Natural strategies for the spatial optimization of metabolism in synthetic biology

Metabolism is a highly interconnected web of chemical reactions that power life. Though the stoichiometry of metabolism is well understood, the multidimensional aspects of metabolic regulation in time and space remain difficult to define, model and engineer. Complex metabolic conversions can be performed by multiple species working cooperatively and exchanging metabolites via structured networks of organisms and resources. Within cells, metabolism is spatially regulated via sequestration in subcellular compartments and through the assembly of multienzyme complexes. Metabolic engineering and synthetic biology have had success in engineering metabolism in the first and second dimensions, designing linear metabolic pathways and channeling metabolic flux. More recently, engineering of the third dimension has improved output of engineered pathways through isolation and organization of multicell and multienzyme complexes. This review highlights natural and synthetic examples of three-dimensional metabolism both inter- and intracellularly, offering tools and perspectives for biological design.     

Biodegradation of phenanthrene by Arthrobacter polychromogenes isolated from a contaminated soil

Summary Degradation of phenanthrene by Arthrobacter polychromogenes isolated from a contaminated soil was investigated. In experiments in which [9-¹⁴C]-phenanthrene was incubated with cultures of A. polychromogenes containing 150 mg phenanthrene/l it was shown that after 26 h of incubation 47.7% of the recovered radiolabelled carbon originally present was metabolized to ¹⁴CO₂, 47.8% was recovered from the aqueous fraction, and 4.5% remained in the dichloromethane fraction. Increasing phenanthrene concentration in the culture medium resulted in improved growth and degradation rates, probably due to the higher amount of phenanthrene crystals in the medium. Shifting the temperature from 30°C to 35°C did not influence phenanthrene degradation significantly but inhibited cell division of A. polychromogenes. Medium supplementation with glucose led to stimulation of phenanthrene degradation at low amounts of glucose (0.45 g/l) whereas at higher concentrations (3 g/l) phenanthrene mineralization decreased.Professor Dr. D. Behrens dedicated to his 65th birthdayOffprint requests to: H.-J. Rehm     

Nitric oxide,NOC-12, and S-nitrosoglutathione modulate the skeletal muscle calcium release channel/ryanodine receptor by different mechanisms. An allosteric function for O2 in S-nitrosylation of the channel

The skeletal muscle Ca(2+) release channel/ryanodine receptor (RyR1) contains approximately 50 thiols per subunit. These thiols have been grouped according to their reactivity/responsiveness toward NO, O(2), and glutathione, but the molecular mechanism enabling redox active molecules to modulate channel activity is poorly understood. In the case of NO, very low concentrations (submicromolar) activate RyR1 by S-nitrosylation of a single cysteine residue (Cys-3635), which resides within a calmodulin binding domain. S-Nitrosylation of Cys-3635 only takes place at physiological tissue O(2) tension (pO(2); i.e. approximately 10 mm Hg) but not at pO(2) approximately 150 mm Hg. Two explanations have been offered for the loss of RyR1 responsiveness to NO at ambient pO(2), i.e. Cys-3635 is oxidized by O(2) versus O(2) subserves an allosteric function (Eu, J. P., Sun, J. H., Xu, L., Stamler, J. S., and Meissner, G. (2000) Cell 102, 499-509). Here we report that the NO donors NOC-12 and S-nitrosoglutathione both activate RyR1 by release of NO but do so independently of pO(2). Moreover, NOC-12 activates the channel by S-nitrosylation of Cys-3635 and thereby reverses channel inhibition by calmodulin. In contrast, S-nitrosoglutathione activates RyR1 by oxidation and S-nitrosylation of thiols other than Cys-3635 (and calmodulin is not involved). Our results suggest that the effect of pO(2) on RyR1 S-nitrosylation is exerted through an allosteric mechanism.     

Cathepsin L expression is directed to secretory vesicles for enkephalin neuropeptide biosynthesis and secretion

Proteases within secretory vesicles are required for conversion of neuropeptide precursors into active peptide neurotransmitters and hormones. This study demonstrates the novel cellular role of the cysteine protease cathepsin L for producing the (Met)enkephalin peptide neurotransmitter from proenkephalin (PE) in the regulated secretory pathway of neuroendocrine PC12 cells. These findings were achieved by coexpression of PE and cathepsin L cDNAs in PC12 cells with analyses of PE-derived peptide products. Expression of cathepsin L resulted in highly increased cellular levels of (Met)enkephalin, resulting from the conversion of PE to enkephalin-containing intermediates of 23, 18-19, 8-9, and 4.5 kDa that were similar to those present in vivo. Furthermore, expression of cathepsin L with PE resulted in increased amounts of nicotine-induced secretion of (Met)enkephalin. These results indicate increased levels of (Met)enkephalin within secretory vesicles of the regulated secretory pathway. Importantly, cathespin L expression was directed to secretory vesicles, demonstrated by colocalization of cathepsin L-DsRed fusion protein with enkephalin and chromogranin A neuropeptides that are present in secretory vesicles. In vivo studies also showed that cathepsin L in vivo was colocalized with enkephalin. The newly defined secretory vesicle function of cathepsin L for biosynthesis of active enkephalin opioid peptide contrasts with its function in lysosomes for protein degradation. These findings demonstrate cathepsin L as a distinct cysteine protease pathway for producing the enkephalin member of neuropeptides.     

