Transcriptome analysis reveals that gga-miR-2954 inhibits the inflammatory response against Eimeria tenella infection.

Coccidiosis is an important parasitic protozoan disease in poultry farming, causing huge economic losses in the global poultry industry every year. MicroRNAs (miRNAs) are a class of RNA macromolecules that play important roles in the immune response to pathogens. However, the expression profiles and functions of miRNAs during Eimeria tenella (E. tenella) infection in chickens remain mostly uncharacterized. In this study, high-throughput sequencing of cecal tissues of control (JC), resistant (JR), and susceptible (JS) chickens led to the identification of 35 differentially expressed miRNAs among the three groups. Functional enrichment analysis showed that the differentially expressed miRNAs were mainly associated with the TGF-beta, NF-kB, and Jak-STAT signaling pathways. Notably, gga-miR-2954 was found to be significantly upregulated after coccidial infection. Functional analysis showed that gga-miR-2954 inhibited the production of the inflammatory cytokines IL-6, IL-1ß, TNF-α, and IL-8 in sporozoite-stimulated DF-1 cells. Mechanistically, we found that gga-miR-2954 targeted the RORC gene and that RORC promoted the inflammatory response in sporozoite-stimulated DF-1 cells. In conclusion, our study was the first to identify differentially expressed miRNAs in chicken cecal tissue during E. tenella infection and found that gga-miR-2954 regulates the host immune response to coccidial infection in chickens by targeting the RORC gene.

Identification of metabolite biomarkers in Salmonella enteritidis-contaminated chickens using UHPLC-QTRAP-MS-based targeted metabolomics.

This study aimed to characterize the metabolic profile of Salmonella enteritidis (S. enteritidis) in chicken matrix and to identify metabolic biomarkers of S. enteritidis in chicken. The UHPLC-QTRAP-MS high-throughput targeted metabolomics approach was employed to analyze the metabolic profiles of contaminated and control group chickens. A total of 348 metabolites were quantified, and the application of deep learning least absolute shrinkage and selection operator (LASSO) modelling analysis obtained eight potential metabolite biomarkers for S. enteritidis. Metabolic abundance change analysis revealed significantly enriched abundances of anthranilic acid, l-pyroglutamic acid, 5-hydroxylysine, n,n-dimethylarginine, 4-hydroxybenzoic acid, and menatetrenone in contaminated chicken samples. The receiver operating characteristic (ROC) curve analysis demonstrated the strong ability of these six metabolites as biomarkers to distinguish S. enteritidis contaminated and fresh chicken samples. The findings presented in this study offer a theoretical foundation for developing an innovative approach to identify and detect foodborne contamination caused by S. enteritidis.

Comprehensive analysis of gut microbiome and host transcriptome in chickens after Eimeria tenella infection.

Background: Coccidiosis is an intestinal parasitic disease caused by Eimeria protozoa, which endangers the health and growth of animals, and causes huge economic losses to the poultry industry worldwide every year. Studies have shown that poultry gut microbiota plays an important role in preventing the colonization of pathogens and maintaining the health of the host. Coccidia infection also affects host gene expression. However, the underlying potential relationship between gut microbiome and host transcriptome during E. tenella infection in chickens remain unclear. Methods: In this study, metagenomic and transcriptome sequencing were applied to identify microbiota and genes in cecal contents and cecal tissues of infected (JS) and control (JC) chickens on day 4.5 postinfection (pi), respectively. Results: First, microbial sequencing results of cecal contents showed that the abundance of Lactobacillus, Roseburia sp. and Faecalibacterium sp decreased significantly after E. tenella infection (P < 0.05), while the abundance of Alistipes and Prevotella pectinovora increased significantly (P < 0.05). Second, transcriptome sequencing results showed that a total of 434 differentially expressed mRNAs were identified, including 196 up-regulated and 238 down-regulated genes. These differentially expressed genes related to inflammation and immunity, such as GAMA, FABP1, F2RL1 and RSAD2, may play an important role in the process of host resistance to coccidia infection. Functional studies showed that the enriched pathways of differentially expressed genes included the TGF-beta signaling pathway and the ErbB signaling pathways. Finally, the integrated analysis of gut microbiome and host transcriptome suggested that Prevotella pectinovora associated with FABP1, Butyricicoccus porcorum and Colidextribacter sp. associated with RSAD2 were involved in the immune response upon E. tenella infection. Conclusion: In conclusion, this study provides valuable information on the microbiota and key immune genes after chicken E. tenella infection, with the aim of providing reference for the impact of coccidia infection on cecal microbiome and host.

