Efficient models of cortical activity via local dynamic equilibria and coarse-grained interactions.

Biologically detailed models of brain circuitry are challenging to build and simulate due to the large number of neurons, their complex interactions, and the many unknown physiological parameters. Simplified mathematical models are more tractable, but harder to evaluate when too far removed from neuroanatomy/physiology. We propose that a multiscale model, coarse-grained (CG) while preserving local biological details, offers the best balance between biological realism and computability. This paper presents such a model. Generally, CG models focus on the interaction between groups of neurons-here termed "pixels"-rather than individual cells. In our case, dynamics are alternately updated at intra- and interpixel scales, with one informing the other, until convergence to equilibrium is achieved on both scales. An innovation is how we exploit the underlying biology: Taking advantage of the similarity in local anatomical structures across large regions of the cortex, we model intrapixel dynamics as a single dynamical system driven by "external" inputs. These inputs vary with events external to the pixel, but their ranges can be estimated a priori. Precomputing and tabulating all potential local responses speed up the updating procedure significantly compared to direct multiscale simulation. We illustrate our methodology using a model of the primate visual cortex. Except for local neuron-to-neuron variability (necessarily lost in any CG approximation) our model reproduces various features of large-scale network models at a tiny fraction of the computational cost. These include neuronal responses as a consequence of their orientation selectivity, a primary function of visual neurons.

[18F]FDG PET integrated with structural MRI for accurate brain age prediction.

PURPOSE: Brain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data. METHODS: The dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and structural magnetic resonance imaging (sMRI) images of NC subjects as input. Several prevailing DL models were trained and tested using either MRI or PET data for comparison. Model accuracies were evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (r). Brain age gap (BAG), deviations of brain age from chronologic age, was correlated with cognitive assessments in MCI and AD subjects. RESULTS: Both PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI). CONCLUSION: The integration of [18F]FDG PET with structural MRI enhances the accuracy of brain age prediction, potentially introducing a new avenue for related multimodal brain age prediction studies.

Learning spiking neuronal networks with artificial neural networks: neural oscillations.

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by a spiking neuronal network model. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.

Selection on the promoter regions plays an important role in complex traits during duck domestication.

BACKGROUND: Identifying the key factors that underlie complex traits during domestication is a great challenge for evolutionary and biological studies. In addition to the protein-coding region differences caused by variants, a large number of variants are located in the noncoding regions containing multiple types of regulatory elements. However, the roles of accumulated variants in gene regulatory elements during duck domestication and economic trait improvement are poorly understood. RESULTS: We constructed a genomics, transcriptomics, and epigenomics map of the duck genome and assessed the evolutionary forces that have been in play across the whole genome during domestication. In total, 304 (42.94%) gene promoters have been specifically selected in Pekin duck among all selected genes. Joint multi-omics analysis reveals that 218 genes (72.01%) with selected promoters are located in open and active chromatin, and 267 genes (87.83%) with selected promoters were highly and differentially expressed in domestic trait-related tissues. One important candidate gene ELOVL3, with a strong signature of differentiation on the core promoter region, is known to regulate fatty acid elongation. Functional experiments showed that the nearly fixed variants in the top selected ELOVL3 promoter in Pekin duck decreased binding ability with HLF and increased gene expression, with the overexpression of ELOVL3 able to increase lipid deposition and unsaturated fatty acid enrichment. CONCLUSIONS: This study presents genome resequencing, RNA-Seq, Hi-C, and ATAC-Seq data of mallard and Pekin duck, showing that selection of the gene promoter region plays an important role in gene expression and phenotypic changes during domestication and highlights that the variants of the ELOVL3 promoter may have multiple effects on fat and long-chain fatty acid content in ducks.

Fe1-xNix(PO3)2/Ni2P Heterostructure for Boosting Alkaline Oxygen Evolution Reaction in Fresh Water and Real Seawater at High Current Density.

