Estimation of Growth Curves and Suitable Slaughter Weight of the Liangshan Pig

Using multiomics to explore the weight differences between genders in Muscovy ducks

Sexual dimorphism in poultry, especially in Muscovy ducks, is a proven phenomenon characterized by significant differences in body weight, growth patterns, and gene expression between male and female individuals. However, there is a dearth of research on the candidate genes and mechanisms underlying these weight differences. We selected 301 Muscovy ducks and recorded their weekly body weights from birth. We utilized 3 non-linear growth models (Logistic, Bertalanffy, and Gompertz) to fit the growth curve of Muscovy ducks, it was found that the logistic model was the most suitable model for describing the growth curve of Muscovy ducks. The results from the logistic model showed that the inflection point of male Muscovy ducks occurred at a later age, and they had a heavier mature body weight than female Muscovy ducks. At 10 wk of age, we collected Muscovy duck breast muscle tissues for transcriptome sequencing (RNA-seq). To exclude the impact of weight difference, 185 differentially expressed genes (DEGs), such as PPAR, FABP3, PLIN1, and FOXO1, were screened. These DEGs were predominantly enriched in terms related to mitochondria, lipids, and nucleic acids. In addition, the gut microbiota has the ability to influence host physiology through the regulation of multiple processes, including playing a crucial role in host muscle growth and development. We randomly selected male and female Muscovy ducks for 16S rRNA sequencing analysis of their cecal microbiota. The results showed that there were significant differences in the composition of cecal microbiota between male and female Muscovy ducks. At the genus level, the relative abundance of Enterenecus and CAG_269 were lower in males compared to females, while Lawsonibacter, Parabacteroides_B, Streptococcus, UBA2658, Caccousia, and Butyricimonas were higher in males than in females. In summary, this study provides a scientific theoretical basis for revealing the different growth patterns of male and female Muscovy ducks, and offers explanations from both the molecular level and microbiological perspectives.

Improving Efficiency: Automatic Intelligent Weighing System as a Replacement for Manual Pig Weighing

To verify the accuracy of AIWS, we weighed 106 pen growing-finishing pigs' weights using both the manual and AIWS methods, respectively. Accuracy was evaluated based on the values of MAE, MAPE, and RMSE. In the growth experiment, manual weighing was conducted every two weeks and AIWS predicted weight data was recorded daily, followed by fitting the growth curves. The results showed that MAE, MAPE, and RMSE values for 60 to 120 kg pigs were 3.48 kg, 3.71%, and 4.43 kg, respectively. The correlation coefficient r between the AIWS and manual method was 0.9410, and R2 was 0.8854. The two were extremely significant correlations (p < 0.001). In growth curve fitting, the AIWS method has lower AIC and BIC values than the manual method. The Logistic model by AIWS was the best-fit model. The age and body weight at the inflection point of the best-fit model were 164.46 d and 93.45 kg, respectively. The maximum growth rate was 831.66 g/d. In summary, AIWS can accurately predict pigs' body weights in actual production and has a better fitting effect on the growth curves of growing-finishing pigs. This study suggested that it was feasible for AIWS to replace manual weighing to measure the weight of 50 to 120 kg live pigs in large-scale farming.

Growth charts for small sample sizes using unsupervised clustering: Application to canine early growth

Breed-specific growth curves (GCs) are needed for neonatal puppies, but breed-specific data may be insufficient. We investigated an unsupervised clustering methodology for modeling GCs by augmenting breed-specific data with data from breeds having similar growth profiles. Puppy breeds were grouped by median growth profiles (bodyweights between birth and Day 20) using hierarchical clustering on principal components. Median bodyweights for breeds in a cluster were centered to that cluster’s median and used to model cluster GCs by Generalized Additive Models for Location, Shape and Scale. These were centered back to breed growth profiles to produce cluster-scale breed GCs. The accuracy of breed-scale GCs modeled with breed-specific data only and cluster-scale breed GCs were compared when modeled from diminishing sample sizes. A complete dataset of Labrador Retriever bodyweights (birth to Day 20) was split into training (410 puppies) and test (460 puppies) datasets. Cluster-scale breed and breed-scale GCs were modelled from defined sample sizes from the training dataset. Quality criteria were the percentages of observed data in the test dataset outside the target growth centiles of simulations. Accuracy of cluster-scale breed GCs remained consistently high down to sampling sizes of three. They slightly overestimated breed variability, but centile curves were smooth and consistent with breed-scale GCs modeled from the complete Labrador Retriever dataset. At sampling sizes ≤ 20, the quality of breed-scale GCs reduced notably. In conclusion, GCs for neonatal puppies generated using a breed-cluster hybrid methodology can be more satisfactory than GCs at purely the breed level when sample sizes are small.

