RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors.

Cell surface receptors play essential roles in perceiving and processing external and internal signals at the cell surface of plants and animals. The receptor-like protein kinases (RLK) and receptor-like proteins (RLPs), two major classes of proteins with membrane receptor configuration, play a crucial role in plant development and disease defense. Although RLPs and RLKs share a similar single-pass transmembrane configuration, RLPs harbor short divergent C-terminal regions instead of the conserved kinase domain of RLKs. This RLP receptor structural design precludes sequence comparison algorithms from being used for high-throughput predictions of the RLP family in plant genomes, as has been extensively performed for RLK superfamily predictions. Here, we developed the RLPredictiOme, implemented with machine learning models in combination with Bayesian inference, capable of predicting RLP subfamilies in plant genomes. The ML models were simultaneously trained using six types of features, along with three stages to distinguish RLPs from non-RLPs (NRLPs), RLPs from RLKs, and classify new subfamilies of RLPs in plants. The ML models achieved high accuracy, precision, sensitivity, and specificity for predicting RLPs with relatively high probability ranging from 0.79 to 0.99. The prediction of the method was assessed with three datasets, two of which contained leucine-rich repeats (LRR)-RLPs from Arabidopsis and rice, and the last one consisted of the complete set of previously described Arabidopsis RLPs. In these validation tests, more than 90% of known RLPs were correctly predicted via RLPredictiOme. In addition to predicting previously characterized RLPs, RLPredictiOme uncovered new RLP subfamilies in the Arabidopsis genome. These include probable lipid transfer (PLT)-RLP, plastocyanin-like-RLP, ring finger-RLP, glycosyl-hydrolase-RLP, and glycerophosphoryldiester phosphodiesterase (GDPD, GDPDL)-RLP subfamilies, yet to be characterized. Compared to the only Arabidopsis GDPDL-RLK, molecular evolution studies confirmed that the ectodomain of GDPDL-RLPs might have undergone a purifying selection with a predominance of synonymous substitutions. Expression analyses revealed that predicted GDPGL-RLPs display a basal expression level and respond to developmental and biotic signals. The results of these biological assays indicate that these subfamily members have maintained functional domains during evolution and may play relevant roles in development and plant defense. Therefore, RLPredictiOme provides a framework for genome-wide surveys of the RLP superfamily as a foundation to rationalize functional studies of surface receptors and their relationships with different biological processes.

Enviromics in breeding: applications and perspectives on envirotypic-assisted selection.

KEY MESSAGE: We propose the application of enviromics to breeding practice, by which the similarity among sites assessed on an "omics" scale of environmental attributes drives the prediction of unobserved genotype performances. Genotype by environment interaction (GEI) studies in plant breeding have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent geographic information system (GIS) techniques have opened new frontiers for better understanding and dealing with GEI. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term enviromics, within an envirotypic-assisted breeding framework. In summary, likewise genotypes at DNA markers, any particular site is characterized by a set of "envirotypes" at multiple "enviromic" markers corresponding to environmental variables that may interact with the genetic background, thus providing informative breeding re-rankings for optimized decisions over different environments. Based on simulated data, we illustrate an index-based enviromics method (the "GIS-GEI") which, due to its higher granular resolution than standard methods, allows for: (1) accurate matching of sites to their most appropriate genotypes; (2) better definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (3) efficient determination of the best sites to carry out experiments for further analyses. Environmental scenarios can also be optimized for productivity improvement and genetic resources management, especially in the current outlook of dynamic climate change. Envirotyping provides a new class of markers for genetic studies, which are fairly inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of enviromics approaches into plant breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity.

NIK1-mediated translation suppression functions as a plant antiviral immunity mechanism.

