Whole-genome resequencing reveals signatures of selection and timing of duck domestication

BackgroundThe genetic basis of animal domestication remains poorly understood, and systems with substantial phenotypic differences between wild and domestic populations are useful for elucidating the genetic basis of adaptation to new environments as well as the genetic basis of rapid phenotypic change. Here, we sequenced the whole genome of 78 individual ducks, from two wild and seven domesticated populations, with an average sequencing depth of 6.42X per individual.ResultsOur population and demographic analyses indicate a complex history of domestication, with early selection for separate meat and egg lineages. Genomic comparison of wild to domesticated populations suggests that genes that affect brain and neuronal development have undergone strong positive selection during domestication. Our FST analysis also indicates that the duck white plumage is the result of selection at the melanogenesis-associated transcription factor locus.ConclusionsOur results advance the understanding of animal domestication and selection for complex phenotypic traits

A genome-wide association study explores the genetic determinism of host resistance to Salmonella pullorum infection in chickens

International audienceAbstractBackgroundSalmonella infection is a serious concern in poultry farming because of its impact on both economic loss and human health. Chicks aged 20 days or less are extremely vulnerable to Salmonella pullorum (SP), which causes high mortality. Furthermore, an outbreak of SP infection can result in a considerable number of carriers that become potential transmitters, thus, threatening fellow chickens and offspring. In this study, we conducted a genome-wide association study (GWAS) to detect potential genomic loci and candidate genes associated with two disease-related traits: death and carrier state.MethodsIn total, 818 birds were phenotyped for death and carrier state traits through a SP challenge experiment, and genotyped by using a 600 K high-density single nucleotide polymorphism (SNP) array. A GWAS using a single-marker linear mixed model was performed with the GEMMA software. RNA-sequencing on spleen samples was carried out for further identification of candidate genes.ResultsWe detected a region that was located between 33.48 and 34.03 Mb on chicken chromosome 4 and was significantly associated with death, with the most significant SNP (rs314483802) accounting for 11.73% of the phenotypic variation. Two candidate genes, FBXW7 and LRBA, were identified as the most promising genes involved in resistance to SP. The expression levels of FBXW7 and LRBA were significantly downregulated after SP infection, which suggests that they may have a role in controlling SP infections. Two other significant loci and related genes (TRAF3 and gga-mir-489) were associated with carrier state, which indicates a different polygenic determinism compared with that of death. In addition, genomic inbreeding coefficients showed no correlation with resistance to SP within each breed in our study.ConclusionsThe results of this GWAS with a carefully organized Salmonella challenge experiment represent an important milestone in understanding the genetics of infectious disease resistance, offer a theoretical basis for breeding SP-resistant chicken lines using marker-assisted selection, and provide new information for salmonellosis research in humans and other animals

Protein asparagine deamidation prediction based on structures with machine learning methods

<div><p>Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein “hotspots” are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure–function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.</p></div

Performance comparison between NG-motif, NGOME and our structure-based methods.

<p>NG-motif and NGOME prediction performances were represented by plotting the TPR v.s. FPR points (red triangle for NG-motif and blue square for NGOME) on the ROC of the RF method (purple line).</p

Cross validation of binary deamidation prediction models.

<p>Cross validation of binary deamidation prediction models.</p

RBF Neural Network Fractional-Order Sliding Mode Control with an Application to Direct a Three Matrix Converter under an Unbalanced Grid

This paper presents a fractional-order sliding mode control scheme based on an RBF neural network (RBFFOSMC) for a direct three matrix converter (DTMC) operating under unbalanced grid voltages. The RBF neural network (RBF NN) is designed to approximate a nonlinear fractional-order sliding mode controller. The proposed method aims to achieve constant active power whilst maintaining a near unity input power factor. First, an opportune reference current is accurately generated according to the reference power and the RBFFOSMC is designed in a dq reference frame to achieve a perfect tracking of the input current reference. An almost constant active power, free of low-frequency ripples, is then supplied from the grid after compensating for the output voltage. Simulation and experimental studies prove the feasibility and effectiveness of the proposed control method

Different types of descriptors that were developed for building deamidation prediction models.

<p>Different types of descriptors that were developed for building deamidation prediction models.</p

Comparison between NG-motif, NGOME, and our structure-based prediction methods.

<p>Comparison between NG-motif, NGOME, and our structure-based prediction methods.</p