Qualitative and Quantitative Determination of Decoquinate in Chicken Tissues by Gas Chromatography Tandem Mass Spectrometry.

A novel precolumn derivatization-GC-MS/MS method was developed for the determination of decoquinate residues in chicken tissues (muscle, liver, and kidney). The samples were extracted and purified by liquid-liquid extraction combined with solid-phase extraction and derivatized with acetic anhydride and pyridine. The recovery rates for decoquinate were 77.38~89.65%, and the intra-day and inter-day RSDs were 1.63~5.74% and 2.27~8.06%, respectively. The technique parameters meet the necessities for veterinary drug residue detection in China, the US, and the EU. Finally, the method was applied to analyze tissues of 60 chickens bought from a neighborhood supermarket, and solely one sample of chicken muscle contained 15.6 µg/kg decoquinate residue.

A bidirectional interaction-based hybrid network architecture for EEG cognitive recognition.

BACKGROUND AND OBJECTIVE: Extracting cognitive representation and computational representation information simultaneously from electroencephalography (EEG) data and constructing corresponding information interaction models can effectively improve the recognition capability of brain cognitive status. However, due to the huge gap in the interaction between the two types of information, existing studies have yet to consider the advantages of the interaction of both. METHODS: This paper introduces a novel architecture named the bidirectional interaction-based hybrid network (BIHN) for EEG cognitive recognition. BIHN consists of two networks: a cognitive-based network named CogN (e.g., graph convolution network, GCN; capsule network, CapsNet) and a computing-based network named ComN (e.g., EEGNet). CogN is responsible for extracting cognitive representation features from EEG data, while ComN is responsible for extracting computational representation features. Additionally, a bidirectional distillation-based coadaptation (BDC) algorithm is proposed to facilitate information interaction between CogN and ComN to realize the coadaptation of the two networks through bidirectional closed-loop feedback. RESULTS: Cross-subject cognitive recognition experiments were performed on the Fatigue-Awake EEG dataset (FAAD, 2-class classification) and SEED dataset (3-class classification), and hybrid network pairs of GCN + EEGNet and CapsNet + EEGNet were verified. The proposed method achieved average accuracies of 78.76% (GCN + EEGNet) and 77.58% (CapsNet + EEGNet) on FAAD and 55.38% (GCN + EEGNet) and 55.10% (CapsNet + EEGNet) on SEED, outperforming the hybrid networks without the bidirectional interaction strategy. CONCLUSIONS: Experimental results show that BIHN can achieve superior performance on two EEG datasets and enhance the ability of both CogN and ComN in EEG processing as well as cognitive recognition. We also validated its effectiveness with different hybrid network pairs. The proposed method could greatly promote the development of brain-computer collaborative intelligence.

Untargeted and targeted metabolomics identify metabolite biomarkers for Salmonella enteritidis in chicken meat.

Salmonella Enteritidis easily contaminate chicken during slaughtering, processing, transportation, and sales, which seriously endangers human health. This study aimed to identify metabolite biomarkers for Salmonella Enteritidis contamination in chicken meat. UPLC-Q-Orbitrap MS untargeted metabolomics analysis identified 441 and 240 confidently metabolites in positive and negative ion mode, respectively. Thirty metabolites were defined as potential biomarkers for Salmonella enteritidis contamination in chicken meat. UPLC-QQQ-MS based targeted metabolomics was used to quantitatively analyze candidate metabolite biomarkers in Salmonella enteritidis contaminated and fresh chicken samples. A total of 10 candidate metabolite biomarkers were confirmed in the validation set, among which acetylcholine, l-Methionine, l-Proline, l-Valine, and l-Norleucine were identified as biomarkers for Salmonella Enteritidis contamination in chicken. The combined receiver operating characteristic curve analysis of the five biomarkers achieved an AUC of 0.956, indicating their high sensitivity and specificity in predicting Salmonella Enteritidis in raw chicken. In conclusion, the present study identified five metabolite biomarkers for Salmonella enteritidis in raw chicken. These results provide a potential theoretical basis for developing Salmonella Enteritidis detection methods in raw chicken.