Plants and plant pathogens are subject to continuous co-evolutionary pressure for dominance, and the outcomes of these interactions can substantially impact agriculture and food security. In virus-plant interactions, one of the major mechanisms for plant antiviral immunity relies on RNA silencing, which is often suppressed by co-evolving virus suppressors, thus enhancing viral pathogenicity in susceptible hosts. In addition, plants use the nucleotide-binding and leucine-rich repeat (NB-LRR) domain-containing resistance proteins, which recognize viral effectors to activate effector-triggered immunity in a defence mechanism similar to that employed in non-viral infections. Unlike most eukaryotic organisms, plants are not known to activate mechanisms of host global translation suppression to fight viruses. Here we demonstrate in Arabidopsis that the constitutive activation of NIK1, a leucine-rich repeat receptor-like kinase (LRR-RLK) identified as a virulence target of the begomovirus nuclear shuttle protein (NSP), leads to global translation suppression and translocation of the downstream component RPL10 to the nucleus, where it interacts with a newly identified MYB-like protein, L10-INTERACTING MYB DOMAIN-CONTAINING PROTEIN (LIMYB), to downregulate translational machinery genes fully. LIMYB overexpression represses ribosomal protein genes at the transcriptional level, resulting in protein synthesis inhibition, decreased viral messenger RNA association with polysome fractions and enhanced tolerance to begomovirus. By contrast, the loss of LIMYB function releases the repression of translation-related genes and increases susceptibility to virus infection. Therefore, LIMYB links immune receptor LRR-RLK activation to global translation suppression as an antiviral immunity strategy in plants.

Applying an association weight matrix in weighted genomic prediction of boar taint compounds.

Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome-wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome-wide association studies (GWAS) in data set containing single- (SL) or a multi-line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM-WssGBLUP). The single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work.

Urea supplementation in rumen and post-rumen for cattle fed a low-quality tropical forage.

We evaluated the differences between the supplementation of urea in rumen and/or abomasum on forage digestion, N metabolism and urea kinetics in cattle fed a low-quality tropical forage. Five Nellore heifers were fitted with rumen and abomasum fistulas and assigned to a Latin square design. The treatments were control, continuous infusion of urea in the abomasum (AC), continuous infusion of urea in the rumen, a pulse dose of urea in the rumen every 12 h (PR) and a combination of PR and AC. The control exhibited the lowest (P < 0·10) faecal and urinary N losses, which were, overall, increased by supplementation. The highest urinary N losses (P < 0·10) were observed when urea was either totally or partially supplied as a ruminal pulse dose. The rumen N balance was negative for the control and when urea was totally supplied in the abomasum. The greatest microbial N production (P < 0·10) was obtained when urea was partially or totally supplied in the abomasum. Urea supplementation increased (P < 0·10) the amount of urea recycled to the gastrointestinal tract and the amount of urea-N returned to the ornithine cycle. The greatest (P < 0·10) amounts of urea-N used for anabolism were observed when urea was totally and continuously infused in the abomasum. The continuous abomasal infusion also resulted in the highest (P < 0·10) assimilation of microbial N from recycling. The continuous releasing of urea throughout day either in the rumen or abomasum is able to improve N accretion in the animal body, despite mechanism responsible for that being different.

Multivariate diallel analysis by factor analysis for establish mega-traits.

In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program. Here, we propose a method of multivariate analysis used for stablishing mega-traits (MTs) in diallel trials. The proposed model is applied in the evaluation of a multi-environment complete diallel trial with 90 F1's of simple maize hybrids. From a set of 14 traits, we demonstrated how establishing and interpreting MTs with agronomic implication. The diallel analyzes based on mega-traits present an important evolution in statistical procedures since the selection is based on several traits. We believe that the proposed method fills an important gap of plant breeding. In our example, three MTs were established. The first, formed by plant stature-related traits, the second by tassel size-related traits, and the third by grain yield-related traits. Individual and joint diallel analysis using the established MTs allowed identifying the best hybrid combinations for achieving F1's with lower plant stature, tassel size, and higher grain yield.

Autoregressive repeatability model for genetic evaluation of longitudinal reproductive traits in dairy cattle.

We investigated the efficiency of the autoregressive repeatability model (AR) for genetic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle and compared the results with those from the conventional repeatability model (REP). The data set comprised records taken during the first four calving orders, corresponding to a total of 416, 766, 872 and 766 thousand records for interval between calving to first service, days open, calving interval and daughter pregnancy rate, respectively. Both models included fixed (month and age classes associated to each calving order) and random (herd-year-season, animal and permanent environmental) effects. For AR model, a first-order autoregressive (co)variance structure was fitted for the herd-year-season and permanent environmental effects. The AR outperformed the REP model, with lower Akaike Information Criteria, lower Mean Square Error and Akaike Weights close to unity. Rank correlations between estimated breeding values (EBV) with AR and REP models ranged from 0.95 to 0.97 for all studied reproductive traits, when the total bulls were considered. When considering only the top-100 selected bulls, the rank correlation ranged from 0.72 to 0.88. These results indicate that the re-ranking observed at the top level will provide more opportunities for selecting the best bulls. The EBV reliabilities provided by AR model was larger for all traits, but the magnitudes of the annual genetic progress were similar between two models. Overall, the proposed AR model was suitable for genetic evaluations of longitudinal reproductive traits in dairy cattle, outperforming the REP model.

