Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli

The identification of genetic targets that are effective in bringing about a desired phenotype change is still an open problem. While random gene knockouts have yielded improved strains in certain cases, it is also important to seek the guidance of cell-wide stoichiometric constraints in identifying promising gene knockout targets. To investigate these issues, we undertook a genome-wide stoichiometric flux balance analysis as an aid in discovering putative genes impacting network properties and cellular phenotype. Specifically, we calculated metabolic fluxes such as to optimize growth and then scanned the genome for single and multiple gene knockouts that yield improved product yield while maintaining acceptable overall growth rate. For the particular case of lycopene biosynthesis in Escherichia coli, we identified such targets that we subsequently tested experimentally by constructing the corresponding single, double and triple gene knockouts. While such strains are suggested (by the stoichiometric calculations) to increase precursor availability, this beneficial effect may be further impacted by kinetic and regulatory effects not captured by the stoichiometric model. For the case of lycopene biosynthesis, the so identified knockout targets yielded a triple knockout construct that exhibited a nearly 40% increase over an engineered, high producing parental strain.     

Regeneration Capacity of Calli Derived from Immature Embryos in Spring Barley Cultivars

Callogenesis, somatic embryogenesis and regeneration capacity in twenty-three agronomically important spring barley (Hordeum vulgare L.) cultivars on induction media with 2,4-dichlorophenoxyacetic acid (2,4-D) or 3,6-dichloro-o-anisic acid (dicamba) and on modified regeneration media were studied. The frequency of zygotic embryos exhibiting callogenesis varied from 88 to 100 % according to genotype. Dicamba was more suitable for somatic embryogenesis induction and exhibited a higher frequency of regenerants than did 2,4-D. Green regenerants were obtained in all cultivars, and there were no albino plants. Except for cv. Victor all cultivars used in the experiment showed lower regeneration capacity as compared to the model cv. Golden Promise. This revised version was published online in July 2006 with corrections to the Cover Date.     

Identification of follicle-stimulating hormone-selective beta-strands in the N-terminal hormone-binding exodomain of human gonadotropin receptors

Glycoprotein hormone receptors contain large N-terminal extracellular domains (ECDs) that distinguish these receptors from most other G protein-coupled receptors. Each glycoprotein hormone receptor ECD consists of a curved leucine-rich repeat domain flanked by N- and C-terminal cysteine-rich regions. Selectivity of the different glycoprotein hormone receptors for their cognate hormones is exclusively determined by their ECDs and, in particular, their leucine-rich repeat domain. To identify human (h)FSH-selective determinants we used a gain-of-function mutagenesis strategy in which beta-strands of the hLH receptor (hLH-R) were substituted with their hFSH receptor (hFSH-R) counterparts. Introduction of hFSH-R beta-strand 1 into hLH-R conferred responsiveness to hFSH, whereas hLH-R mutants harboring one of the other hFSH-R beta-strands displayed none or very limited sensitivity to hFSH. However, combined substitution of hFSH-R beta-strand 1 and some of the other hFSH-R beta-strands further increased the sensitivity of the mutant hLH-R to hFSH. The apparent contribution of multiple hFSH-R beta-strands in providing a selective hormone binding interface corresponds well with their position in relation to hFSH as recently determined in the crystal structure of hFSH in complex with part of the hFSH-R ECD.     

Spatio-temporal variation in Markov chain models of subtidal community succession

In this paper we ask whether succession in a rocky subtidal community varies in space and time, and if so how much affect that variation has on predictions of community dynamics and structure. We describe succession by Markov chain models based on observed frequencies of species replacements. We use loglinear analysis to detect and quantify spatio‐temporal variation in the transition matrices describing succession. The analysis shows that space and time, but not their interaction, have highly significant effects on transition probabilities. To explore the ecological importance of the spatio‐temporal variability detected in this analysis, we compare the equilibria and the transient dynamics among three Markov chain models: a time‐averaged model that includes the effects of space on succession, a spatially averaged model that include the effects of time, and a constant matrix that averages over the effects of space and time. All three models predicted similar equilibrium composition and similar rates of convergence to equilibrium, as measured by the damping ratio or the subdominant Lyapunov exponent. The predicted equilibria from all three models were very similar to the observed community structure. Thus, although spatial and temporal variation is statistically significant, at least in this system this variation does not prevent homogeneous models from predicting community structure.