Comparative transcriptomic analysis of the hypothalamus between tumbler and white king pigeons

Abstract

Tumbler pigeons (Columba livia) were shaped by long-term artificial selection, and their superior flight performance is closely linked to neural regulatory mechanisms. However, the molecular bases of neural regulation—particularly at the hypothalamic transcriptomic level—remain insufficiently characterized. Here, we conducted neurochemical and whole-transcriptome comparisons of the hypothalamus (HYP) in tumbler pigeons (FF) and meat-type White King pigeons (BY), analyzing neurotransmitters and the transcriptome, including mRNA, long non-coding RNA (lncRNA), microRNA (miRNA), and circular RNA (circRNA). Neurotransmitter quantitation revealed that γ-aminobutyric acid (GABA) levels in FF HYP were significantly higher than those in BY. Transcriptome analysis identified 514 differentially expressed mRNAs, 317 differentially expressed lncRNAs, 49 differentially expressed miRNAs and 304 differentially expressed circRNAs. Functional enrichment showed that differentially expressed genes (DEGs) were significantly overrepresented in metabolic pathways, cytokine-cytokine receptor interactions and TGF-β signaling. Differential expression changes in PDK4, PCK1, and POMC reveal complex molecular mechanisms during flight in tumbler pigeons, characterized by increased energy dependence on fatty acids, inhibition of gluconeogenesis, and enhanced stress response. In this study, we systematically elucidated the molecular regulatory mechanisms of the pigeon hypothalamus (HYP) controlling energy metabolism, neural excitability, and stress response through neurochemical and transcriptomic analyses. It provides a theoretical basis for the neurogenetic basis of behavioral adaptation in birds and for the conservation and selective breeding of local pigeon genetic resources.

Introduction

Pigeons (Columba livia) have been selectively bred for various purposes, such as consumption, ornamental display, and competitive events, leading to significant diversity in their physical traits and behaviors. Weight variations among different breeds are substantial, ranging from nearly 250 grams to nearly four times that weight (Entrikin and Bryant, 1974; Lan et al., 2025; Shapiro et al., 2013; Stringham et al., 2012). Meat pigeons, such as the White King pigeon, are characterized by rapid growth and well-developed breast muscles. Racing pigeons and ornamental pigeons, such as homing pigeons, tumbling pigeons, and high-flying pigeons, emphasize endurance flight or aerial acrobatics. Carrier pigeons are renowned for their ability to navigate long distances back to their home base, while tumbling pigeons are celebrated for their continuous aerial somersaults (Chang et al., 2023). The differences in these behavioral traits extend beyond flight patterns alone, being closely linked to muscle tissue development, energy metabolism, and neural regulation (Hou et al., 2022; Mehlhorn and Rehkaemper, 2017; Ye et al., 2018). Comparative genomics and transcriptomics studies confirm that pigeon breeds with different breeding objectives exhibit distinct selection patterns and transcriptional expression profiles for muscle-related genes, primarily in pathways such as muscle fiber development and energy metabolism (Hou et al., 2021).

The HYP is one of the important centers of avian metabolism and neural signaling, regulating a variety of physiological activities such as body temperature, feeding, growth and circadian rhythms (Piórkowska et al., 2020). Neurotransmitters are the main molecular substances involved in hypothalamic regulation, with glutamate, the major excitatory transmitter, and γ-aminobutyric acid (GABA), an inhibitory transmitter, and dopamine and 5-hydroxytryptamine providing regulatory signals that are important in motor control and stress responses (Sigel and Steinmann, 2012). The lateral hypothalamic nucleus can mediate the production of orienting behaviors in birds (Tian et al., 2024). With the development of high-throughput sequencing technology, transcriptomics has become an important tool for analyzing molecular mechanisms (D’Agostino et al., 2022). Whole transcriptome sequencing detects mRNA expression along with non-coding RNAs such as long stranded non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and miRNAs, which leads to the construction of a regulatory network of competing endogenous RNAs (ceRNAs) and reveals the regulatory mechanisms between different genes. This method is widely used in livestock and poultry research.