Proteomic analysis reveals changes in energy metabolism of skeletal muscle in beef cattle supplemented with vitamin A.

BACKGROUND: Vitamin A has been reported as a factor influencing marbling deposition in meat from animals. Although the mechanisms by which vitamin A regulates lipid metabolism in mature adipocytes are already well-established, information regarding molecular mechanisms underlying the effects of vitamin A on the regulation of intramuscular fat deposition in beef cattle still remains limited. The present study aimed to assess the molecular mechanisms involved in the intramuscular fat deposition in beef cattle supplemented with vitamin A during the fattening phase using a proteomic approach. RESULTS: Vitamin A supplementation during the fattening phase decreased intramuscular fat deposition in beef cattle. Proteome and phospho-proteome analysis together with biological and networking analysis of the protein differentially abundant between treatments indicated that Vitamin A supplementation affects the overall energy metabolism of skeletal muscle, impairing lipid biosynthesis in skeletal muscle. CONCLUSION: Vitamin A supplementation at fattening phase impairs intramuscular fat deposition in beef cattle likely by changing the energy metabolism of skeletal muscle. The interaction of retinoic acid and heat shock 70-kDa protein may play a pivotal role in intramuscular fat deposition as a consequence of vitamin A supplementation by impairing de novo fatty acid synthesis as a result of a possible decrease in insulin sensitivity in the skeletal muscle. © 2020 Society of Chemical Industry.

Differentially expressed mRNAs, proteins and miRNAs associated to energy metabolism in skeletal muscle of beef cattle identified for low and high residual feed intake.

BACKGROUND: Feed efficiency is one of the most important parameters that affect beef production costs. The energy metabolism of skeletal muscle greatly contributes to variations in feed efficiency. However, information regarding differences in proteins involved in the energy metabolism of the skeletal muscle in beef cattle divergently identified for feed efficiency is scarce. In this study, we aimed to investigate energy metabolism of skeletal muscle of Nellore beef cattle, identified for low and high residual feed intake using a proteomics approach. We further assessed the expression of candidate microRNAs as a one of the possible mechanisms controlling the biosynthesis of the proteins involved in energy metabolism that were differentially abundant between high and low residual feed intake animals. RESULTS: A greater abundance of 14-3-3 protein epsilon (P = 0.01) was observed in skeletal muscle of residual feed intake (RFI) high animals (RFI-High). Conversely, a greater abundance of Heat Shock Protein Beta 1 (P < 0.01) was observed in the skeletal muscle of RFI-Low cattle. A greater mRNA expression of YWHAE, which encodes the 14-3-3 protein epsilon, was also observed in the skeletal muscle of RFI-High animals (P = 0.01). A lower mRNA expression of HSPB1, which encodes the Heat Shock Protein Beta 1, was observed in the skeletal muscle of RFI-High animals (P = 0.01). The miR-665 was identified as a potential regulator of the 14-3-3 protein epsilon, and its expression was greater in RFI-Low animals (P < .001). A greater expression of miR-34a (P = 0.01) and miR-2899 (P < .001) was observed in the skeletal muscle of RFI-High animals, as both miRNAs were identified as potential regulators of HSPB1 expression. CONCLUSION: Our results show that Nellore cattle divergently identified for feed efficiency by RFI present changes in the abundance of proteins involved in energy expenditure in skeletal muscle. Moreover, our data point towards that miR-665, miR34a and miR-2899 are likely involved in controlling both 14-3-3 epsilon and HSPB1 proteins identified as differentially abundant in the skeletal muscle of RFI-High and RFI-Low Nellore cattle.