Identification of miRNA-mRNA Networks Associated with Pigeon Skeletal Muscle Development and Growth.

The growth and development of skeletal muscle determine the productivity of pigeon meat production, and miRNA plays an important role in the growth and development of this type of muscle. However, there are few reports regarding miRNA regulating the growth and development of skeletal muscle in pigeons. To explore the function of miRNA in regulating the growth and development of pigeon skeletal muscle, we used RNA sequencing technology to study the transcriptome of pigeons at two embryonic stages (E8 and E13) and two growth stages (D1 and D10). A total of 32,527 mRNAs were identified in pigeon skeletal muscles, including 14,378 novel mRNAs and 18,149 known mRNAs. A total of 2362 miRNAs were identified, including 1758 known miRNAs and 624 novel miRNAs. In total, 839 differentially expressed miRNAs (DEmiRNAs) and 11,311 differentially expressed mRNAs (DEGs) were identified. STEM clustering analysis assigned DEmiRNAs to 20 profiles, of which 7 were significantly enriched (p-value < 0.05). These seven significantly enriched profiles can be classified into two categories. The first category represents DEmiRNAs continuously downregulated from the developmental stage to the growth stage of pigeon skeletal muscle, and the second category represents DEmiRNAs with low expression at the development and early growth stage, and significant upregulation at the high growth stage. We then constructed an miRNA−mRNA network based on target relationships between DEmiRNAs and DEGs belonging to the seven significantly enriched profiles. Based on the connectivity degree, 20 hub miRNAs responsible for pigeon skeletal muscle development and growth were identified, including cli-miR-20b-5p, miR-130-y, cli-miR-106-5p, cli-miR-181b-5p, miR-1-z, cli-miR-1a-3p, miR-23-y, cli-miR-30d-5p, miR-1-y, etc. The hub miRNAs involved in the miRNA−mRNA regulatory networks and their expression patterns during the development and growth of pigeon skeletal muscle were visualized. GO and KEGG enrichment analysis found potential biological processes and pathways related to muscle growth and development. Our findings expand the knowledge of miRNA expression in pigeons and provide a database for further investigation of the miRNA−mRNA regulatory mechanism underlying pigeon skeletal muscle development and growth.

miRNA-10a-5p Targeting the BCL6 Gene Regulates Proliferation, Differentiation and Apoptosis of Chicken Myoblasts.

Proliferation, differentiation, and apoptosis are three essential stages in cell development, and miRNAs can achieve extensive regulation of cellular developmental processes by repressing the expression of target genes. According to our previous RNA-seq results, miRNA-10a-5p was differentially expressed at different periods in chicken myoblasts, revealing a possible association with muscle development. In this study, we concluded that miRNA-10a-5p inhibited chicken myoblasts' proliferation and differentiation and promoted chicken myoblasts' apoptosis by directly targeting BCL6, a critical transcription factor involved in muscle development and regeneration. Overexpression of BCL6 significantly facilitated myoblasts' proliferation and differentiation and suppressed myoblasts' apoptosis. On the contrary, knockdown of BCL6 significantly repressed myoblasts' proliferation and differentiation and induced myoblasts' apoptosis. The results above suggest that miRNA-10a-5p plays a potential role in skeletal muscle growth, development and autophagy by targeting the BCL6 gene. We first revealed the functions of miRNA-10a-5p and BCL6 in the proliferation, differentiation, and apoptosis of chicken myoblasts.

miRNA-seq analysis in skeletal muscle of chicken and function exploration of miR-24-3p.