In summary, a transcriptome-wide approach can be used to reveal the gene network associated with differences in muscle development and athletic ability in poultry. Whole transcriptome studies targeting pigeon neural tissues are extremely limited and systematic studies correlating the transcriptome with neurotransmitter metabolism analysis are lacking. The aim of this study was to investigate the molecular mechanisms of neural excitability and flight adaptation in tumbler pigeons and to study the key genes and neural pathways involved in these processes. These findings will deepen our understanding of behavioral regulation in pigeons and have potential applications in the conservation of local pigeon breeds and the identification of genetic traits associated with flight performance.

Materials and methods

Ethics approval and consent to participate

Animal Welfare and Use Committee of the Zhejiang Academy of Agricultural Sciences (No. 25ZALAS55) and conducted in accordance with the Regulations on the Management of Laboratory Animals.

Experimental animals and sample collection

The tumbling pigeon is a local distinctive breed of racing pigeon characterized by unique aerial behaviors, including hovering and continuous flipping in the air. The White King Pigeon is one of the most widely bred meat pigeon breeds in China, and can be used for both meat and eggs, and is therefore widely bred.

In this study, three male 2-year-old healthy tumbling pigeons (FF) and white king pigeons (BY) were randomly selected from Yanqi Lifeng Cooperative and Bayinguoleng Tianyu Pigeon Breeding Cooperative (Korla) in Xinjiang. Pigeons were euthanized using cervical dislocation and the HYP was collected immediately after the pigeons were euthanized. The pigeon's head was dissected to expose the brain and locate the HYP. The HYP is located at the base of the brain and is connected to the brainstem. Using fine surgical scissors and forceps, the hypothalamic tissue is quickly and carefully removed. HYP was collected, immediately frozen in liquid nitrogen, and stored at -80°C for RNA extraction and subsequent transcriptome sequencing.

Neurotransmitter levels detection and analysis

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to qualitatively and quantitatively analyze neurotransmitters in BY and FF pigeon HYP. The experimental steps were divided into sample preparation, chromatographic analysis, mass spectrometric analysis and data processing. In the sample preparation stage, firstly, about 50 mg of pigeon HYP sample was accurately weighed and 500 μL of pre-cooled 70% methanol aqueous extract at -20°C was added, vortexed and mixed for 3 min, then the sample was centrifuged at 12,000 r/min for 10 min at 4°C, and 300 μL of supernatant was pipetted and transferred to 1.5 mL centrifuge tubes, which continued to rest for 30 min at -20°C in a refrigerator. The samples were removed and centrifuged again at 4°C/12000 r/min for 10 minutes. Finally, 200 μL of supernatant was transferred for further analysis.

Chromatographic analysis was carried out using an ultra performance liquid chromatography (UPLC) system, utilizing a Waters ACQUITY UPLC HSS T3 C18 column (1.8 μm, 100 mm × 2.1 mm), with the mobile phases of ultrapure water containing 0.1% formic acid (phase A) and acetonitrile (phase B), at a flow rate of 0.35 mL/min, the column temperature was set at 40°C, and the injection volume was 2 μL. The chromatographic gradient started from 95% aqueous phase at 0 min, reached 95% acetonitrile at 8 min, and returned to 95% aqueous phase at 9.6 min, which ensured good separation of the metabolites.

Mass spectrometry was performed using a SCIEX QTRAP 6500+ system (SCIEX, Canada) in electrospray ionization (ESI) mode, with positive and negative ion modes switched, the source temperature set to 550°C, and a spray voltage of 5500 V (positive ion mode) or -4500 V (negative ion mode). The parent ion of each target substance is ionized by the collision chamber, forming multiple fragment ions that are analyzed in multiple reaction monitoring (MRM) mode. The MRM model effectively reduces the interference of non-target substances, thus improving the accuracy of quantitative analysis.