Location matters: Offset in tissue-engineered vascular graft implantation location affects wall shear stress in porcine models

Objective

Although surgical simulation using computational fluid dynamics has advanced, little is known about the accuracy of cardiac surgical procedures after patient-specific design. We evaluated the effects of discrepancies in location for patient-specific simulation and actual implantation on hemodynamic performance of patient-specific tissue-engineered vascular grafts (TEVGs) in porcine models.

Methods

Magnetic resonance angiography and 4-dimensional (4D) flow data were acquired in porcine models (n = 11) to create individualized TEVGs. Graft shapes were optimized and manufactured by electrospinning bioresorbable material onto a metal mandrel. TEVGs were implanted 1 or 3 months postimaging, and postoperative magnetic resonance angiography and 4D flow data were obtained and segmented. Displacement between intended and observed TEVG position was determined through center of mass analysis. Hemodynamic data were obtained from 4D flow analysis. Displacement and hemodynamic data were compared using linear regression.

Results

Patient-specific TEVGs were displaced between 1 and 8 mm during implantation compared with their surgically simulated, intended locations. Greater offset between intended and observed position correlated with greater wall shear stress (WSS) in postoperative vasculature (P < .01). Grafts that were implanted closer to their intended locations showed decreased WSS.

Conclusions

Patient-specific TEVGs are designed for precise locations to help optimize hemodynamic performance. However, if TEVGs were implanted far from their intended location, worse WSS was observed. This underscores the importance of not only patient-specific design but also precision-guided implantation to optimize hemodynamics in cardiac surgery and increase reproducibility of surgical simulation.

Estimation of body weight in captive Amur tigers (Panthera tigris altaica)

So far, there has been no safe and convenient method to weigh the large fierce animals, like Amur tigers. To address this problem, we built models to predict the body weight of Amur tigers based on the fact that body weight is proportional to body measurements or age. Using the method of body measurements, we extracted the body measurements from four different kinds of the lateral body image of tigers, i.e., total lateral image, central lateral image, ellipse fitting image and rectangle fitting image, and then we respectively used artificial neural network (ANN) and power regression model to analyze the predictive relationships between body weight and body measurements. Our results demonstrated that, among all ANN models, the model built with rectangle fitting image had the smallest mean square error. Comparatively, we screened power regression models which had the smallest Akakai information criteria (AIC). In addition, using the method of age, we fitted nonlinear regression models for the relationship between body weight and age and found that, for male tigers, logistic model had the smallest AIC. For female tigers, Gompertz model had the smallest AIC. Consequently, this study could be applied to estimate body weight of captive, or even wild, Amur tigers safely and conveniently, helping to monitor individual health and growth of the Amur tiger populations. This article is protected by copyright. All rights reserved.

Profiling and Functional Analysis of Long Noncoding RNAs and mRNAs during Porcine Skeletal Muscle Development