Oxygen evolution reaction (OER) is a limiting reaction for highly efficient water electrolysis. Thus, the development of cost-effective and highly efficient OER catalysts is the key to large-scale water electrolysis for hydrogen production. Herein, by using an interfacial engineering strategy, a unique nanoflower-like Fe1-xNix(PO3)2/Ni2P/NF heterostructure with abundant heterogeneous interfaces is successfully fabricated. The catalyst exhibits excellent OER catalytic activity in alkaline fresh water and alkaline natural seawater at high current densities, which only, respectively, requires overpotentials of 318 and 367 mV to drive 1000 mA cm-2 in fresh water and natural seawater both containing 1 M KOH. Furthermore, Fe1-xNix(PO3)2/Ni2P/NF demonstrates excellent durability, which can basically remain stable for 80 h during the electrocatalytic OER processes, respectively, in alkaline fresh water and natural seawater. This work provides a new construction strategy for designing highly efficient electrocatalysts for OER at high current densities both in alkaline fresh water and in natural seawater.

Genome-wide association study identified the candidate genes associated with angel wing trait in Pekin duck.

Angel wing is a developmental wing deformity that can influence breeding and reproduction in the commercial duck industry. The nutrition foundation of angel wing trait was initially explored, but the genetic basic remains poorly understood. In this study, we identified candidate genes and single-nucleotide polymorphisms (SNPs) associated with angel wing trait in Pekin ducks using a genome-wide association study (GWAS) and selective sweep analysis. The GWAS results showed that nine SNPs across five chromosomes were significantly correlated with the angel wing trait. In total, 468 selection signals were shown between the angel wing ducks and normal ducks, and these signals harbored 154 genes, which were enriched in the nervous system and metabolism. This study provides the new insights into the genetic factors that may influence duck angel wing.

Multi-band oscillations emerge from a simple spiking network.

In the brain, coherent neuronal activities often appear simultaneously in multiple frequency bands, e.g., as combinations of alpha (8-12 Hz), beta (12.5-30 Hz), and gamma (30-120 Hz) oscillations, among others. These rhythms are believed to underlie information processing and cognitive functions and have been subjected to intense experimental and theoretical scrutiny. Computational modeling has provided a framework for the emergence of network-level oscillatory behavior from the interaction of spiking neurons. However, due to the strong nonlinear interactions between highly recurrent spiking populations, the interplay between cortical rhythms in multiple frequency bands has rarely been theoretically investigated. Many studies invoke multiple physiological timescales (e.g., various ion channels or multiple types of inhibitory neurons) or oscillatory inputs to produce rhythms in multi-bands. Here, we demonstrate the emergence of multi-band oscillations in a simple network consisting of one excitatory and one inhibitory neuronal population driven by constant input. First, we construct a data-driven, Poincaré section theory for robust numerical observations of single-frequency oscillations bifurcating into multiple bands. Then, we develop model reductions of the stochastic, nonlinear, high-dimensional neuronal network to capture the appearance of multi-band dynamics and the underlying bifurcations theoretically. Furthermore, when viewed within the reduced state space, our analysis reveals conserved geometrical features of the bifurcations on low-dimensional dynamical manifolds. These results suggest a simple geometric mechanism behind the emergence of multi-band oscillations without appealing to oscillatory inputs or multiple synaptic or neuronal timescales. Thus, our work points to unexplored regimes of stochastic competition between excitation and inhibition behind the generation of dynamic, patterned neuronal activities.

Chromosome-level genome assembly of the Muscovy duck provides insight into fatty liver susceptibility.

The Muscovy duck (Cairina moschata) is an economically important poultry species, which is susceptible to fatty liver. Thus, the Muscovy duck may serve as an excellent candidate animal model of non-alcoholic fatty liver disease. However, the mechanisms underlying fatty liver development in this species are poorly understood. In this study, we report a chromosome-level genome assembly of the Muscovy duck, with a contig N50 of 11.8 Mb and scaffold N50 of 83.16 Mb. The susceptibility of Muscovy duck to fatty liver was mainly attributed to weak lipid catabolism capabilities (fatty acid ß-oxidation and lipolysis). Furthermore, conserved noncoding elements (CNEs) showing accelerated evolution contributed to fatty liver formation by down-regulating the expression of genes involved in hepatic lipid catabolism. We propose that the susceptibility of Muscovy duck to fatty liver is an evolutionary by-product. In conclusion, this study revealed the potential mechanisms underlying the susceptibility of Muscovy duck to fatty liver.

A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT.