Quantifying the effects of switchgrass (Panicum virgatum) on deep organic C stocks using natural abundance 14C in three marginal soils

Perennial bioenergy crops have been shown to increase soil organic carbon (SOC) stocks, potentially offsetting anthropogenic C emissions. The effects of perennial bioenergy crops on SOC are typically assessed at shallow depths (<30 cm), but the deep root systems of these crops may also have substantial effects on SOC stocks at greater depths. We hypothesized that deep (>30 cm) SOC stocks would be greater under bioenergy crops relative to stocks under shallow‐rooted conventional crop cover. To test this, we sampled soils to between 1‐ and 3‐m depth at three sites in Oklahoma with 10‐ to 20‐year‐old switchgrass (Panicum virgatum) stands, and collected paired samples from nearby fields cultivated with shallow rooted annual crops. We measured root biomass, total organic C, ¹⁴C, ¹³C, and other soil properties in three replicate soil cores in each field and used a mixing model to estimate the proportion of recently fixed C under switchgrass based on ¹⁴C. The subsoil C stock under switchgrass (defined over 500–1500 kg/m² equivalent soil mass, approximately 30–100 cm depth) exceeded the subsoil stock in neighboring fields by 1.5 kg C/m² at a sandy loam site, 0.6 kg C/m² at a site with loam soils, and showed no significant difference at a third site with clay soils. Using the mixing model, we estimated that additional SOC introduced after switchgrass cultivation comprised 31% of the subsoil C stock at the sandy loam site, 22% at the loam site, and 0% at the clay site. These results suggest that switchgrass can contribute significantly to subsoil organic C—but also indicated that this effect varies across sites. Our analysis shows that agricultural strategies that emphasize deep‐rooted grass cultivars can increase soil C relative to conventional crops while expanding energy biomass production on marginal lands.     

Microzooplankton grazing of phytoplankton in a tropical upwelling region

We measured grazing by herbivorous zooplankton (<200 μm fraction) in coastal and slope regions of the South Brazil Bight. Using the dilution technique, we performed nine experiments during the austral summer, when nutrient-rich South Atlantic Central Water is present on the shelf, and five during winter. These experiments provide the first estimates of microzooplankton grazing in the western South Atlantic Ocean. Model II regression showed a strong relationship between phytoplankton intrinsic growth rates and grazing, with a slope of 0.64 (±0.28; 95% confidence interval) indicating that microzooplankton grazing could account for the majority of phytoplankton mortality. Both phytoplankton growth and microzooplankton grazing were higher during the summer upwelling season, compared to winter. For the two experiments that were conducted in oligotrophic slope water, grazing accounted for >80% of phytoplankton production. A comparison of incubations with and without added inorganic nutrients showed no consistent stimulation of phytoplankton growth (slope of enriched versus unenriched treatments not significantly different from 1). Estimates from microscopic counts of heterotrophic organisms >10 μm indicated that copepod nauplii comprised the largest share of the microzooplankton biomass (mean 62.4 ± 5.8% SE). Grazing estimates were not correlated with microzooplankton biomass, whether or not nauplii were included, suggesting that most of the grazing was done by nano-sized zooplankton. Electronic Supplementary Material Electronic supplementary material is available in the online version of this article at and is accessible for authorized users. Handling editor: S. Wellekens     

Profile-based string kernels for remote homology detection and motif extraction

We introduce novel profile-based string kernels for use with support vector machines (SVMs) for the problems of protein classification and remote homology detection. These kernels use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along protein sequences for inexact matching of k-length subsequences ("k-mers") in the data. By use of an efficient data structure, the kernels are fast to compute once the profiles have been obtained. For example, the time needed to run PSI-BLAST in order to build the profiles is significantly longer than both the kernel computation time and the SVM training time. We present remote homology detection experiments based on the SCOP database where we show that profile-based string kernels used with SVM classifiers strongly outperform all recently presented supervised SVM methods. We further examine how to incorporate predicted secondary structure information into the profile kernel to obtain a small but significant performance improvement. We also show how we can use the learned SVM classifier to extract "discriminative sequence motifs"--short regions of the original profile that contribute almost all the weight of the SVM classification score--and show that these discriminative motifs correspond to meaningful structural features in the protein data. The use of PSI-BLAST profiles can be seen as a semi-supervised learning technique, since PSI-BLAST leverages unlabeled data from a large sequence database to build more informative profiles. Recently presented "cluster kernels" give general semi-supervised methods for improving SVM protein classification performance. We show that our profile kernel results also outperform cluster kernels while providing much better scalability to large datasets.