EPR Monitored Redox Titration of the Cofactors of Saccharomyces cerevisiae Nar1

Electron Paramagnetic Resonance (EPR) monitored redox titrations are a powerful method to determine the midpoint potential of cofactors in proteins and to identify and quantify the cofactors in their detectable redox state.The technique is complementary to direct electrochemistry (voltammetry) approaches, as it does not offer information on electron transfer rates, but does establish the identity and redox state of the cofactors in the protein under study. The technique is widely applicable to any protein containing an electron paramagnetic resonance (EPR) detectable cofactor.A typical titration requires 2 ml protein with a cofactor concentration in the range of 1-100 µM. The protein is titrated with a chemical reductant (sodium dithionite) or oxidant (potassium ferricyanide) in order to poise the sample at a certain potential. A platinum wire and a Ag/AgCl reference electrode are connected to a voltmeter to measure the potential of the protein solution. A set of 13 different redox mediators is used to equilibrate between the redox cofactors of the protein and the electrodes. Samples are drawn at different potentials and the Electron Paramagnetic Resonance spectra, characteristic for the different redox cofactors in the protein, are measured. The plot of the signal intensity versus the sample potential is analyzed using the Nernst equation in order to determine the midpoint potential of the cofactor.     

New Mechanisms to Explain the Effects of Added Lactose Fines on the Dispersion Performance of Adhesive Mixtures for Inhalation

Fine excipient particles or ‘fines’ have been shown to improve the dispersion performance of carrier-based formulations for dry powder inhalation. Mechanistic formulation studies have focussed mainly on explaining this positive effect. Previous studies have shown that higher drug contents may cause a decrease in dispersion performance, and there is no reason why this should not be true for fines with a similar shape, size and cohesiveness as drug particles. Therefore, the effects on drug detachment of ‘fine lactose fines’ (FLF, X₅₀ = 1.95 µm) with a similar size and shape as micronised budesonide were studied and compared to those of ‘coarse lactose fines’ (CLF, X₅₀ = 3.94 µm). Furthermore, interactions with the inhalation flow rate, the drug content and the mixing order were taken into account. The observed effects of FLF are comparable to drug content effects in that the detached drug fraction was decreased at low drug content and low flow rates but increased at higher flow rates. At high drug content the effects of added FLF were negligible. In contrast, CLF resulted in higher detached drug fractions at all flow rates and drug contents. The results from this study suggest that the effects of fines may be explained by two new mechanisms in addition to those previously proposed. Firstly, fines below a certain size may increase the effectiveness of press-on forces or cause the formation of strongly coherent fine particle networks on the carrier surface containing the drug particles. Secondly, when coarse enough, fines may prevent the formation of, or disrupt such fine particle networks, possibly through a lowering of their tensile strength. It is recommended that future mechanistic studies are based on the recognition that added fines may have any effect on dispersion performance, which is determined by the formulation and dispersion conditions.     

Coupling of M3 Acetylcholine Receptor to Gq16 Activates a Natriuretic Peptide Receptor Guanylyl Cyclase

Muscarinic activation of tracheal smooth muscle (TSM) involves a M₃AChR/heterotrimeric-G protein/NPR-GC coupling mechanism. G protein activators Mastoparan (MAS) and Mastoparan-7 stimulated 4- and 10-fold the NPR-GC respectively, being insensitive to PTX and antibodies against Gα_i/o subfamily. Muscarinic and MAS stimulation of NPR-GC was blocked by antibodies against C-terminal of Gα_q16, whose expression was confirmed by RT-PCR. However, synthetic peptides from C-terminal of Gα_q15/16 stimulated the NPR-GC. Coupling of α_q16 to M₃AChR is supported by MAS decreased [³H]QNB binding, being abolished after M₃AChR-4-DAMP-alkylation. Anti-i₃M₃AChR antibodies blocked the muscarinic activation of NPR-GC, and synthetic peptide from i₃M₃AChR (M₃P) was more potent than MAS increasing GTPγ [³⁵S] and decreasing the [³H]QNB activities. Coupling between NPR-GC and Gα_q16 was evaluated by using trypsin-solubilized-fraction from TSM membranes, which displayed a MAS-sensitive-NPR-GC activity, being immunoprecipitated with anti-Gα_q16, also showing an immunoreactive heterotrimeric-G-β -subunit. These data support the existence of a novel transducing cascade, involving Gα_q16β γ coupling M₃AChR to NPR-GC.     