The regulation of skeletal muscle growth and development in chicken is complex. MicroRNAs (miRNAs) have been found to play an important role in the process, and more research is needed to further understand the regulatory mechanism of miRNAs. In this study, leg muscles of Jinghai yellow chickens at 300 d with low body weight (slow-growing group) and high body weight (fast-growing group) were collected for miRNA sequencing (miRNA-seq) and Bioinformatics analysis revealed 12 differentially expressed miRNAs (DEMs) between the two groups. We predicted 150 target genes for the DEMs, and GO and KEGG pathway analysis showed the target genes of miR-24-3p and novel_miR_133 were most enriched in the terms related to growth and development. Moreover, networks of DEMs and target genes showed that miR-24-3p and novel_miR_133 were the 2 core miRNAs. Hence, miR-24-3p was selected for further functional exploration in chicken primary myoblasts (CPMs) with molecular biology technologies including qPCR, cell counting kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU) and immunofluorescence. When proliferating CPMs were transfected with miR-24-3p mimic, the expression of cyclin dependent kinase inhibitor 1A (P21) was up-regulated and both CCK-8 and EdU assays showed that the proliferation of CPMs was inhibited. However, when the inhibitor was transfected into the proliferating CPMs, the opposite results were found. In differentiated CPMs, transfection with miR-24-3p mimic resulted in up regulation of MYOD, MYOG and MYHC after 48 h. Myotube areas also increased significantly compared to the mimic negative control (NC) group. When treated with inhibitor, differentiation CPMs produced the opposite effects. Overall, we revealed 2 miRNAs (novel_miR_133 and miR-24-3p) significantly related with growth and development and further proved that miR-24-3p could suppress the proliferation and promote differentiation of CPMs. The results would facilitate understanding the effects of miRNAs on the growth and development of chickens at the post-transcriptional level and could also have an important guiding role in yellow-feathered chicken breeding.

Long noncoding RNA profiling reveals that LncRNA BTN3A2 inhibits the host inflammatory response to Eimeria tenella infection in chickens.

Coccidiosis is a widespread parasitic disease that causes serious economic losses to the poultry industry every year. Long noncoding RNAs (lncRNAs) play important roles in transcriptional regulation and are involved in a variety of diseases and immune responses. However, the lncRNAs associated with Eimeria tenella (E. tenella) resistance have not been identified in chickens. In addition, the expression profiles and functions of lncRNAs during E. tenella infection remain unclear. In the present study, high-throughput sequencing was applied to identify lncRNAs in chicken cecal tissues from control (JC), resistant (JR), and susceptible (JS) groups on day 4.5 post-infection (pi), and functional tests were performed. A total of 564 lncRNAs were differentially expressed, including 263 lncRNAs between the JS and JC groups, 192 between the JR and JS groups, and 109 between the JR and JC groups. Functional analyses indicated that these differentially expressed lncRNAs were involved in pathways related to E. tenella infection, including the NF-kappa B signaling, B cell receptor signaling and natural killer cell-mediated cytotoxicity pathways. Moreover, through cis regulation network analysis of the differentially expressed lncRNAs, we found that a novel lncRNA termed lncRNA BTN3A2 was significantly increased in both cecum tissue and DF-1 cells after coccidia infection or sporozoite stimulation. Functional test data showed that the overexpression of lncRNA BTN3A2 reduced the production of inflammatory cytokines, including IL-6, IL-1ß, TNF-α and IL-8, while lncRNA BTN3A2 knockdown promoted the production of these inflammatory cytokines. Taken together, this study identify the differentially expressed lncRNAs during E. tenella infection in chickens for the first time and provide the direct evidence that lncRNA BTN3A2 regulates the host immune response to coccidia infection.

EMCI: A Novel EEG-Based Mental Workload Assessment Index of Mild Cognitive Impairment.

As aging deepens, early detection of mild cognitive impairment (MCI) is increasingly important to prevent Alzheimer Dementia (AD) and improve the quality of life of older adults. In recent years, a large number of studies focus on the abnormal brain cognitive function of MCI, while ignoring the quantitative evaluation of MCI's mental workload. In this study, we propose a workload index for MCI screening, named EMCI, which is a linear discriminant cumulative estimate of subjects' electroencephalography (EEG) power spectra in α and ß rhythms. Then, we design a matched prototype system to verify the effectiveness of EMCI. The results show that the EMCI is sensitive to changes of subjects' mental workload, and is significantly lower in MCI than in HC (Health control), which may be precisely caused by cognitive dysfunction. The proposed EMCI index can be used for online assessment of mental workload in older adults, which can help achieve quick screening of MCI and provide a critical window for clinical treatment interventions.

EEG-Based Index for Timely Detecting User's Drowsiness Occurrence in Automotive Applications.

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic (EEG)-based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so-called "MDrow index", was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements (self-reports).

Qualitative and quantitative determination of tilmicosin in poultry eggs by gas chromatography tandem mass spectrometry after derivatization with acetic anhydride.