In qualitative analysis, standards are used as a reference for accurate identification of metabolites by comparing peak shapes and retention times of chromatographic data. Quantitative analysis is based on a standard curve in which the concentration of each metabolite is calculated using a known standard concentration compared to the peak area ratio of the target substance in the sample. Standard curves were constructed using standard solutions of different concentrations, linear equations were plotted, and correlation coefficients were determined by calculating the relationship between the concentration ratios and peak area ratios of the external and internal standards to ensure the accuracy of the quantitative results.

RNA isolation, library preparation, and sequencing

Total RNA was extracted from HYP and RNA purity was assessed using a microspectrophotometer (Nanodrop 2000, Thermo Fisher Scientific, Waltham, MA, USA), while RNA integrity and quality were determined using an Agilent 2100 system (Agilent Technologies, Palo Alto, CA, USA) to determine RNA integrity and quality. During mRNA library construction, total RNA was first enriched and purified with magnetic beads, and then RNA was fragmented at high temperature. lncRNA and circRNA require removal of rRNA from total RNA, enrichment by SPRI-purified magnetic beads, and then fragmentation with high temperature. The first cDNA strand was synthesized in reverse transcriptase mixture using the fragmented RNA as a template, respectively. During the synthesis of the second cDNA strand, end repairs and A-tails were ligated upon completion. mRNA was purified and target fragments were selected using Hieff NGS® DNA Selection Beads, and lncRNA, circRNA were purified using AHTS DNA Clean Beads, the libraries were amplified by PCR. From the extracted total RNA, 18-30 nt fragments were selected by agarose gel electrophoresis amplification and sequentially ligated to the 3′ and 5′ ends. miRNAs with both sides of the junctions ligated were subjected to reverse transcription and PCR amplification, and then the bands of ∼140 bp were recovered and purified by agarose gel electrophoresis to complete the library construction. Finally, all libraries were analyzed on the Illumina Novaseq X Plus platform.

Differential expression analysis and functional enrichment

Raw sequencing data were quality controlled to exclude spliced sequences, low-quality reads, reads containing too many Ns, and sequences that were too short, resulting in high-quality clean reads for subsequent analysis. mRNAs, lncRNAs and circRNAs were aligned to the reference genome (NCBI: GCA_00033793.2) using HISAT2 and quantified by RSEM; miRNAs were aligned by Bowtie and annotated with reference to the miRBase database. Based on the HISAT2 comparison results, we reconstructed the transcripts using Stringtie and calculated the expression levels of all genes in each sample by RSEM. Differential expression analysis between samples was performed using DESeq2, with thresholds set at |log₂ Fold Change| ≥ 1 and FDR < 0.05. To further explore the functional significance of differentially expressed genes (DEGs), we mapped differentially expressed genes to entries in the Gene Ontology (GO) database (http://www.geneontology.org/) and counted the number of genes in each entry to obtain a list of genes with specific GO functions and their statistical counts. P-values were calculated using the hypergeometric distribution method (significance threshold: P < 0.05) to identify GO entries that were significantly enriched for differentially expressed genes relative to the genome-wide background, and thus to determine the primary biological functions of these genes. Kyoto Encyclopedia of Genes and Genomes (http://www.kegg.jp/) pathway analysis was performed using KAAS.

ceRNA analysis

CeRNA refers to RNA molecules that compete for binding to common miRNA response elements (MREs) within complex transcriptional regulatory networks in organisms. These include mRNA from protein-coding genes, lncRNA, pseudogenes, and circRNA. Screen significantly differentially expressed mRNAs, lncRNAs, circRNAs, and miRNAs by setting the differential threshold to FDR < 0.05 and |log2FC| > 1. Target genes were predicted using miRNA prediction tools miRanda and TargetScan, and relevant target gene pairs were screened. Highly correlated ceRNA pairs were identified using Pearson's correlation coefficient, and significant ceRNA interactions were further selected via hypergeometric distribution testing. Use Cytoscape to construct a ceRNA regulatory network diagram, illustrating the regulatory relationships between RNAs. Perform GO and KEGG enrichment analysis to identify significantly enriched gene functions and pathways.