A holistic high-throughput screening framework for biofuel feedstock assessment that characterises variations in soluble sugars and cell wall composition in <Emphasis Type="Italic">Sorghum bicolor</Emphasis>

Background

A major hindrance to the development of high yielding biofuel feedstocks is the ability to rapidly assess large populations for fermentable sugar yields. Whilst recent advances have outlined methods for the rapid assessment of biomass saccharification efficiency, none take into account the total biomass, or the soluble sugar fraction of the plant. Here we present a holistic high-throughput methodology for assessing sweet Sorghum bicolor feedstocks at 10 days post-anthesis for total fermentable sugar yields including stalk biomass, soluble sugar concentrations, and cell wall saccharification efficiency.

Results

A mathematical method for assessing whole S. bicolor stalks using the fourth internode from the base of the plant proved to be an effective high-throughput strategy for assessing stalk biomass, soluble sugar concentrations, and cell wall composition and allowed calculation of total stalk fermentable sugars. A high-throughput method for measuring soluble sucrose, glucose, and fructose using partial least squares (PLS) modelling of juice Fourier transform infrared (FTIR) spectra was developed. The PLS prediction was shown to be highly accurate with each sugar attaining a coefficient of determination (R ² ) of 0.99 with a root mean squared error of prediction (RMSEP) of 11.93, 5.52, and 3.23 mM for sucrose, glucose, and fructose, respectively, which constitutes an error of <4% in each case. The sugar PLS model correlated well with gas chromatography–mass spectrometry (GC-MS) and brix measures. Similarly, a high-throughput method for predicting enzymatic cell wall digestibility using PLS modelling of FTIR spectra obtained from S. bicolor bagasse was developed. The PLS prediction was shown to be accurate with an R ² of 0.94 and RMSEP of 0.64 μg.mgDW^-1.h^-1.

Conclusions

This methodology has been demonstrated as an efficient and effective way to screen large biofuel feedstock populations for biomass, soluble sugar concentrations, and cell wall digestibility simultaneously allowing a total fermentable yield calculation. It unifies and simplifies previous screening methodologies to produce a holistic assessment of biofuel feedstock potential.

     

The effect of substrate, dihydrobiopterin, and dopamine on the EPR spectroscopic properties and the midpoint potential of the catalytic iron in recombinant human phenylalanine hydroxylase

Phenylalanine hydroxylase (PAH) is a tetrahydrobiopterin (BH(4)) and non-heme iron-dependent enzyme that hydroxylates L-Phe to L-Tyr. The paramagnetic ferric iron at the active site of recombinant human PAH (hPAH) and its midpoint potential at pH 7.25 (E(m)(Fe(III)/Fe(II))) were studied by EPR spectroscopy. Similar EPR spectra were obtained for the tetrameric wild-type (wt-hPAH) and the dimeric truncated hPAH(Gly(103)-Gln(428)) corresponding to the "catalytic domain." A rhombic high spin Fe(III) signal with a g value of 4.3 dominates the EPR spectra at 3.6 K of both enzyme forms. An E(m) = +207 +/- 10 mV was measured for the iron in wt-hPAH, which seems to be adequate for a thermodynamically feasible electron transfer from BH(4) (E(m) (quinonoid-BH(2)/BH(4)) = +174 mV). The broad EPR features from g = 9.7-4.3 in the spectra of the ligand-free enzyme decreased in intensity upon the addition of L-Phe, whereas more axial type signals were observed upon binding of 7,8-dihydrobiopterin (BH(2)), the stable oxidized form of BH(4), and of dopamine. All three ligands induced a decrease in the E(m) value of the iron to +123 +/- 4 mV (L-Phe), +110 +/- 20 mV (BH(2)), and -8 +/- 9 mV (dopamine). On the basis of these data we have calculated that the binding affinities of L-Phe, BH(2), and dopamine decrease by 28-, 47-, and 5040-fold, respectively, for the reduced ferrous form of the enzyme, with respect to the ferric form. Interestingly, an E(m) value comparable with that of the ligand-free, resting form of wt-hPAH, i.e. +191 +/- 11 mV, was measured upon the simultaneous binding of both L-Phe and BH(2), representing an inactive model for the iron environment under turnover conditions. Our findings provide new information on the redox properties of the active site iron relevant for the understanding of the reductive activation of the enzyme and the catalytic mechanism.     

