUND: Microalgae 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. RESULTS: We 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. CONCLUSIONS: AlgaGEM 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.