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.

     

Termite mounds differ in their importance for herbivores across savanna types,seasons and spatial scales

Herbivores do not forage uniformly across landscapes, but select for patches of higher nutrition and lower predation risk. Macrotermes mounds contain higher concentrations of soil nutrients and support grasses of higher nutritional value than the surrounding savanna matrix, attracting mammalian grazers that preferentially forage on termite mound vegetation. However, little is known about the spatial extent of such termite influence on grazing patterns and how it might differ in time and space. We measured grazing intensity in three African savanna types differing in rainfall and foliar nutrients and predicted that the functional importance of mounds for grazing herbivores would increase as the difference in foliar nutrient levels between mound and savanna matrix grasses increases and the mounds become more attractive. We expected this to occur in nutrient‐poor areas and during the dry season when savanna matrix grass nutrient levels are lower. Tuft use and grass N and P content were measured along transects away from termite mounds, enabling calculation of the spatial extent of termite influence on mammalian grazing. Using termite mound densities estimated from airborne light detection and ranging (LiDAR), we further upscaled field‐based results to determine the percentage of the landscape influenced by termite activity. Grasses in close proximity to termite mounds were preferentially grazed at all sites and in both seasons, but the strength of mound influence varied between savanna types and seasons. In the wet season, mounds had a relatively larger effect on grazers at the landscape scale in the nutrient‐poor, wetter savanna, whereas in the dry season the pattern was reversed with more of the landscape influenced at the nutrient‐rich, driest site. Our results reveal that termite mounds enhance the value of savanna landscapes for herbivores, but that their functional importance varies across savanna types and seasons.     

The Perfect Burrow,but for What? Identifying Local Habitat Conditions Promoting the Presence of the Host and Vector Species in the Kazakh Plague System

Introduction

The wildlife plague system in the Pre-Balkhash desert of Kazakhstan has been a subject of study for many years. Much progress has been made in generating a method of predicting outbreaks of the disease (infection by the gram negative bacterium Yersinia pestis) but existing methods are not yet accurate enough to inform public health planning. The present study aimed to identify characteristics of individual mammalian host (Rhombomys opimus) burrows related to and potentially predictive of the presence of R.opimus and the dominant flea vectors (Xenopsylla spp.).

Methods

Over four seasons, burrow characteristics, their current occupancy status, and flea and tick burden of the occupants were recorded in the field. A second data set was generated of long term occupancy trends by recording the occupancy status of specific burrows over multiple occasions. Generalised linear mixed models were constructed to identify potential burrow properties predictive of either occupancy or flea burden.

Results

At the burrow level, it was identified that a burrow being occupied by Rhombomys, and remaining occupied, were both related to the characteristics of the sediment in which the burrow was constructed. The flea burden of Rhombomys in a burrow was found to be related to the tick burden. Further larger scale properties were also identified as being related to both Rhombomys and flea presence, including latitudinal position and the season.

Conclusions

Therefore, in advancing our current predictions of plague in Kazakhstan, we must consider the landscape at this local level to increase our accuracy in predicting the dynamics of gerbil and flea populations. Furthermore this demonstrates that in other zoonotic systems, it may be useful to consider the distribution and location of suitable habitat for both host and vector species at this fine scale to accurately predict future epizootics.     

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.     

Visfatin is regulated by rosiglitazone in type 2 diabetes mellitus and influenced by NFκB and JNK in human abdominal subcutaneous adipocytes

Visfatin has been proposed as an insulin-mimicking adipocytokine, predominantly secreted from adipose tissue and correlated with obesity. However, recent studies suggest visfatin may act as a proinflammatory cytokine. Our studies sought to determine the significance of this adipocytokine and its potential role in the pathogenesis of T2DM. Firstly, we examined the effects of diabetic status on circulating visfatin levels, and several other adipocytokines, demonstrating that diabetic status increased visfatin*, TNF-α*** and IL-6*** compared with non-diabetic subjects (*p<0.05, **p<0.01, ***p<0.001, respectively). We then assessed the effects of an insulin sensitizer, rosiglitazone (RSG), in treatment naïve T2DM subjects, on circulating visfatin levels. Our findings showed that visfatin was reduced post-RSG treatment [vs. pre-treatment (*p<0.05)] accompanied by a reduction in HOMA-IR**, thus implicating a role for insulin in visfatin regulation. Further studies addressed the intracellular mechanisms by which visfatin may be regulated, and may exert pro-inflammatory effects, in human abdominal subcutaneous (Abd Sc) adipocytes. Following insulin (Ins) and RSG treatment, our in vitro findings highlighted that insulin (100 nM), alone, upregulated visfatin protein expression whereas, in combination with RSG (10 nM), it reduced visfatin*, IKKβ** and p-JNK1/2*. Furthermore, inhibition of JNK protein exacted a significant reduction in visfatin expression (**p<0.01), whilst NF-κB blockade increased visfatin (*p<0.05), thus identifying JNK as the more influential factor in visfatin regulation. Additional in vitro analysis on adipokines regulating visfatin showed that only Abd Sc adipocytes treated with recombinant human (rh)IL-6 increased visfatin protein (*p<0.05), whilst rh visfatin treatment, itself, had no influence on TNF-α, IL-6 or resistin secretion from Sc adipocytes. These data highlight visfatin''s regulation by insulin and RSG, potentially acting through NF-κB and JNK mechanisms, with only rh IL-6 modestly affecting visfatin regulation. Taken together, these findings suggest that visfatin may represent a pro-inflammatory cytokine that is influenced by insulin/insulin sensitivity via the NF-κB and JNK pathways.     

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.