Gene transcripts or mRNAs and long noncoding RNAs (lncRNAs) are differentially expressed during porcine skeletal muscle development. However, only a few studies have been conducted on skeletal muscle transcriptome in pigs based on timepoints according to the growth curve for porcine. Here, we investigated gene expression in Qingyu pigs at three different growth stages: the inflection point with the maximum growth rate (MGI), the inflection point of the gradually increasing stage to the rapidly increasing stage (GRI), and the inflection point of the rapidly increasing stage to the slowly increasing stage (RSI). Subsequently, we explored gene expression profiles during muscle development at the MGI, GRI and RSI stages by Ribo-Zero RNA sequencing. Qingyu pigs reached the MGI, GRI and RSI stages at 156.40, 23.82 and 288.97 days of age with 51.73, 3.14 and 107.03 kg body weight, respectively. A total of 14,530 mRNAs and 11,970 lncRNAs were identified at the three stages, and 645, 323 differentially expressed genes (DEGs) and 696, 760 differentially expressed lncRNAs (DELs) were identified in the GRI vs. MGI, and RSI vs. MGI, comparisons. Functional enrichment analysis revealed that genes involved in immune system development and energy metabolism (mainly relate to amino acid, carbohydrate and lipid) were enriched at the GRI and MGI stages, respectively, whereas genes involved in lipid metabolism were enriched at the RSI stage. We further characterized G1430, an abundant lncRNA. The full-length sequence (316 nt) of lncRNA G1430 was determined by rapid amplification of cDNA ends (RACE). Subcellular distribution analysis by quantitative real-time PCR (qRT-PCR) revealed that G1430 is a cytoplasmic lncRNA. Binding site prediction and dual luciferase assay showed that lncRNA G1430 directly binds to microRNA 133a (miR-133a). Our findings provide the basis for further investigation of the regulatory mechanisms and molecular genetics of muscle development in pigs.

Profiling of skeletal muscle tissue for long non-coding RNAs related to muscle metabolism in the QingYu pig at the growth inflection point

OBJECTIVE

Investigation of muscle growth at different developmental stages is an appropriate strategy for studying the mechanisms underlying muscle development and differences in phenotypes. In particular, the muscle development mechanisms and the difference between the fastest and slowest growth rates.

METHODS

In this study, we used a growth curve model to fit the growth inflection point (IP) of QingYu pigs and compared differences in the long non-coding RNA (lncRNA) transcriptome of muscle both at the growth IP and plateau phase (PP).

RESULTS

The growth curve of the QingYu pig had a good fit (R2 = 0.974) relative to a typical S-curve and reached the IP at day 177.96. At the PP, marbling, intramuscular fat, and monounsaturated fatty acids had increased significantly and the percentage of lean muscle and polyunsaturated fatty acids had decreased. A total of 1,199 mRNAs and 62 lncRNAs were differentially expressed at the IP compared with the PP. Additional to gene ontology and Kyoto encyclopedia of genes and genomes pathway analyses, these differentially expressed protein coding genes were principally related to muscle growth and lipid metabolism.

CONCLUSION

Our results suggest that the identified differentially expressed lncRNAs, could play roles in muscle growth, fat deposition and regulation of fatty acid composition at the IP and PP.

Single nucleotide polymorphism-based analysis of the genetic structure of Liangshan pig population

Objective

To conserve and utilize the genetic resources of a traditional Chinese indigenous pig breed, Liangshan pig, we assessed the genetic diversity, genetic structure, and genetic distance in this study.

Methods

We used 50K single nucleotide polymorphism (SNP) chip for SNP detection of 139 individuals in the Liangshan Pig Conservation Farm.

Results

The genetically closed conserved population consisted of five overlapping generations, and the total effective content of the population (Ne) was 15. The whole population was divided into five boar families and one non-boar family. Among them, the effective size of each generation subpopulation continuously decreased. However, the proportion of polymorphic markers (P_N) first decreased and then increased. The average genetic distance of these 139 Liangshan pigs was 0.2823±0.0259, and the average genetic distance of the 14 boars was 0.2723±0.0384. Thus, it can be deduced that the genetic distance changed from generation to generation. In the conserved population, 983 runs of homozygosity (ROH) were detected, and the majority of ROH (80%) were within 100 Mb. The inbreeding coefficient calculated based on ROH showed an average value of 0.026 for the whole population. In addition, the inbreeding coefficient of each generation subpopulation initially increased and then decreased. In the pedigree of the whole conserved population, the error rate of paternal information was more than 11.35% while the maternal information was more than 2.13%.

Conclusion

This molecular study of the population genetic structure of Liangshan pig showed loss of genetic diversity during the closed cross-generation reproduction process. It is necessary to improve the mating plan or introduce new outside blood to ensure long-term preservation of Liangshan pig.

