Evaluation of inhibitors of eicosanoid synthesis in leukocytes: possible pitfall of using the calcium ionophore A23187 to stimulate 5' lipoxygenase

The effect on arachidonate metabolism of two compounds (BW755C and benoxaprofen) which have been reported to inhibit 5' lipoxygenase in leukocytes has been evaluated in human polymorphonuclear leukocytes (PMN) stimulated with the calcium ionophore A23187 and serum-treated zymosan (STZ). The syntheses of leukotriene B4 (LTB4) and thromboxane B2 (TXB2) from endogenous substrate were determined by specific radioimmunoassays as indicators of 5' lipoxygenase and cyclo-oxygenase activity in the PMN respectively. Benoxaprofen inhibited the synthesis of leukotriene B4 by human PMN stimulated with the calcium ionophore A23187, but it was approximately 5 times less potent than BW755C. However, benoxaprofen (IC50 1.6 X 10(-4)M) was approximately 100 times less potent than BW755C (IC50 1.7 X 10(-6)M) at inhibiting leukotriene B4 synthesis induced by serum-treated zymosan. Both drugs inhibited thromboxane synthesis by leukocytes stimulated with A23187 or serum-treated zymosan at similar concentrations (approximately 5 X 10(-6)M). The data obtained using STZ as stimulus are consistent with previous in vivo studies and indicate that benoxaprofen is a relatively selective inhibitor of cyclo-oxygenase. However, this selectivity was far less apparent when A23187 was used as a stimulus to release the eicosanoids which suggests that this inhibition could be via an indirect mechanism and therefore A23187 should be used with caution as a stimulus of 5' lipoxygenase for evaluating inhibitors of eicosanoid synthesis.     

Evaluation of inhibitors of eicosanoid synthesis in leukocytes: Possible pitfall of using the calcium ionophore A23187 to stimulate 5′ lipoxygenase

The effect on arachidonate metabolism of two compounds (BW755C and benoxaprofen) which have been reported to inhibit 5′ lipoxygenase in leukocytes has been evaluated in human polymorphonuclear leukocytes (PMN) stimulated with the calcium ionophore A23187 and serum-treated zymosan (STZ). The syntheses of leukotriene B₄ (LTB₄) and thromboxane B₂ (TXB₂) from endogenous substrate were determined by specific radioimmunoassays as indicators of 5′ lipoxygenase and cyclo-oxygenase activity in the PMN respectively. Benoxaprofen inhibited the synthesis of leukotriene B₄ by human PMN stimulated with the calcium ionophore A23187, but it was approximately 5 times less potent than BW755C. However, benoxaprofen (IC₅₀ 1.6 × 10⁻⁴M) was approximately 100 times less potent than BW755C (IC₅₀ 1.7 × 10⁻⁶M) at inhibiting leukotriene B₄ synthesis induced by serum-treated zymosan. Both drugs inhibited thromboxane synthesis by leukocytes stimulated with A23187 or serum-treated zymosan at similar concentrations (approximately 5 × 10⁻⁶M). The data obtained using STZ as stimulus are consistent with previous studies and indicate that benoxaprofen is a relatively selective inhibitor of cylco-oxygenase. However, this selectivity was far less apparent when A23187 was used as a stimulus to release the eicosanoids which suggests that this inhibition could be via an indirect mechanism and therefore A23187 should be used with caution as a stimulus of 5′ lipoxygenase for evaluating inhibitors of eicosanoid synthesis.     

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.

     

External Validation and Calibration of IVFpredict: A National Prospective Cohort Study of 130,960 In Vitro Fertilisation Cycles

Background

Accurately predicting the probability of a live birth after in vitro fertilisation (IVF) is important for patients, healthcare providers and policy makers. Two prediction models (Templeton and IVFpredict) have been previously developed from UK data and are widely used internationally. The more recent of these, IVFpredict, was shown to have greater predictive power in the development dataset. The aim of this study was external validation of the two models and comparison of their predictive ability.

Methods and Findings

130,960 IVF cycles undertaken in the UK in 2008–2010 were used to validate and compare the Templeton and IVFpredict models. Discriminatory power was calculated using the area under the receiver-operator curve and calibration assessed using a calibration plot and Hosmer-Lemeshow statistic. The scaled modified Brier score, with measures of reliability and resolution, were calculated to assess overall accuracy. Both models were compared after updating for current live birth rates to ensure that the average observed and predicted live birth rates were equal. The discriminative power of both methods was comparable: the area under the receiver-operator curve was 0.628 (95% confidence interval (CI): 0.625–0.631) for IVFpredict and 0.616 (95% CI: 0.613–0.620) for the Templeton model. IVFpredict had markedly better calibration and higher diagnostic accuracy, with calibration plot intercept of 0.040 (95% CI: 0.017–0.063) and slope of 0.932 (95% CI: 0.839–1.025) compared with 0.080 (95% CI: 0.044–0.117) and 1.419 (95% CI: 1.149–1.690) for the Templeton model. Both models underestimated the live birth rate, but this was particularly marked in the Templeton model. Updating the models to reflect improvements in live birth rates since the models were developed enhanced their performance, but IVFpredict remained superior.

Conclusion

External validation in a large population cohort confirms IVFpredict has superior discrimination and calibration for informing patients, clinicians and healthcare policy makers of the probability of live birth following IVF.     

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.     

Cell Surface Sialylation and Fucosylation Are Regulated by L1 via Phospholipase Cγ and Cooperate to Modulate Neurite Outgrowth,Cell Survival and Migration

Background

Cell surface glycosylation patterns are markers of cell type and status. However, the mechanisms regulating surface glycosylation patterns remain unknown.

Methodology/Principal Findings

Using a panel of carbohydrate surface markers, we have shown that cell surface sialylation and fucosylation were downregulated in L1^−/y neurons versus L1^+/y neurons. Consistently, mRNA levels of sialyltransferase ST6Gal1, and fucosyltransferase FUT9 were significantly reduced in L1^−/y neurons. Moreover, treatment of L1^+/y neurons with L1 antibodies, triggering signal transduction downstream of L1, led to an increase in cell surface sialylation and fucosylation compared to rat IgG-treated cells. ShRNAs for both ST6Gal1 and FUT9 blocked L1 antibody-mediated enhancement of neurite outgrowth, cell survival and migration. A phospholipase Cγ (PLCγ) inhibitor and shRNA, as well as an Erk inhibitor, reduced ST6Gal1 and FUT9 mRNA levels and inhibited effects of L1 on neurite outgrowth and cell survival.

Conclusions

Neuronal surface sialylation and fucosylation are regulated via PLCγ by L1, modulating neurite outgrowth, cell survival and migration.