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.     

Developmental structuring of phenotypic variation: A case study with a cellular automata model of ontogeny

Developmental mechanisms not only produce an organismal phenotype, but they also structure the way genetic variation maps to phenotypic variation. Here, we revisit a computational model for the evolution of ontogeny based on cellular automata, in which evolution regularly discovered two alternative mechanisms for achieving a selected phenotype, one showing high modularity, the other showing morphological integration. We measure a primary variational property of the systems, their distribution of fitness effects of mutation. We find that the modular ontogeny shows the evolution of mutational robustness and ontogenic simplification, while the integrated ontogeny does not. We discuss the wider use of this methodology on other computational models of development as well as real organisms.     

The presence of alpha-catenin in the VE-cadherin complex is required for efficient transendothelial migration of leukocytes

The majority of the leukocytes cross the endothelial lining of the vessels through cell-cell junctions. The junctional protein Vascular Endothelial (VE)-cadherin is transiently re-distributed from sites of cell-cell contacts during passage of leukocytes. VE-cadherin is part of a protein complex comprising p120-catenin and beta-catenin as intracellular partners. Beta-catenin connects VE-cadherin to alpha-catenin. This VE-cadherin-catenin complex is believed to dynamically control endothelial cell-cell junctions and to regulate the passage of leukocytes, although not much is known about the role of alpha- and beta-catenin during the process of transendothelial migration (TEM). In order to study the importance of the interaction between alpha- and beta-catenin in TEM, we used a cell-permeable version of the peptide encoding the binding site of alpha-catenin for beta-catenin (S27D). The data show that S27D interferes with the interaction between alpha- and beta-catenin and induces a reversible decrease in electrical resistance of the endothelial monolayer. In addition, S27D co-localized with beta-catenin at cell-cell junctions. Surprisingly, transmigration of neutrophils across endothelial monolayers was blocked in the presence of S27D. In conclusion, our results show for the first time that the association of alpha-catenin with the cadherin-catenin complex is required for efficient leukocyte TEM.     

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.

     

An ecogenomic analysis of herbivore‐induced plant volatiles in Brassica juncea

Upon herbivore feeding, plants emit complex bouquets of induced volatiles that may repel insect herbivores as well as attract parasitoids or predators. Due to differences in the temporal dynamics of individual components, the composition of the herbivore‐induced plant volatile (HIPV) blend changes with time. Consequently, the response of insects associated with plants is not constant either. Using Brassica juncea as the model plant and generalist Spodoptera spp. larvae as the inducing herbivore, we investigated herbivore and parasitoid preference as well as the molecular mechanisms behind the temporal dynamics in HIPV emissions at 24, 48 and 72 h after damage. In choice tests, Spodoptera litura moth preferred undamaged plants, whereas its parasitoid Cotesia marginiventris favoured plants induced for 48 h. In contrast, the specialist Plutella xylostella and its parasitoid C. vestalis preferred plants induced for 72 h. These preferences matched the dynamic changes in HIPV blends over time. Gene expression analysis suggested that the induced response after Spodoptera feeding is mainly controlled by the jasmonic acid pathway in both damaged and systemic leaves. Several genes involved in sulphide and green leaf volatile synthesis were clearly up‐regulated. This study thus shows that HIPV blends vary considerably over a short period of time, and these changes are actively regulated at the gene expression level. Moreover, temporal changes in HIPVs elicit differential preferences of herbivores and their natural enemies. We argue that the temporal dynamics of HIPVs may play a key role in shaping the response of insects associated with plants.     

Nucleophosmin1 Is a Negative Regulator of the Small GTPase Rac1

The Rac1 GTPase is a critical regulator of cytoskeletal dynamics and controls many biological processes, such as cell migration, cell-cell contacts, cellular growth and cell division. These complex processes are controlled by Rac1 signaling through effector proteins. We have previously identified several effector proteins of Rac1 that also act as Rac1 regulatory proteins, including caveolin-1 and PACSIN2. Here, we report that Rac1 interacts through its C-terminus with nucleophosmin1 (NPM1), a multifunctional nucleo-cytoplasmic shuttling protein with oncogenic properties. We show that Rac1 controls NPM1 subcellular localization. In cells expressing active Rac1, NPM1 translocates from the nucleus to the cytoplasm. In addition, Rac1 regulates the localization of the phosphorylated pool of NPM1 as this pool translocated from the nucleus to the cytosol in cells expressing activated Rac1. Conversely, we found that expression of NPM1 limits Rac1 GTP loading and cell spreading. In conclusion, this study identifies NPM1 as a novel, negative regulator of Rac1.     

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.     

Production of Diamino propionic acid ammonia lyase by a new strain of Salmonella typhimurium PU011

Background  

Seeds of the legume plant Lathyrus sativus, which is grown in arid and semi arid tropical regions, contain Diamino Propionic acid (DAP). DAP is a neurotoxin, which, when consumed, causes a disease called Lathyrism. Lathryrism may manifest as Neurolathyrism or Osteolathyrism, in which the nervous system, and bone formation respectively, are affected. DAP ammonia lyase is produced by a few microorganisms such as Salmonella typhi, Salmonella typhimurium and Pseudomonas, and is capable of detoxifying DAP.     

Autocatalytic Sets and Biological Specificity

A universal feature of the biochemistry of any living system is that all the molecules and catalysts that are required for reactions of the system can be built up from an available food source by repeated application of reactions from within that system. RAF (reflexively autocatalytic and food-generated) theory provides a formal way to study such processes. Beginning with Kauffman’s notion of “collectively autocatalytic sets,” this theory has been further developed over the last decade with the discovery of efficient algorithms and new mathematical analysis. In this paper, we study how the behaviour of a simple binary polymer model can be extended to models where the pattern of catalysis more precisely reflects the ligation and cleavage reactions involved. We find that certain properties of these models are similar to, and can be accurately predicted from, the simple binary polymer model; however, other properties lead to slightly different estimates. We also establish a number of new results concerning the structure of RAFs in these systems.     

Evolution of Autocatalytic Sets in Computational Models of Chemical Reaction Networks

Several computational models of chemical reaction networks have been presented in the literature in the past, showing the appearance and (potential) evolution of autocatalytic sets. However, the notion of autocatalytic sets has been defined differently in different modeling contexts, each one having some shortcoming or limitation. Here, we review four such models and definitions, and then formally describe and analyze them in the context of a mathematical framework for studying autocatalytic sets known as RAF theory. The main results are that: (1) RAF theory can capture the various previous definitions of autocatalytic sets and is therefore more complete and general, (2) the formal framework can be used to efficiently detect and analyze autocatalytic sets in all of these different computational models, (3) autocatalytic (RAF) sets are indeed likely to appear and evolve in such models, and (4) this could have important implications for a possible metabolism-first scenario for the origin of life.