A novel GC-MS/MS analytical method was established for the qualitative and quantitative determination of tilmicosin in poultry (Jinghai yellow chicken, Gaoyou duck and Yangzhou goose) eggs. The method was based on LLE and SPE for sample extraction and purification. Pyridine and acetic anhydride were used for the derivatization reaction. When tilmicosin was added to blank poultry egg samples at the LOQ and 75 µg/kg, 150 µg/kg, and 300 µg/kg, the recoveries ranged from 72.80% to 88.75%, the intraday and interday RSDs ranged from 2.31% to 4.56% and 3.29%-5.61%, respectively, and the LODs and LOQs ranged from 3.8 to 5.6 µg/kg and 8.4-10.5 µg/kg, respectively. These results confirmed that the parameters of this novel method meet the requirements of the FAO & WHO (2014) for veterinary drug residue testing. Poultry egg samples purchased from the local market were analysed according to the established method and only one egg sample was found to contain 18.9 µg/kg of tilmicosin.

Integrated Analysis Reveals a lncRNA-miRNA-mRNA Network Associated with Pigeon Skeletal Muscle Development.

Growing evidence has demonstrated the emerging role of long non-coding RNA as competitive endogenous RNA (ceRNA) in regulating skeletal muscle development. However, the mechanism of ceRNA regulated by lncRNA in pigeon skeletal muscle development remains unclear. To reveal the function and regulatory mechanisms of lncRNA, we first analyzed the expression profiles of lncRNA, microRNA (miRNA), and mRNA during the development of pigeon skeletal muscle using high-throughput sequencing. We then constructed a lncRNA-miRNA-mRNA ceRNA network based on differentially expressed (DE) lncRNAs, miRNAs, and mRNAs according to the ceRNA hypothesis. Functional enrichment and short time-series expression miner (STEM) analysis were performed to explore the function of the ceRNA network. Hub lncRNA-miRNA-mRNA interactions were identified by connectivity degree and validated using dual-luciferase activity assay. The results showed that a total of 1625 DE lncRNAs, 11,311 DE mRNAs, and 573 DE miRNAs were identified. A ceRNA network containing 9120 lncRNA-miRNA-mRNA interactions was constructed. STEM analysis indicated that the function of the lncRNA-associated ceRNA network might be developmental specific. Functional enrichment analysis identified potential pathways regulating pigeon skeletal muscle development, such as cell cycle and MAPK signaling. Based on the connectivity degree, lncRNAs TCONS_00066712, TCONS_00026594, TCONS_00001557, TCONS_00001553, and TCONS_00003307 were identified as hub genes in the ceRNA network. lncRNA TCONS_00026594 might regulate the FSHD region gene 1 (FRG1)/ SRC proto-oncogene, non-receptor tyrosine kinase (SRC) by sponge adsorption of cli-miR-1a-3p to affect the development of pigeon skeletal muscle. Our findings provide a data basis for in-depth elucidation of the lncRNA-associated ceRNA mechanism underlying pigeon skeletal muscle development.

Label-Based Alignment Multi-Source Domain Adaptation for Cross-Subject EEG Fatigue Mental State Evaluation.

Accurate detection of driving fatigue is helpful in significantly reducing the rate of road traffic accidents. Electroencephalogram (EEG) based methods are proven to be efficient to evaluate mental fatigue. Due to its high non-linearity, as well as significant individual differences, how to perform EEG fatigue mental state evaluation across different subjects still keeps challenging. In this study, we propose a Label-based Alignment Multi-Source Domain Adaptation (LA-MSDA) for cross-subject EEG fatigue mental state evaluation. Specifically, LA-MSDA considers the local feature distributions of relevant labels between different domains, which efficiently eliminates the negative impact of significant individual differences by aligning label-based feature distributions. In addition, the strategy of global optimization is introduced to address the classifier confusion decision boundary issues and improve the generalization ability of LA-MSDA. Experimental results show LA-MSDA can achieve remarkable results on EEG-based fatigue mental state evaluation across subjects, which is expected to have wide application prospects in practical brain-computer interaction (BCI), such as online monitoring of driver fatigue, or assisting in the development of on-board safety systems.

Simultaneous Determination of Tetracyclines and Fluoroquinolones in Poultry Eggs by UPLC Integrated with Dual-Channel-Fluorescence Detection Method.