Pyrococcus furiosus ferredoxin is a functional dimer

Pyrococcus furiosus ferredoxin is subject to a monomer/dimer equilibrium as a function of ionic strength. At physiological ionic strength, approximately 0.35 M NaCl, the protein is very predominantly homodimer. The monomeric form exhibits impaired electron transfer on glassy carbon; it also has a decreased S=3/2 over S=1/2 ratio as shown by electron paramagnetic resonance spectroscopy. Even following sterilization at 121 degrees C the dimer is stable in denaturing gel electrophoresis.     

Impact of QTL properties on the accuracy of multi-breed genomic prediction

Background

Although simulation studies show that combining multiple breeds in one reference population increases accuracy of genomic prediction, this is not always confirmed in empirical studies. This discrepancy might be due to the assumptions on quantitative trait loci (QTL) properties applied in simulation studies, including number of QTL, spectrum of QTL allele frequencies across breeds, and distribution of allele substitution effects. We investigated the effects of QTL properties and of including a random across- and within-breed animal effect in a genomic best linear unbiased prediction (GBLUP) model on accuracy of multi-breed genomic prediction using genotypes of Holstein-Friesian and Jersey cows.

Methods

Genotypes of three classes of variants obtained from whole-genome sequence data, with moderately low, very low or extremely low average minor allele frequencies (MAF), were imputed in 3000 Holstein-Friesian and 3000 Jersey cows that had real high-density genotypes. Phenotypes of traits controlled by QTL with different properties were simulated by sampling 100 or 1000 QTL from one class of variants and their allele substitution effects either randomly from a gamma distribution, or computed such that each QTL explained the same variance, i.e. rare alleles had a large effect. Genomic breeding values for 1000 selection candidates per breed were estimated using GBLUP modelsincluding a random across- and a within-breed animal effect.

Results

For all three classes of QTL allele frequency spectra, accuracies of genomic prediction were not affected by the addition of 2000 individuals of the other breed to a reference population of the same breed as the selection candidates. Accuracies of both single- and multi-breed genomic prediction decreased as MAF of QTL decreased, especially when rare alleles had a large effect. Accuracies of genomic prediction were similar for the models with and without a random within-breed animal effect, probably because of insufficient power to separate across- and within-breed animal effects.

Conclusions

Accuracy of both single- and multi-breed genomic prediction depends on the properties of the QTL that underlie the trait. As QTL MAF decreased, accuracy decreased, especially when rare alleles had a large effect. This demonstrates that QTL properties are key parameters that determine the accuracy of genomic prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0124-6) contains supplementary material, which is available to authorized users.     

Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance

The objective of this simulation study was to compare the effect of the number of QTL and distribution of QTL variance on the accuracy of breeding values estimated with genomewide markers (MEBV). Three distinct methods were used to calculate MEBV: a Bayesian Method (BM), Least Angle Regression (LARS) and Partial Least Square Regression (PLSR). The accuracy of MEBV calculated with BM and LARS decreased when the number of simulated QTL increased. The accuracy decreased more when QTL had different variance values than when all QTL had an equal variance. The accuracy of MEBV calculated with PLSR was affected neither by the number of QTL nor by the distribution of QTL variance. Additional simulations and analyses showed that these conclusions were not affected by the number of individuals in the training population, by the number of markers and by the heritability of the trait. Results of this study show that the effect of the number of QTL and distribution of QTL variance on the accuracy of MEBV depends on the method that is used to calculate MEBV.