PURPOSE: This study aimed to develop a novel analytic approach based on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) radiomic signature (RS) and International Prognostic Index (IPI) to predict the progression-free survival (PFS) and overall survival (OS) of patients with diffuse large B-cell lymphoma (DLBCL). METHODS: We retrospectively enrolled 152 DLBCL patients and divided them into a training cohort (n = 100) and a validation cohort (n = 52). A total of 1245 radiomic features were extracted from the total metabolic tumor volume (TMTV) and the metabolic bulk volume (MBV) of pre-treatment PET/CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to develop the RS. Cox regression analysis was used to construct hybrid nomograms based on different RS and clinical variables. The performances of hybrid nomograms were evaluated using the time-dependent receiver operator characteristic (ROC) curve and the Hosmer-Lemeshow test. The clinical utilities of prediction nomograms were determined via decision curve analysis. The predictive efficiency of different RS, clinical variables, and hybrid nomograms was compared. RESULTS: The RS and IPI were identified as independent predictors of PFS and OS, and were selected to construct hybrid nomograms. Both TMTV- and MBV-based hybrid nomograms had significantly higher values of area under the curve (AUC) than IPI in training and validation cohorts (all P < 0.05), while no significant difference was found between TMTV- and MBV-based hybrid nomograms (P > 0.05). The Hosmer-Lemeshow test showed that both TMTV- and MBV-based hybrid nomograms calibrated well in the training and validation cohorts (all P > 0.05). Decision curve analysis indicated that hybrid nomograms had higher net benefits than IPI. CONCLUSION: The hybrid nomograms combining RS with IPI could significantly improve survival prediction in DLBCL. Radiomic analysis on MBV may serve as a potential approach for prognosis assessment in DLBCL. TRIAL REGISTRATION: NCT04317313. Registered March 16, 2020. Public site: https://clinicaltrials.gov/ct2/show/NCT04317313.

Multilevel monte carlo for cortical circuit models.

Multilevel Monte Carlo (MLMC) methods aim to speed up computation of statistics from dynamical simulations. MLMC is easy to implement and is sometimes very effective, but its efficacy may depend on the underlying dynamics. We apply MLMC to networks of spiking neurons and assess its effectiveness on prototypical models of cortical circuitry under different conditions. We find that MLMC can be very efficient for computing reliable features, i.e., features of network dynamics that are reproducible upon repeated presentation of the same external forcing. In contrast, MLMC is less effective for complex, internally generated activity. Qualitative explanations are given using concepts from random dynamical systems theory.

A data-informed mean-field approach to mapping of cortical parameter landscapes.

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a "biologically plausible" region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.

Genome-wide association study reveals novel loci associated with feeding behavior in Pekin ducks.

BACKGROUND: Feeding behavior traits are an essential part of livestock production. However, the genetic base of feeding behavior traits remains unclear in Pekin ducks. This study aimed to determine novel loci related to feeding behavior in Pekin ducks. RESULTS: In this study, the feeding information of 540 Pekin ducks was recorded, and individual genotype was evaluated using genotyping-by-sequencing methods. Genome-wide association analysis (GWAS) was conducted for feeding behavior traits. Overall, thirty significant (P-value < 4.74E-06) SNPs for feeding behavior traits were discovered, and four of them reached the genome-wide significance level (P-value < 2.37E-07). One genome-wide significance locus associated with daily meal times was located in a 122.25 Mb region on chromosome 2, which was within the intron of gene ubiquitin-conjugating enzyme E2 E2 (UBE2E2), and could explain 2.64% of the phenotypic variation. This locus was also significantly associated with meal feed intake, and explained 2.72% of this phenotypic variation. CONCLUSIONS: This study is the first GWAS for feeding behavior traits in ducks. Our results provide a list of candidate genes associated with feeding behavior, and also help to better understand the genetic mechanisms of feeding behavior patterns in ducks.

A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE). METHODS: We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent 18F-FDG PET-CT studies. 18F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis. RESULTS: The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31-0.44, P = 0.005-0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01). CONCLUSION: The proposed deep learning framework for 18F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients. TRIAL REGISTRATION: NCT04169581. Registered November 13, 2019 Public site: https://clinicaltrials.gov/ct2/show/NCT04169581.