Effects of alleles in crossbred pigs estimated for genomic prediction depend on their breed-of-origin.

BACKGROUND: This study investigated if the allele effect of a given single nucleotide polymorphism (SNP) for crossbred performance in pigs estimated in a genomic prediction model differs depending on its breed-of-origin, and how these are related to estimated effects for purebred performance. RESULTS: SNP-allele substitution effects were estimated for a commonly used SNP panel using a genomic best linear unbiased prediction model with breed-specific partial relationship matrices. Estimated breeding values for purebred and crossbred performance were converted to SNP-allele effects by breed-of-origin. Differences between purebred and crossbred, and between breeds-of-origin were evaluated by comparing percentage of variance explained by genomic regions for back fat thickness (BF), average daily gain (ADG), and residual feed intake (RFI). From ten regions explaining most additive genetic variance for crossbred performance, 1 to 5 regions also appeared in the top ten for purebred performance. The proportion of genetic variance explained by a genomic region and the estimated effect of a haplotype in such a region were different depending upon the breed-of-origin. To illustrate underlying mechanisms, we evaluated the estimated effects across breeds-of-origin for haplotypes associated to the melanocortin 4 receptor (MC4R) gene, and for the MC4Rsnp itself which is a missense mutation with a known effect on BF and ADG. Although estimated allele substitution effects of the MC4Rsnp mutation were very similar across breeds, explained genetic variance of haplotypes associated to the MC4R gene using a SNP panel that does not include the mutation, was considerably lower in one of the breeds where the allele frequency of the mutation was the lowest. CONCLUSIONS: Similar regions explaining similar additive genetic variance were observed across purebred and crossbred performance. Moreover, there was some overlap across breeds-of-origin between regions that explained relatively large proportions of genetic variance for crossbred performance; albeit that the actual proportion of variance deviated across breeds-of-origin. Results based on a missense mutation in MC4R confirmed that even if a causal locus has similar effects across breeds-of-origin, estimated effects and explained variance in its region using a commonly used SNP panel can strongly depend on the allele frequency of the underlying causal mutation.

Weighted single-step GWAS and gene network analysis reveal new candidate genes for semen traits in pigs.

BACKGROUND: In recent years, there has been increased interest in the study of the molecular processes that affect semen traits. In this study, our aim was to identify quantitative trait loci (QTL) regions associated with four semen traits (motility, progressive motility, number of sperm cells per ejaculate and total morphological defects) in two commercial pig lines (L1: Large White type and L2: Landrace type). Since the number of animals with both phenotypes and genotypes was relatively small in our dataset, we conducted a weighted single-step genome-wide association study, which also allows unequal variances for single nucleotide polymorphisms. In addition, our aim was also to identify candidate genes within QTL regions that explained the highest proportions of genetic variance. Subsequently, we performed gene network analyses to investigate the biological processes shared by genes that were identified for the same semen traits across lines. RESULTS: We identified QTL regions that explained up to 10.8% of the genetic variance of the semen traits on 12 chromosomes in L1 and 11 chromosomes in L2. Sixteen QTL regions in L1 and six QTL regions in L2 were associated with two or more traits within the population. Candidate genes SCN8A, PTGS2, PLA2G4A, DNAI2, IQCG and LOC102167830 were identified in L1 and NME5, AZIN2, SPATA7, METTL3 and HPGDS in L2. No regions overlapped between these two lines. However, the gene network analysis for progressive motility revealed two genes in L1 (PLA2G4A and PTGS2) and one gene in L2 (HPGDS) that were involved in two biological processes i.e. eicosanoid biosynthesis and arachidonic acid metabolism. PTGS2 and HPGDS were also involved in the cyclooxygenase pathway. CONCLUSIONS: We identified several QTL regions associated with semen traits in two pig lines, which confirms the assumption of a complex genetic determinism for these traits. A large part of the genetic variance of the semen traits under study was explained by different genes in the two evaluated lines. Nevertheless, the gene network analysis revealed candidate genes that are involved in shared biological pathways that occur in mammalian testes, in both lines.

Geminivirus data warehouse: a database enriched with machine learning approaches.

