Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

<p>Abstract</p> <p>Background</p> <p>Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. <it>Clostridium thermocellum </it>(ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous.</p> <p>Results</p> <p>Here we present a genome-scale model of <it>C. thermocellum </it>metabolism, <it>i</it>SR432, for the purpose of establishing a computational tool to study the metabolic network of <it>C. thermocellum </it>and facilitate efforts to engineer <it>C. thermocellum </it>for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the <it>i</it>SR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production.</p> <p>Conclusions</p> <p>By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of <it>C. thermocellum </it>and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of <it>C. thermocellum </it>and highlight remaining gaps in the existing genome annotations.</p

Genome-scale resources for Thermoanaerobacterium saccharolyticum

Background Thermoanaerobacterium saccharolyticum is a hemicellulose-degrading thermophilic anaerobe that was previously engineered to produce ethanol at high yield. A major project was undertaken to develop this organism into an industrial biocatalyst, but the lack of genome information and resources were recognized early on as a key limitation. Results Here we present a set of genome-scale resources to enable the systems level investigation and development of this potentially important industrial organism. Resources include a complete genome sequence for strain JW/SL-YS485, a genome-scale reconstruction of metabolism, tiled microarray data showing transcription units, mRNA expression data from 71 different growth conditions or timepoints and GC/MS-based metabolite analysis data from 42 different conditions or timepoints. Growth conditions include hemicellulose hydrolysate, the inhibitors HMF, furfural, diamide, and ethanol, as well as high levels of cellulose, xylose, cellobiose or maltodextrin. The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid. An active prophage was also detected, and the expression levels of CRISPR genes were observed to increase in association with those of the phage. Hemicellulose hydrolysate elicited a response of carbohydrate transport and catabolism genes, as well as poorly characterized genes suggesting a redox challenge. In some conditions, a time series of combined transcription and metabolite measurements were made to allow careful study of microbial physiology under process conditions. As a demonstration of the potential utility of the metabolic reconstruction, the OptKnock algorithm was used to predict a set of gene knockouts that maximize growth-coupled ethanol production. The predictions validated intuitive strain designs and matched previous experimental results. Conclusion These data will be a useful asset for efforts to develop T. saccharolyticum for efficient industrial production of biofuels. The resources presented herein may also be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0159-x) contains supplementary material, which is available to authorized users

The Relationship between Phytoplankton Distribution and Water Column Characteristics in North West European Shelf Sea Waters

Phytoplankton underpin the marine food web in shelf seas, with some species having properties that are harmful to human health and coastal aquaculture. Pressures such as climate change and anthropogenic nutrient input are hypothesized to influence phytoplankton community composition and distribution. Yet the primary environmental drivers in shelf seas are poorly understood. To begin to address this in North Western European waters, the phytoplankton community composition was assessed in light of measured physical and chemical drivers during the “Ellett Line” cruise of autumn 2001 across the Scottish Continental shelf and into adjacent open Atlantic waters. Spatial variability existed in both phytoplankton and environmental conditions, with clear differences not only between on and off shelf stations but also between different on shelf locations. Temperature/salinity plots demonstrated different water masses existed in the region. In turn, principal component analysis (PCA), of the measured environmental conditions (temperature, salinity, water density and inorganic nutrient concentrations) clearly discriminated between shelf and oceanic stations on the basis of DIN∶DSi ratio that was correlated with both salinity and temperature. Discrimination between shelf stations was also related to this ratio, but also the concentration of DIN and DSi. The phytoplankton community was diatom dominated, with multidimensional scaling (MDS) demonstrating spatial variability in its composition. Redundancy analysis (RDA) was used to investigate the link between environment and the phytoplankton community. This demonstrated a significant relationship between community composition and water mass as indexed by salinity (whole community), and both salinity and DIN∶DSi (diatoms alone). Diatoms of the Pseudo-nitzschia seriata group occurred at densities potentially harmful to shellfish aquaculture, with the potential for toxicity being elevated by the likelihood of DSi limitation of growth at most stations and depths

High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity

Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (Vc,max), maximum electron transport rate (Jmax) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted Vc,max, Jmax and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for Vc,max, but not for Jmax, and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R2 increases of 17% for Vc,max. and 13% Jmax. Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower Vc,max. The PLSR model was able to accurately predict both lower Vc,max and higher leaf [N] for this genotype suggesting that the spectral based estimates of Vc,max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required

Metabolic engineering of a tyrosine-overproducing yeast platform using targeted metabolomics