An innovative, rapid and stable method for simultaneous determination of three tetracycline (oxytetracycline, tetracycline and doxycycline) and two fluoroquinolone (ciprofloxacin and enrofloxacin) residues in poultry eggs by ultra-high performance liquid chromatography-fluorescence detection (UPLC-FLD) was established and optimized. The samples were homogenized and extracted with acetonitrile/ultrapure water (90:10, v/v) and then purified by solid-phase extraction (SPE). LC separation was achieved on an ACQUITY UPLC BEH C18 column (1.7 µm, 2.1 mm × 100 mm), and the mobile phase was composed of acetonitrile and a 0.1 mol/L malonic acid solution containing 50 mmol/L magnesium chloride (the pH was adjusted to 5.5 with ammonia). When the five target drugs were spiked at the limit of quantification, 0.5 times the maximum residue limit (MRL), 1.0 MRL and 2.0 MRL, the recoveries were above 83.5% and the precision ranged from 1.99% to 6.24%. These figures of merit complied with the parameter validation regulations of the EU and U.S. FDA. The limits of detection and quantifications of the targets were 0.1-13.4 µg/kg and 0.3-40.1 µg/kg, respectively. The proposed method was easily extended to quantitative analyses of target drug residues in 85 egg samples, thus demonstrating its reliability and applicability.

Antibiotic-Induced Dysbiosis of Microbiota Promotes Chicken Lipogenesis by Altering Metabolomics in the Cecum.

Elucidation of the mechanism of lipogenesis and fat deposition is essential for controlling excessive fat deposition in chicken. Studies have shown that gut microbiota plays an important role in regulating host lipogenesis and lipid metabolism. However, the function of gut microbiota in the lipogenesis of chicken and their relevant mechanisms are poorly understood. In the present study, the gut microbiota of chicken was depleted by oral antibiotics. Changes in cecal microbiota and metabolomics were detected by 16S rRNA sequencing and ultra-high performance liquid chromatography coupled with MS/MS (UHPLC-MS/MS) analysis. The correlation between antibiotic-induced dysbiosis of gut microbiota and metabolites and lipogenesis were analysed. We found that oral antibiotics significantly promoted the lipogenesis of chicken. 16S rRNA sequencing indicated that oral antibiotics significantly reduced the diversity and richness and caused dysbiosis of gut microbiota. Specifically, the abundance of Proteobacteria was increased considerably while the abundances of Bacteroidetes and Firmicutes were significantly decreased. At the genus level, the abundances of genera Escherichia-Shigella and Klebsiella were significantly increased while the abundances of 12 genera were significantly decreased, including Bacteroides. UHPLC-MS/MS analysis showed that antibiotic-induced dysbiosis of gut microbiota significantly altered cecal metabolomics and caused declines in abundance of 799 metabolites and increases in abundance of 945 metabolites. Microbiota-metabolite network revealed significant correlations between 4 differential phyla and 244 differential metabolites as well as 15 differential genera and 304 differential metabolites. Three metabolites of l-glutamic acid, pantothenate acid and N-acetyl-l-aspartic acid were identified as potential metabolites that link gut microbiota and lipogenesis in chicken. In conclusion, our results showed that antibiotic-induced dysbiosis of gut microbiota promotes lipogenesis of chicken by altering relevant metabolomics. The efforts in this study laid a basis for further study of the mechanisms that gut microbiota regulates lipogenesis and fat deposition of chicken.

Transcriptome Analysis of Differentially Expressed mRNA Related to Pigeon Muscle Development.

The mechanisms behind the gene expression and regulation that modulate the development and growth of pigeon skeletal muscle remain largely unknown. In this study, we performed gene expression analysis on skeletal muscle samples at different developmental and growth stages using RNA sequencing (RNA-Seq). The differentially expressed genes (DEGs) were identified using edgeR software. Weighted gene co-expression network analysis (WGCNA) was used to identify the gene modules related to the growth and development of pigeon skeletal muscle based on DEGs. A total of 11,311 DEGs were identified. WGCNA aggregated 11,311 DEGs into 12 modules. Black and brown modules were significantly correlated with the 1st and 10th day of skeletal muscle growth, while turquoise and cyan modules were significantly correlated with the 8th and 13th days of skeletal muscle embryonic development. Four mRNA-mRNA regulatory networks corresponding to the four significant modules were constructed and visualised using Cytoscape software. Twenty candidate mRNAs were identified based on their connectivity degrees in the networks, including Abca8b, TCONS-00004461, VWF, OGDH, TGIF1, DKK3, Gfpt1 and RFC5, etc. A KEGG pathway enrichment analysis showed that many pathways were related to the growth and development of pigeon skeletal muscle, including PI3K/AKT/mTOR, AMPK, FAK, and thyroid hormone pathways. Five differentially expressed genes (LAST2, MYPN, DKK3, B4GALT6 and OGDH) in the network were selected, and their expression patterns were quantified by qRT-PCR. The results were consistent with our sequencing results. These findings could enhance our understanding of the gene expression and regulation in the development and growth of pigeon muscle.