BACKGROUND: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics. RESULTS: Here, we describe the development of a data warehouse enriched with ML approaches, designated geminivirus.org. We implemented search modules, bioinformatics tools, and ML methods to retrieve high precision information, demarcate species, and create classifiers for genera and open reading frames (ORFs) of geminivirus genomes. CONCLUSIONS: The use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain a database with quality data and suitable tools for bioinformatics analysis. The Geminivirus Data Warehouse (geminivirus.org) offers a simple and user-friendly environment for information retrieval and knowledge discovery related to geminiviruses.

Gene networks for total number born in pigs across divergent environments.

For reproductive traits such as total number born (TNB), variance due to different environments is highly relevant in animal breeding. In this study, we aimed to perform a gene-network analysis for TNB in pigs across different environments using genomic reaction norm models. Thus, based on relevant single-nucleotide polymorphisms and linkage disequilibrium blocks across environments obtained from GWAS, different sets of candidate genes having biological roles linked to TNB were identified. Network analysis across environment levels resulted in gene interactions consistent with known mammal's fertility biology, captured relevant transcription factors for TNB biology and pointing out different sets of candidate genes for TNB in different environments. These findings may have important implication for animal production, as optimal breeding may vary depending on later environments. Based on these results, genomic diversity was identified and inferred across environments highlighting differential genetic control in each scenario.

Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models.

BACKGROUND: The cattle tick is a parasite that adversely affects livestock performance in tropical areas. Although countries such as Australia and Brazil have developed genetic evaluations for tick resistance, these evaluations have not considered genotype by environment (G*E) interactions. Genetic gains could be adversely affected, since breedstock comparisons are environmentally dependent on the presence of G*E interactions, particularly if residual variability is also heterogeneous across environments. The objective of this study was to infer upon the existence of G*E interactions for tick resistance of cattle based on various models with different assumptions of genetic and residual variability. METHODS: Data were collected by the Delta G Connection Improvement program and included 10,673 records of tick counts on 4363 animals. Twelve models, including three traditional animal models (AM) and nine different hierarchical Bayesian reaction norm models (HBRNM), were investigated. One-step models that jointly estimate environmental covariates and reaction norms and two-step models based on previously estimated environmental covariates were used to infer upon G*E interactions. Model choice was based on the deviance criterion information. RESULTS: The best-fitting model specified heterogeneous residual variances across 10 subclasses that were bounded by every decile of the contemporary group (CG) estimates of tick count effects. One-step models generally had the highest estimated genetic variances. Heritability estimates were normally higher for HBRNM than for AM. One-step models based on heterogeneous residual variances also usually led to higher heritability estimates. Estimates of repeatability varied along the environmental gradient (ranging from 0.18 to 0.45), which implies that the relative importance of additive and permanent environmental effects for tick resistance is influenced by the environment. Estimated genetic correlations decreased as the tick infestation level increased, with negative correlations between extreme environmental levels, i.e., between more favorable (low infestation) and harsh environments (high infestation). CONCLUSIONS: HBRNM can be used to describe the presence of G*E interactions for tick resistance in Hereford and Braford beef cattle. The preferred model for the genetic evaluation of this population for tick counts in Brazilian climates was a one-step model that considered heteroscedastic residual variance. Reaction norm models are a powerful tool to identify and quantify G*E interactions and represent a promising alternative for genetic evaluation of tick resistance, since they are expected to lead to greater selection efficiency and genetic progress.

Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways.

BACKGROUND: Reproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks. RESULTS: Comparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes. CONCLUSIONS: Our comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known newborn survival traits (e.g. congenital heart disease in fetuses and kidney diseases and diabetes in the mother) and mammary gland biology (e.g. mammary gland development and body length).

Sustained NIK-mediated antiviral signalling confers broad-spectrum tolerance to begomoviruses in cultivated plants.