Abstract Background L-tyrosine is a common precursor for a wide range of valuable secondary metabolites, including benzylisoquinoline alkaloids (BIAs) and many polyketides. An industrially tractable yeast strain optimized for production of L-tyrosine could serve as a platform for the development of BIA and polyketide cell factories. This study applied a targeted metabolomics approach to evaluate metabolic engineering strategies to increase the availability of intracellular L-tyrosine in the yeast Saccharomyces cerevisiae CEN.PK. Our engineering strategies combined localized pathway engineering with global engineering of central metabolism, facilitated by genome-scale steady-state modelling. Results Addition of a tyrosine feedback resistant version of 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase Aro4 from S. cerevisiae was combined with overexpression of either a tyrosine feedback resistant yeast chorismate mutase Aro7, the native pentafunctional arom protein Aro1, native prephenate dehydrogenase Tyr1 or cyclohexadienyl dehydrogenase TyrC from Zymomonas mobilis. Loss of aromatic carbon was limited by eliminating phenylpyruvate decarboxylase Aro10. The TAL gene from Rhodobacter sphaeroides was used to produce coumarate as a simple test case of a heterologous by-product of tyrosine. Additionally, multiple strategies for engineering global metabolism to promote tyrosine production were evaluated using metabolic modelling. The T21E mutant of pyruvate kinase Cdc19 was hypothesized to slow the conversion of phosphoenolpyruvate to pyruvate and accumulate the former as precursor to the shikimate pathway. The ZWF1 gene coding for glucose-6-phosphate dehydrogenase was deleted to create an NADPH deficiency designed to force the cell to couple its growth to tyrosine production via overexpressed NADP+-dependent prephenate dehydrogenase Tyr1. Our engineered Zwf1− strain expressing TYRC ARO4 FBR and grown in the presence of methionine achieved an intracellular L-tyrosine accumulation up to 520 μmol/g DCW or 192 mM in the cytosol, but sustained flux through this pathway was found to depend on the complete elimination of feedback inhibition and degradation pathways. Conclusions Our targeted metabolomics approach confirmed a likely regulatory site at DAHP synthase and identified another possible cofactor limitation at prephenate dehydrogenase. Additionally, the genome-scale metabolic model identified design strategies that have the potential to improve availability of erythrose 4-phosphate for DAHP synthase and cofactor availability for prephenate dehydrogenase. We evaluated these strategies and provide recommendations for further improvement of aromatic amino acid biosynthesis in S. cerevisiae

Genome-scale resources for Thermoanaerobacterium saccharolyticum

Abstract Background Thermoanaerobacterium saccharolyticum is a hemicellulose-degrading thermophilic anaerobe that was previously engineered to produce ethanol at high yield. A major project was undertaken to develop this organism into an industrial biocatalyst, but the lack of genome information and resources were recognized early on as a key limitation. Results Here we present a set of genome-scale resources to enable the systems level investigation and development of this potentially important industrial organism. Resources include a complete genome sequence for strain JW/SL-YS485, a genome-scale reconstruction of metabolism, tiled microarray data showing transcription units, mRNA expression data from 71 different growth conditions or timepoints and GC/MS-based metabolite analysis data from 42 different conditions or timepoints. Growth conditions include hemicellulose hydrolysate, the inhibitors HMF, furfural, diamide, and ethanol, as well as high levels of cellulose, xylose, cellobiose or maltodextrin. The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid. An active prophage was also detected, and the expression levels of CRISPR genes were observed to increase in association with those of the phage. Hemicellulose hydrolysate elicited a response of carbohydrate transport and catabolism genes, as well as poorly characterized genes suggesting a redox challenge. In some conditions, a time series of combined transcription and metabolite measurements were made to allow careful study of microbial physiology under process conditions. As a demonstration of the potential utility of the metabolic reconstruction, the OptKnock algorithm was used to predict a set of gene knockouts that maximize growth-coupled ethanol production. The predictions validated intuitive strain designs and matched previous experimental results. Conclusion These data will be a useful asset for efforts to develop T. saccharolyticum for efficient industrial production of biofuels. The resources presented herein may also be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum

Drawing from memory: hand-eye coordination at multiple scales.

Eyes move to gather visual information for the purpose of guiding behavior. This guidance takes the form of perceptual-motor interactions on short timescales for behaviors like locomotion and hand-eye coordination. More complex behaviors require perceptual-motor interactions on longer timescales mediated by memory, such as navigation, or designing and building artifacts. In the present study, the task of sketching images of natural scenes from memory was used to examine and compare perceptual-motor interactions on shorter and longer timescales. Eye and pen trajectories were found to be coordinated in time on shorter timescales during drawing, and also on longer timescales spanning study and drawing periods. The latter type of coordination was found by developing a purely spatial analysis that yielded measures of similarity between images, eye trajectories, and pen trajectories. These results challenge the notion that coordination only unfolds on short timescales. Rather, the task of drawing from memory evokes perceptual-motor encodings of visual images that preserve coarse-grained spatial information over relatively long timescales as well