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.

     

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.     

Unexpected stimulation of soil methane uptake as emergent property of agricultural soils following bio‐based residue application

Intensification of agriculture to meet the global food, feed, and bioenergy demand entail increasing re‐investment of carbon compounds (residues) into agro‐systems to prevent decline of soil quality and fertility. However, agricultural intensification decreases soil methane uptake, reducing, and even causing the loss of the methane sink function. In contrast to wetland agricultural soils (rice paddies), the methanotrophic potential in well‐aerated agricultural soils have received little attention, presumably due to the anticipated low or negligible methane uptake capacity in these soils. Consequently, a detailed study verifying or refuting this assumption is still lacking. Exemplifying a typical agricultural practice, we determined the impact of bio‐based residue application on soil methane flux, and determined the methanotrophic potential, including a qualitative (diagnostic microarray) and quantitative (group‐specific qPCR assays) analysis of the methanotrophic community after residue amendments over 2 months. Unexpectedly, after amendments with specific residues, we detected a significant transient stimulation of methane uptake confirmed by both the methane flux measurements and methane oxidation assay. This stimulation was apparently a result of induced cell‐specific activity, rather than growth of the methanotroph population. Although transient, the heightened methane uptake offsets up to 16% of total gaseous CO₂ emitted during the incubation. The methanotrophic community, predominantly comprised of Methylosinus may facilitate methane oxidation in the agricultural soils. While agricultural soils are generally regarded as a net methane source or a relatively weak methane sink, our results show that methane oxidation rate can be stimulated, leading to higher soil methane uptake. Hence, even if agriculture exerts an adverse impact on soil methane uptake, implementing carefully designed management strategies (e.g. repeated application of specific residues) may compensate for the loss of the methane sink function following land‐use change.     

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.     

Differential effects of nitrogenous fertilizers on methane-consuming microbes in rice field and forest soils

The impact of environmental perturbation (e.g., nitrogenous fertilizers) on the dynamics of methane fluxes from soils and wetland systems is poorly understood. Results of fertilizer studies are often contradictory, even within similar ecosystems. In the present study the hypothesis of whether these contradictory results may be explained by the composition of the methane-consuming microbial community and hence whether methanotrophic diversity affects methane fluxes was investigated. To this end, rice field and forest soils were incubated in microcosms and supplemented with different nitrogenous fertilizers and methane concentrations. By labeling the methane with 13C, diversity and function could be coupled by analyses of phospholipid-derived fatty acids (PLFA) extracted from the soils at different time points during incubation. In both rice field and forest soils, the activity as well as the growth rate of methane-consuming bacteria was affected differentially. For type I methanotrophs, fertilizer application stimulated the consumption of methane and the subsequent growth, while type II methanotrophs were generally inhibited. Terminal restriction fragment length polymorphism analyses of the pmoA gene supported the PLFA results. Multivariate analyses of stable-isotope-probing PLFA profiles indicated that in forest and rice field soils, Methylocystis (type II) species were affected by fertilization. The type I methanotrophs active in forest soils (Methylomicrobium/Methylosarcina related) differed from the active species in rice field soils (Methylobacter/Methylomonas related). Our results provide a case example showing that microbial community structure indeed matters, especially when assessing and predicting the impact of environmental change on biodiversity loss and ecosystem functioning.     

Structural and functional response of methane-consuming microbial communities to different flooding regimes in riparian soils

Climate change will lead to more extreme precipitation and associated increase of flooding events of soils. This can turn these soils from a sink into a source of atmospheric methane. The latter will depend on the balance of microbial methane production and oxidation. In the present study, the structural and functional response of methane oxidizing microbial communities was investigated in a riparian flooding gradient. Four sites differing in flooding frequency were sampled and soil-physico-chemistry as well as methane oxidizing activities, numbers and community composition were assessed. Next to this, the active community members were determined by stable isotope probing of lipids. Methane consumption as well as population size distinctly increased with flooding frequency. All methane consumption parameters (activity, numbers, lipids) correlated with soil moisture, organic matter content, and conductivity. Methane oxidizing bacteria were present and activated quickly even in seldom flooded soils. However, the active species comprised only a few representatives belonging to the genera Methylobacter, Methylosarcina, and Methylocystis, the latter being active only in permanently or regularly flooded soils.This study demonstrates that soils exposed to irregular flooding harbor a very responsive methane oxidizing community that has the potential to mitigate methane produced in these soils. The number of active species is limited and dominated by one methane oxidizing lineage. Knowledge on the characteristics of these microbes is necessary to assess the effects of flooding of soils and subsequent methane cycling therein.     

Biogeography of sulfate-reducing prokaryotes in river floodplains

In this study, a large-scale field survey was conducted to describe the biogeography of sulfate-reducing prokaryotes (SRPs) in river floodplains. Fingerprints obtained with three methods, i.e. 16S rRNA gene-based oligonucleotide microarray, dsrB-based denaturing gradient gel electrophoresis (DGGE) and polar lipid-derived fatty acid (PLFA) analyses, were used as a proxy to describe the SRPs community diversity. Each set of profiles was subjected to a combined multivariate/correlation analysis in order to compare SRP community profiles and to highlight the environmental variables influencing the SRPs distribution along environmental gradients. Floodplain soils harbored distinct SRP communities displaying biogeographic patterns. Nearly all profiles from the tidal sites consistently separated from the nontidal sites, independently from the screening method and the multivariate statistics used. The distribution of the microarray/DGGE/PLFA-based fingerprints in the principal component plots could be correlated to eight soil variables, i.e. soil organic matter, total nitrogen, total phosphorous and total potassium, and extractable ammonium, nitrate, phosphate and sulfate, as well as seven pore water variables, i.e. phosphate, sulfate, sulfide, chloride, sodium, potassium and magnesium ions. Indication of a salinity- and plant nutrient-dependent distribution of SRPs related to Desulfosarcina, Desulfomonile and Desulfobacter was suggested by microarray, DGGE and PLFA analyses.     

The impact of climate change on lakes in the Netherlands: a review

Climate change will alter freshwater ecosystems but specific effects will vary among regions and the type of water body. Here, we give an integrative review of the observed and predicted impacts of climate change on shallow lakes in the Netherlands and put these impacts in an international perspective. Most of these lakes are man-made and have preset water levels and poorly developed littoral zones. Relevant climatic factors for these ecosystems are temperature, ice-cover and wind. Secondary factors affected by climate include nutrient loading, residence time and water levels. We reviewed the relevant literature in order to assess the impact of climate change on these lakes. We focussed on six management objectives as bioindicators for the functioning of these ecosystems: target species, nuisance species, invading species, transparency, carrying capacity and biodiversity. We conclude that climate change will likely (i) reduce the numbers of several target species of birds; (ii) favour and stabilize cyanobacterial dominance in phytoplankton communities; (iii) cause more serious incidents of botulism among waterfowl and enhance the spreading of mosquito borne diseases; (iv) benefit invaders originating from the Ponto-Caspian region; (v) stabilize turbid, phytoplankton-dominated systems, thus counteracting restoration measures; (vi) destabilize macrophyte-dominated clear-water lakes; (vii) increase the carrying capacity of primary producers, especially phytoplankton, thus mimicking eutrophication; (viii) affect higher trophic levels as a result of enhanced primary production; (ix) have a negative impact on biodiversity which is linked to the clear water state; (x) affect biodiversity by changing the disturbance regime. Water managers can counteract these developments by reduction of nutrient loading, development of the littoral zone, compartmentalization of lakes and fisheries management.