Williamsia soli sp. nov., an actinobacterium isolated from soil at a thermal power plant in Yantai, China.

Strain C17T, a novel strain belonging to the phylum Actinobacteria, was isolated from a thermal power plant in Yantai, Shandong Province, China. Cells of strain C17T were Gram stain positive, aerobic, pink, non-motile and round with neat edges, showing optimum growth at 28 °C. Phylogenetically, strain C17T was a member of the class Actinobacteria, order Mycobacteriales, family Gordoniaceae. Phylogenetic analysis based on 16S rRNA gene sequence comparisons revealed that the related strains were Williamsia faeni JCM 17784 T and Williamsia limnetica KCTC 19981 T with pairwise sequence similarity of 98.5% for both strains. According to the draft genome sequence, the DNA G + C content was 64.7%. The average amino acid identity (AAI), average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between genome sequences of strain C17T and the closest type strain W. faeni JCM 17784 T were 77.5, 77.9, and 20.7%, respectively. Predominant fatty acids were C16:0 (31.7%) and C18:1ω9c (26.8%). The major menaquinone was MK-9. The diagnostic phospholipids were phosphatidylethanolamine (PE), diphosphatidylglycerol (DPG), and phosphatidylinositol (PI). Therefore, the combined phenotypic, chemotaxonomic and phylogenetic data indicated that strain C17T was considered to represent a novel species of the genus Williamsia. Williamsia soli sp. nov. was proposed for strain C17T (= KCTC 49567 T = MCCC 1K04355T).

Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes.

OBJECTIVES: Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using 18F-fluorodeoxyglucose positron emission tomography ([18F]FDG PET) imaging, and explore whether these differences can help distinguish. METHODS: Forty-six patients with typical BECTS, 31 patients with atypical BECTS and 23 controls who underwent [18F]FDG PET examination were retrospectively involved. Absolute asymmetry index (|AI|) was applied to evaluate the severity of metabolic abnormality. Glucose metabolic differences were investigated among typical BECTS, atypical BECTS, and controls by using statistical parametric mapping (SPM). Logistic regression analyses were performed based on clinical, PET, and hybrid features. RESULTS: The |AI| was found significantly higher in atypical BECTS than in typical BECTS (p = 0.040). Atypical BECTS showed more hypo-metabolism regions than typical BECTS, mainly located in the fronto-temporo-parietal cortex. The PET model had significantly higher area under the curve (AUC) than the clinical model (0.91 vs. 0.70, p = 0.006). The hybrid model had the highest sensitivity (0.90), specificity (0.85), and accuracy (0.87) of all three models. CONCLUSIONS: Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, depending on which the two groups can be well distinguished. The combination of metabolic characteristics and clinical variables has the potential to be used clinically to distinguish between typical and atypical BECTS. KEY POINTS: • Distinguishing between typical and atypical BECTS is very important for the formulation of treatment regimens in clinical practice. • Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, mainly located in the fronto-temporo-parietal cortex. • The logistic regression model based on PET outperformed that based on clinical characteristics in classification of typical and atypical BECTS, and the hybrid model achieved the best classification performance.

The transcriptome landscapes of ovary and three oviduct segments during chicken (Gallus gallus) egg formation.

The avian embryo develops within a specialized biological container (eggshell) that contains crucial nutritional compartments (albumen, yolk). We analyzed the transcriptome of ovary and three segments of oviduct, including magnum, isthmus and uterus in the chicken during egg formation. RNA-Seq libraries (42 in total) for ovary and three different parts of the oviduct were sequenced for two different phases of egg formation. We obtained 8365 novel transcripts with an mRNA length longer than 200 bp; of these, 6832 were long intergenic non-coding RNA transcripts. We identified 547 differentially expressed genes in magnum (actively secreting albumen versus inactive) and 585 in uterus (active eggshell calcification versus quiescent). By combining QTL, transcriptome and proteome data, we obtained high quality gene lists for chicken egg formation. This is the first study to describe the ovary and oviduct transcriptomes by mRNA sequencing, and to elucidate the global repertoire of functional genes involved in egg formation.

Genome-wide association study of bone quality and feed efficiency-related traits in Pekin ducks.

