Host-microbiota interactions describe a co-evolution and mutualistic symbiosis. Gut microbial communities are important for diverse host functions. H

In this study, we used young broilers to investigate the relationship between their gut microbiota and bursal B cell development. We characterized the important variables, microbes, B cells, and immunoglobulins during the posthatch development of birds.

The sex detection of chicks is an important work in poultry breeding. Separating chicks of different sexes early can effectively improve production efficiency and commercial benefits. In this paper, based on the difference in calls among one-day-old chicks of different sexes, a sex detection method based on chick calls is designed. Deep learning methods were used to classify the calls of chicks and detect their sex. This experiment studies three different varieties of chicks. The short-time zero-crossing rate was used to automatically detect the endpoints of chick calls in audio. Three kinds of audio features were compared: Spectrogram, Cepstrogram and MFCC+Logfbank. The features were used as the input in neural networks, and there were five kinds of neural networks: CNN, GRU, CRNN, TwoStream and ResNet-50. After the cross-comparison experiment of different varieties of chicks, audio features and neural networks, the ResNet-50 neural network trained with the MFCC+Logfbank audio features of three yellow chick calls had the highest test accuracy of 83% when testing Three-yellow chicks’ calls. The GRU neural network trained with the Spectrogram audio features of native chick calls had the highest test accuracy of 76.8% when testing Native chicks’ calls. The ResNet-50 neural network trained with Spectrogram audio features of flaxen-yellow chick calls had the highest test accuracy of 66.56%when testing flaxen-yellow chick calls. Multiple calls of each chick were detected, and the majority voting method was used to detect the sex of the chicks. The ResNet-50 neural network trained with the Spectrogram of three yellow chick calls had the highest sex detection accuracy of 95% when detecting the three yellow chicks’ sex. The GRU neural network trained with the Spectrogram and cepstrogram of native chick calls and the CRNN network trained with the Spectrogram of native chick calls had the highest sex detection accuracy of 90% when detecting the native chicks’ sex. The Twostream neural network trained with MFCC+Logfbank of flaxen-yellow chick calls and the ResNet-50 network trained with the Spectrogram of flaxen-yellow chick calls had the highest sex detection accuracy of 80% when detecting the flaxen-yellow chicks’ sex. The results of the cross-comparison experiment show that there is a large diversity between the sex differences in chick calls of different breeds. The method is more applicable to chick sex detection in three yellow chicks and less so in native chicks and flaxen-yellow chicks. Additionally, when detecting the sex of chicks of a similar breed to the training chicks, the method obtained better results, while detecting the sex of chicks of other breeds, the detection accuracy was significantly reduced. This paper provides further perspectives on the sex detection method of chicks based on their calls and help and guidance for future research.

The dominance effect is considered to be a key factor affecting complex traits. However, previous studies have shown that the improvement of the model, including the dominance effect, is usually less than 1%. This study proposes a novel genomic prediction method called CADM, which combines additive and dominance genetic effects through locus-specific weights on heterozygous genotypes. To the best of our knowledge, this is the first study of weighting dominance effects for genomic prediction. This method was applied to the analysis of chicken (511 birds) and pig (3534 animals) datasets. A 5-fold cross-validation method was used to evaluate the genomic predictive ability. The CADM model was compared with typical models considering additive and dominance genetic effects (ADM) and the model considering only additive genetic effects (AM). Based on the chicken data, using the CADM model, the genomic predictive abilities were improved for all three traits (body weight at 12th week, eviscerating percentage, and breast muscle percentage), and the average improvement in prediction accuracy was 27.1% compared with the AM model, while the ADM model was not better than the AM model. Based on the pig data, the CADM model increased the genomic predictive ability for all the three pig traits (trait names are masked, here designated as T1, T2, and T3), with an average increase of 26.3%, and the ADM model did not improve, or even slightly decreased, compared with the AM model. The results indicate that dominant genetic variation is one of the important sources of phenotypic variation, and the novel prediction model significantly improves the accuracy of genomic prediction.

Salmonella enterica is a food-borne pathogen that poses a severe threat to both poultry production and human health. Antibiotics are critical for the initial treatment of bacterial infections. However, the overuse and misuse of antibiotics results in the rapid evolution of antibiotic-resistant bacteria, and the discovery and development of new antibiotics are declining. Therefore, understanding antibiotic resistance mechanisms and developing novel control measures are essential. In the present study, GC-MS-based metabolomics analysis was performed to determine the metabolic profile of gentamicin sensitive (SE-S) and resistant (SE-R) S. enterica. Fructose was identified as a crucial biomarker. Further analysis demonstrated a global depressed central carbon metabolism and energy metabolism in SE-R. The decrease in the pyruvate cycle reduces the production of NADH and ATP, causing a decrease in membrane potential, which contributes to gentamicin resistance. Exogenous fructose potentiated the effectiveness of gentamicin in killing SE-R by promoting the pyruvate cycle, NADH, ATP and membrane potential, thereby increasing gentamicin intake. Further, fructose plus gentamicin improved the survival rate of chicken infected with gentamicin-resistant Salmonella in vivo. Given that metabolite structures are conserved across species, fructose identified from bacteria could be used as a biomarker for breeding disease-resistant phenotypes in chicken. Therefore, a novel strategy is proposed for fighting against antibiotic-resistant S. enterica, including exploring molecules suppressed by antibiotics and providing a new approach to find pathogen targets for disease resistance in chicken breeding.

Carotenoid consumption decreases the risk of cancer, osteoporosis, or neurodegenerative diseases through interrupting the formation of free radicals. The deposition of carotenoids in chicken skin makes the skin color turn from white into yellow. The enzyme β-carotene oxygenase 2 (BCO2) plays a key role during the degradation process of carotenoids in skin. How the BCO2 affects the skin color of the chicken and whether it is the key factor that results in the phenotypic difference between yellow- and white-skin chickens are still unclear. In this research, the measurement of the concentration of carotenoids in chicken skin by HPLC showed that the carotenoid concentration in chickens with a yellow skin was significantly higher than that in white-skin chickens. Moreover, there were significant differences in BCO2 gene expression in the back skin between yellow- and white-skin chickens. Scanning the SNPs in BCO2 gene revealed a G/A mutation in exon 6 of the BCO2 gene in white and yellow skin chicken. Generally, one SNP c.890A>G was found to be associated with the chicken skin color and may be used as a genetic marker in breeding for yellow skin in Chinese indigenous chickens.