Results

Neurotransmitter analysis

Neurotransmitter profiling of the HYP in FF and BY pigeons revealed that most neurotransmitters exhibited a generally similar distribution between the two groups, whereas several key compounds showed significant differences (Fig. 1). Among all detected neurotransmitters, glutamic acid had the highest concentration, and the FF group had significantly higher levels of GABA, EPI, and HGA than the BY group. In contrast, no significant differences were observed in the levels of glutamine, dopamine, acetylcholine, or serotonin, which are representative monoamine neurotransmitters.

Fig. 1.

Neurotransmitter levels. The y-axis represents the changes in neurotransmitter levels in the hypothalamus (unit: ng per million cells), and the x-axis indicates the names of the neurotransmitters. The concentrations of Glutathione (GSH), Glutamic Acid (Glu), Gamma-Aminobutyric Acid (GABA), Epinephrine (EPI), Dopa (DA), Choline (CHO), Aspartic Acid (Asp), Acetylcholine (ACh), Serotonin (5-HT), Homogentisic Acid (HGA), 5-Hydroxy-Tryptophan (5-HTP), 3-Hydroxytyramine (3-HT), Thyroxine (T4), Glycine (Gly), and Glutamine (Gln) were compared using a T-test. *P ≤ 0.05; NS indicates no significant difference between the two groups.

Transcriptome data

Differential expression mRNAs

The volcano plot revealed a total of 145 significantly upregulated genes(p ≤ 0.05), including PDK4, NUDT2, POMC, ATP5PD, and OR14I1, and 369 significantly downregulated mRNAs, such as PLG, LIPC, and PCK1 (Fig. 2A, Fig. S1A, Supplementary Table 1). Heatmaps clearly reveal differential mRNA expression between the FF and BY groups (Fig. 2B,). To further explore the biological functions of these DEGs, GO enrichment analysis was conducted, showing that the 514 DEGs were mainly enriched in biological process (BP), cellular component (CC), and molecular function (MF) categories (Fig. 2C). In BP, the most enriched terms included cellular process, metabolic process, and biological regulation; in CC, cellular anatomical entity and protein-containing complex were dominant; while in MF, binding, catalytic activity, and molecular function regulator activity were significantly enriched. Further KEGG pathway enrichment analysis indicated that the DEGs were significantly associated with pathways such as metabolic pathways, environmental information processing, cytokine–cytokine receptor interaction, TGF-beta signaling pathway, and glycine, serine and threonine metabolism (Fig. 2D). Among them, cytokine–cytokine receptor interaction and metabolic pathways were the most significantly enriched, suggesting their potential involvement in energy metabolism regulation and stress responses.

Fig. 2.

Differential expression analysis of mRNAs. (A) Volcano plot of differentially expressed mRNAs, The x-axis represents the fold change in hypothalamic gene expression, while the y-axis indicates the statistical significance of differential expression.; (B) Heatmap of differentially expressed mRNAs; (C) GO enrichment of mRNAs; (D) KEGG enrichment of mRNAs.

Differential expression lncRNAs

A total of 317 differentially expressed lncRNAs were identified, including 131 upregulated (p ≤ 0.05; e.g., XR_010470883.1, MSTRG.13173.14, XR_010466941.1) and 186 downregulated (e.g., MSTRG.14615.4, MSTRG.2990.24, XR_010471646.1) (Fig. 3A, Fig. S1B, Supplementary Table 2). The heatmap intuitively illustrated the expression divergence of lncRNAs between groups, indicating that the overall transcriptional profiles of the FF and BY hypothalami were clearly distinct (Fig. 3B).

Fig. 3.