Feeding and bone traits are vital for breeding and reproduction in the commercial duck industry. In this study, we performed a genome-wide association study for feeding and bone traits in a population of 540 lean-type Pekin ducks, followed by genotyping-by-sequencing procedures. The genetic parameters of feeding and bone traits were also estimated using genomic information. In total, seventy-eight significant SNPs were determined, and eleven of them reached the genome-wide significant level for 7 traits except for body weight at 42-day old. A peak of genome-wide significant SNPs was detected on chromosome 2 for feed conversion ratio (P-value = 7.46E-11), and the top SNP (P-value = 2.23E-08) for bone-breaking strength was also obtained in the upstream of gene RAPGEF5. This study provided a list of novel markers and candidate genes associated with feeding and bone traits in Pekin ducks, which could contribute to the genetic selection in duck breeding.

Integrating transcriptome, proteome and QTL data to discover functionally important genes for duck eggshell and albumen formation.

Duck egg quality improvement is an essential target for Asian poultry breeding. In total, 15 RNA-Seq libraries (magnum, isthmus, and uterus at two different physiological states) were sequenced from 48 weeks old Pekin ducks. De novo assembly and annotation methods were utilized to generate new reference transcripts. Our results revealed that 1264 and 2517 genes were differentially expressed in magnum and uterus in the presence versus absence of an egg, respectively. We identified 1089 genes that were differentially expressed in isthmus compared to uterus (in both presence and absence of a calcifying egg). We observed that 11 common DEGs were detected in the egg white proteomes of 6 different bird species including domestic Chicken, Duck, Goose, Turkey, Quail, and Pigeon. On the other hand, only one of the top five most highly expressed genes in duck isthmus was in this category for the chicken isthmus (SPINK7). Among the large number of DEGs during eggshell formation in ducks, only 41 genes showed a similar differential expression pattern in both duck and chicken. By combining chicken QTL database, chicken oviduct transcriptome and egg proteome data for five bird species, we have obtained high-quality gene lists for egg formation. This is the first study to elucidate the transcriptomic changes in different duck oviduct segments during egg formation, and to integrate QTL, proteome and transcriptome data to probe the functional genes associated with albumen secretion and eggshell mineralization.

Genome-wide association study of the level of blood components in Pekin ducks.

Blood components are considered to reflect nutrient metabolism and immune activity in both humans and animals. In this study, we measured 12 blood components in Pekin ducks and performed genome-wide association analysis to identify the QTLs (quantitative trait locus) using a genotyping-by-sequencing strategy. A total of 54 QTLs were identified for blood components. One genome-wide significant QTL for alkaline phosphatase was identified within the intron-region of the OTOG gene (P = 1.31E-07). Moreover, 21 genome-wide significant SNPs for the level of serum cholinesterase were identified on six different scaffolds. In addition, for serum calcium, one genome-wide significant QTL was identified in the upstream region of gene RAB11B. These results provide new markers for functional studies in Pekin ducks, and several candidate genes were identified, which may provide additional insights into specific mechanisms for blood metabolism in ducks and their potential application for duck breeding programs.

iTRAQ-Based Quantitative Proteomic Analysis of Duck Eggshell During Biomineralization.

The avian egg is a valuable model for the calcitic biomineralization process as it is the fastest calcification process occurring in nature and is a clear example of biomineralization. In this study, iTRAQ MS/MS is used to detect and study for the first time: 1) the overall duck eggshell proteome; 2) regional differences in the proteome between the inner and outer portions of the duck eggshell. The new reference protein datasets allow us to identify 179 more eggshell proteins than solely using the current release of Ensembl duck annotations. In total, 484 proteins are identified in the entire duck eggshell proteome. Twenty-eight novel proteins of unknown function that are involved in eggshell formation are also identified. Among the identified eggshell proteins, 54 proteins show differential abundances between the inner, partially mineralized eggshell (obtained 16 h after ovulation) compared to the overall complete eggshell (normally expulsed eggshell). At least 64 of the abundant matrix proteins are common to eggshell of 4 different domesticated bird species (chicken, duck, quail, turkey) and zebra finch. This study provides a new resource for avian eggshell proteomics, and augments the inventory of eggshell matrix proteins that will lead to a deeper understanding of calcitic biomineralization.