Begomovirus-associated epidemics currently threaten tomato production worldwide due to the emergence of highly pathogenic virus species and the proliferation of a whitefly B biotype vector that is adapted to tomato. To generate an efficient defence against begomovirus, we modulated the activity of the immune defence receptor nuclear shuttle protein (NSP)-interacting kinase (NIK) in tomato plants; NIK is a virulence target of the begomovirus NSP during infection. Mutation of T474 within the kinase activation loop promoted the constitutive activation of NIK-mediated defences, resulting in the down-regulation of translation-related genes and the suppression of global translation. Consistent with these findings, transgenic lines harbouring an activating mutation (T474D) were tolerant to the tomato-infecting begomoviruses ToYSV and ToSRV. This phenotype was associated with reduced loading of coat protein viral mRNA in actively translating polysomes, lower infection efficiency and reduced accumulation of viral DNA in systemic leaves. Our results also add some relevant insights into the mechanism underlying the NIK-mediated defence. We observed that the mock-inoculated T474D-overexpressing lines showed a constitutively infected wild-type transcriptome, indicating that the activation of the NIK-mediated signalling pathway triggers a typical response to begomovirus infection. In addition, the gain-of-function mutant T474D could sustain an activated NIK-mediated antiviral response in the absence of the virus, further confirming that phosphorylation of Thr-474 is the crucial event that leads to the activation of the kinase.

Linkage disequilibrium patterns and persistence of phase in purebred and crossbred pig (Sus scrofa) populations.

BACKGROUND: Genomic selection and genomic wide association studies are widely used methods that aim to exploit the linkage disequilibrium (LD) between markers and quantitative trait loci (QTL). Securing a sufficiently large set of genotypes and phenotypes can be a limiting factor that may be overcome by combining data from multiple breeds or using crossbred information. However, the estimated effect of a marker in one breed or a crossbred can only be useful for the selection of animals in another breed if there is a correspondence of the phase between the marker and the QTL across breeds. Using data of five pure pig (Sus scrofa) lines (SL1, SL2, SL3, DL1, DL2), one F1 cross (DLF1) and two commercial finishing crosses (TER1 and TER2), the objectives of this study were: (i) to compare the equality of LD decay curves of different pig populations; and (ii) to evaluate the persistence of the LD phase across lines or final crosses. RESULTS: Almost all of the lines presented different extents of LD, except for the SL2 and DL3, both of which exhibited the same extent of LD. Similar levels of LD over large distances were found in crossbred and pure lines. The crossbred animals (DLF1, TER1 and TER2) presented a high persistence of phase with their parental lines, suggesting that the available porcine single nucleotide polymorphism (SNP) chip should be dense enough to include markers that have the same LD phase with QTL across crossbred and parental pure lines. The persistence of phase across pure lines varied considerably between the different line comparisons; however, correlations were above 0.8 for all line comparisons when marker distances were smaller than 50 kb. CONCLUSIONS: This study showed that crossbred populations could be very useful as a reference for the selection of pure lines by means of the available SNP chip panel. Here, we also pinpoint pure lines that could be combined in a multiline training population. However, if multiline reference populations are used for genomic selection, the required density of SNP panels should be higher compared with a single breed reference population.

Improved estimation of inbreeding and kinship in pigs using optimized SNP panels.

BACKGROUND: Traditional breeding programs consider an average pairwise kinship between sibs. Based on pedigree information, the relationship matrix is used for genetic evaluations disregarding variation due to Mendelian sampling. Therefore, inbreeding and kinship coefficients are either over or underestimated resulting in reduction of accuracy of genetic evaluations and genetic progress. Single nucleotide polymorphism (SNPs) can be used to estimate pairwise kinship and individual inbreeding more accurately. The aim of this study was to optimize the selection of markers and determine the required number of SNPs for estimation of kinship and inbreeding. RESULTS: A total of 1,565 animals from three commercial pig populations were analyzed for 28,740 SNPs from the PorcineSNP60 Beadchip. Mean genomic inbreeding was higher than pedigree-based estimates in lines 2 and 3, but lower in line 1. As expected, a larger variation of genomic kinship estimates was observed for half and full sibs than for pedigree-based kinship reflecting Mendelian sampling. Genomic kinship between father-offspring pairs was lower (0.23) than the estimate based on pedigree (0.26). Bootstrap analyses using six reduced SNP panels (n = 500, 1000, 1500, 2000, 2500 and 3000) showed that 2,000 SNPs were able to reproduce the results very close to those obtained using the full set of unlinked markers (n = 7,984-10,235) with high correlations (inbreeding r > 0.82 and kinship r > 0.96) and low variation between different sets with the same number of SNPs. CONCLUSIONS: Variation of kinship between sibs due to Mendelian sampling is better captured using genomic information than the pedigree-based method. Therefore, the reduced sets of SNPs could generate more accurate kinship coefficients between sibs than the pedigree-based method. Variation of genomic kinship of father-offspring pairs is recommended as a parameter to determine accuracy of the method rather than correlation with pedigree-based estimates. Inbreeding and kinship coefficients can be estimated with high accuracy using ≥2,000 unlinked SNPs within all three commercial pig lines evaluated. However, a larger number of SNPs might be necessary in other populations or across lines.