Characterization of chilled chicken spoilage using an integrated microbiome and metabolomics analysis.

Spoilage of chilled chicken can occur as a result of microbial development and consumption of meat nutrients by spoilage bacteria, ultimately resulting in the release of undesired metabolites. Characterizing the profiles of the microbiota and metabolites and clarifying their relationships will contribute to an improved understanding of the mechanism underlying chilled chicken spoilage. In the present study, 16S rRNA gene sequencing and ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS)-based untargeted metabolomics analyses were applied to determine the microbial and metabolic profiles in chicken during chilled storage. The microbial and metabolic datasets were subjected to combined analysis using weighted gene co-expression network analysis (WGCNA) and Spearman's correlation analysis. Brochothrix, Carnobacterium, Photobacterium, Pseudomonas, Acinetobacter, Serratia, Kurthia, Shewanella, and Obesumbacterium genera were identified as the dominant spoilage bacteria in chilled chicken. Ten metabolic pathways, including histidine metabolism and purine metabolism, were identified as potential mechanisms underlying chilled chicken spoilage. Correlation analysis demonstrated that spoilage bacterial genera were highly correlated with spoilage-related metabolites. Taken together, the present study proposed an integrated microbiome and metabolomics approach to investigate the mechanism of chilled chicken spoilage caused by microbial activity. The results obtained by this approach provide a comprehensive insight into changes in the microbial and metabolic profiles of chilled chicken during spoilage.

Research Note: Expression of T cell-related cytokines in chicken cecal and spleen tissues following Eimeria tenella infection in vivo.

The T cell-mediated immune response plays an important role in coccidiosis. To reveal the host T cell immune response following Eimeria tenella (E. tenella) infection in chickens, we performed quantitative real-time PCR to analyze the dynamic expression of the Th1-related cytokines IFN-γ, IL-2, and IL-12; the Th17-related cytokines IL-17A, IL-17F, and IL-22; and the Treg-related cytokines IL-10, TGF-ß, and CTLA-4 in the cecum and spleen at 0, 2, 4, 6, 8, and 10 d postinfection (dpi). In the cecal tissue, the expression of the Th1-related cytokine IFN-γ was significantly higher at 6 and 8 dpi than at other time points (11.97-fold and 39.78-fold, respectively, compared with 0 dpi, P < 0.05). IL-2 and IL-12 expression was significantly higher at 6 and 8 dpi than at 0, 2 and 10 dpi (P < 0.05). The expression of the Th17-related cytokines IL-17A and IL-17F at 2 and 4 dpi and IL-22 expression at 4 dpi were significantly higher than those at 0, 6, 8 and 10 dpi (P < 0.05). The expression of the Treg-related cytokines IL-10, TGF-ß and CTLA-4 was significantly higher at 6 and 8 dpi than at 0, 2 and 4 dpi (P < 0.05). In the spleen, IFN-γ and IL-12 expression peaked at 4 dpi, while IL-2 expression peaked at 10 dpi. IL-17A, IL-17F and IL-22 expression was significantly higher at 2 and 4 dpi than at 0, 6, 8 and 10 dpi (P < 0.05). Treg-related cytokine TGF-ß expression was almost unchanged and significantly decreased at only 4 dpi (P < 0.05), while CTLA-4 expression showed an overall decreasing trend from 0 to 8 dpi but increased significantly at 10 dpi (P < 0.05). The expression patterns of three T cell subset-related cytokines were different in the cecum and spleen. Furthermore, Th1 and Treg cells participate in the immune response mainly in the latter stage of coccidia infection (6 and 8 dpi), while Th17 cells play a role mainly in the early stages of infection (2 and 4 dpi). Our data will help to deepen the understanding of the complex T cell immune response after coccidia infection.