Meat Quality, Amino Acid, and Fatty Acid Composition of Liangshan Pigs at Different Weights

Simple Summary

The research on the quality of traditional pork can not only provide a reference for the thorough breeding and food development of pigs, but also make a reference for understanding the local history and social culture. The Liangshan pig is a traditional Chinese miniature pig breed. It is mainly raised in the Liangshan Yi area and is closely related to the dietary culture of the local people. The characteristics of, and changes in, the meat quality, amino acid composition and fatty acid composition of Liangshan pigs of different weights were revealed for the first time in this paper. It was found that as the weight of Liangshan pigs increased, the contents of marbling score, intramuscular fat, shear force, Met, Asp, Asn, C18: 0 and C20: 2 increased, and drip loss, Trp and C22: 6 decreased. Taken together, our findings serve as a reference for the development of the local Liangshan pig industry.

Abstract

Indigenous pig breeds are important biological resources and their diversity has been severely damaged. The Liangshan pig is a typical mountain-type local pig breed in southwest China. Here, the meat quality, amino acid, and fatty acid composition of Liangshan pigs were compared at seven stages within the weight range of 50–90 kg. A score for comprehensive factors of meat quality was maintained after rising and kept in a plateau within 74.9–91.5 kg of body weight. The total amount of amino acids in the longissimus dorsi muscle remained stable, and the total fatty acids showed an upward trend. Amino acid composition analysis revealed that as the body weight of Liangshan pigs increased, umami, basic, and acidic amino acid contents decreased, while the essential amino acids (EAA) content and the ratio of basic amino acids to acidic amino acids increased. Fatty acid composition analysis revealed that as body weight increased, the content of polyunsaturated fatty acids (PUFA) exhibited a downward trend, while the content of saturated fatty acids (SFA) exhibited an upward trend. This study is a primary step towards the development and utilization of Liangshan pigs and provides useful information for local pork processing and genetic improvement.

Using quantile regression methodology to evaluate changes in the shape of growth curves in pigs selected for increased feed efficiency based on residual feed intake

Growth rate is a major component of feed efficiency when estimating residual feed intake (RFI). Quantile regression (QR) methodology can be used to identify animals with different growth trajectories. The objective of this study was to evaluate the use of QR to identify phenotypic and genetic differences in pigs selected for low RFI. Using performance data on 750 Yorkshire pigs selected for low RFI, individual average daily gain (ADG), average daily feed intake (ADFI), RFI and Gompertz growth curve parameters (asymptotic weight (a), inflection point (b) and decay parameter (c)) were estimated for each pig. Using QR methodology, three Gompertz growth curves were estimated for the whole population for three quantiles (0.1, 0.5 and 0.9) of the BW data. Each animal was classified into one of the quantile regression groups (QRG) based on their overall Euclidian distance between each observed and estimated BW from the quantile growth curves. These three curves were also estimated using only part of the data (generations -1 to 3, and -1 to 4) in order to evaluate the agreement classification rate of animals from later generations into QRGs. We evaluated the effect of QRG on growth parameters and performance traits. Genetic parameters were estimated for these traits, as well as for QRG. In addition, genetic trends for each QRG were estimated. Three distinct growth curves were observed for animals classified into either quantiles 0.1 (QRG0.1), 0.5 (QRG0.5) or 0.9 (QRG0.9). When only part of the data was used to estimate quantile growth curves, all animals from QRG0.1 were correctly classified in their group. Animals in QRG0.1 had significantly lower ADFI, ADG and RFI, and greater a, b and c than animals in the other groups. Quantile regression groups analysed as a trait was highly heritable (0.41) and had high (0.8) and moderate (0.46) genetic correlations with ADG and RFI, respectively. Selection for reduced RFI increased the number of animals classified as QRG0.1 in the population. Overall, downward genetic trends were observed for all traits as a function of selection for reduced RFI. However, QRG0.1 was the only group that had a positive genetic trend for ADG. Altogether, these results indicate that selection for reduced RFI changes the shape of growth curves in Yorkshire in pigs, and that QR methodology was able to identify animals having different genetic potential for feed efficiency, bringing a new opportunity to improve selection for reduced RFI.