Fine mapping and single nucleotide polymorphism effects estimation on pig chromosomes 1, 4, 7, 8, 17 and X.

Fine mapping of quantitative trait loci (QTL) from previous linkage studies was performed on pig chromosomes 1, 4, 7, 8, 17, and X which were known to harbor QTL. Traits were divided into: growth performance, carcass, internal organs, cut yields, and meat quality. Fifty families were used of a F2 population produced by crossing local Brazilian Piau boars with commercial sows. The linkage map consisted of 237 SNP and 37 microsatellite markers covering 866 centimorgans. QTL were identified by regression interval mapping using GridQTL. Individual marker effects were estimated by Bayesian LASSO regression using R. In total, 32 QTL affecting the evaluated traits were detected along the chromosomes studied. Seven of the QTL were known from previous studies using our F2 population, and 25 novel QTL resulted from the increased marker coverage. Six of the seven QTL that were significant at the 5% genome-wide level had SNPs within their confidence interval whose effects were among the 5% largest effects. The combined use of microsatellites along with SNP markers increased the saturation of the genome map and led to smaller confidence intervals of the QTL. The results showed that the tested models yield similar improvements in QTL mapping accuracy.

The tomato RLK superfamily: phylogeny and functional predictions about the role of the LRRII-RLK subfamily in antiviral defense.

BACKGROUND: Receptor-like kinases (RLKs) play key roles during development and in responses to the environment. Despite the relevance of the RLK family and the completion of the tomato genome sequencing, the tomato RLK family has not yet been characterized, and a framework for functional predictions of the members of the family is lacking. RESULTS: To generate a complete list of all the members of the tomato RLK family, we performed a phylogenetic analysis using the Arabidopsis family as a template. A total of 647 RLKs were identified in the tomato genome, which were organized into the same subfamily clades as Arabidopsis RLKs. Only eight of 58 RLK subfamilies exhibited specific expansion/reduction compared to their Arabidopsis counterparts. We also characterized the LRRII-RLK family by phylogeny, genomic analysis, expression profile and interaction with the virulence factor from begomoviruses, the nuclear shuttle protein (NSP). The LRRII subfamily members from tomato and Arabidopsis were highly conserved in both sequence and structure. Nevertheless, the majority of the orthologous pairs did not display similar conservation in the gene expression profile, indicating that these orthologs may have diverged in function after speciation. Based on the fact that members of the Arabidopsis LRRII subfamily (AtNIK1, AtNIK2 and AtNIK3) interact with the begomovirus nuclear shuttle protein (NSP), we examined whether the tomato orthologs of NIK, BAK1 and NsAK genes interact with NSP of Tomato Yellow Spot Virus (ToYSV). The tomato orthologs of NSP interactors, SlNIKs and SlNsAK, interacted specifically with NSP in yeast and displayed an expression pattern consistent with the pattern of geminivirus infection. In addition to suggesting a functional analogy between these phylogenetically classified orthologs, these results expand our previous observation that NSP-NIK interactions are neither virus-specific nor host-specific. CONCLUSIONS: The tomato RLK superfamily is made-up of 647 proteins that form a monophyletic tree with the Arabidopsis RLKs and is divided into 58 subfamilies. Few subfamilies have undergone expansion/reduction, and only six proteins were lineage-specific. Therefore, the tomato RLK family shares functional and structural conservation with Arabidopsis. For the LRRII-RLK members SlNIK1 and SlNIK3, we observed functions analogous to those of their Arabidopsis counterparts with respect to protein-protein interactions and similar expression profiles, which predominated in tissues that support high efficiency of begomovirus infection. Therefore, NIK-mediated antiviral signaling is also likely to operate in tomato, suggesting that tomato NIKs may be good targets for engineering resistance against tomato-infecting begomoviruses.