Differential expression analysis of lncRNAs. (A) Volcano plot of differentially expressed lncRNAs; (B) Heatmap of differentially expressed lncRNAs.

Differential expression miRNAs

For miRNAs, 23 were significantly upregulated (p ≤ 0.05; e.g., novel-m0082-5p, novel-m0166-3p, miR-9624-y), while 26 were significantly downregulated (miR-1454-z, miR-122-x, novel-m0017-5p) (Fig. S1C,Supplementary Table 3). Both the volcano plot and heatmap showed clear differential expression patterns between FF and BY samples(Fig. 4A,B). GO enrichment analysis revealed that the target genes of these miRNAs were mainly enriched in cellular process, biological regulation, metabolic process, binding, and cellular anatomical entity, consistent with mRNA enrichment results (Fig. 4C). KEGG pathway analysis indicated significant enrichment in phosphatidylinositol signaling system, adherens junction, inositol phosphate metabolism, and MAPK signaling pathway, suggesting that these miRNAs play important roles in cell signal transduction, adhesion, metabolism, and stress response regulation (Fig. 4D).

Fig. 4.

Differential expression analysis of miRNAs. (A) Volcano plot of differentially expressed miRNAs; (B) Heatmap of differentially expressed miRNAs; (C) GO enrichment of miRNA; (D) KEGG enrichment of miRNAs.

Differential expression circRNAs

In total, 304 differentially expressed circRNAs were identified, including 165 upregulated (p ≤ 0.05; e.g., novel_circ_014758, novel_circ_002690, novel_circ_017880) and 139 downregulated (novel_circ_028042, novel_circ_028107, novel_circ_028104) (Fig. 5A,Fig. S1D,Supplementary Table 4). The heatmap revealed distinct clustering patterns, demonstrating that circRNA expression significantly differed between the two groups (Fig. 5B). GO enrichment analysis indicated that DEcircRNAs were mainly involved in cellular process, biological regulation, metabolic process, and regulation of biological process, suggesting roles in cellular regulation and energy metabolism (Fig. 5C). KEGG pathway analysis further revealed that DEcircRNAs were significantly enriched in glycosaminoglycan biosynthesis-heparan sulfate/heparin, motor proteins, and cardiac muscle contraction pathways (Fig. 5D). These pathways are associated with cell–environment interactions, contractile capacity, and energy-excitation coupling, implying that circRNAs may participate in hypothalamic energy and neural activity regulation during flight adaptation.

Fig. 5.

Differential expression analysis of circRNAs. (A) Volcano plot of differentially expressed circRNAs; (B) Heatmap of differentially expressed circRNAs; (C) GO enrichment of miRNA; (D) KEGG enrichment of circRNAs.

Protein-protein interaction

The protein–protein interaction (PPI) networks of the DEGs enriched in KEGG pathways were constructed using the STRING database (http://string-db.org). Key proteins in the pathway and potential regulatory networks were identified by visualizing protein-protein interactions. Proteins in this network, such as NPY and PCK1, play critical roles in metabolic regulation, muscle development, and neuroendocrine signaling to support sustained flight performance. (Fig. 6).

Fig. 6.

Protein–protein interaction (PPI) network of differentially expressed mRNAs. Each node represents a protein encoded by a differentially expressed gene, and the edges indicate predicted or experimentally validated interactions. The network highlights the functional associations and interaction modules among key genes in the hypothalamus.

ceRNA network

The ceRNA regulatory networks of tumbler pigeons and white king pigeons were analyzed by integrating transcriptome sequencing data. The results are shown in Fig. 7A, in which 439 corresponding regulatory pairs were identified, including 138 mRNAs and 165 lncRNAs. Fig. 7B shows that multiple lncRNAs, such as XR_010466062.1 and XR_010475485.1, competitively bind to miRNAs like cli-miR-204-5p and miR-204-x, thereby regulating the expression of mRNAs such as FAM124A and LOC102091083.

Fig. 7.

ceRNA. (A) Statistical plot of interactive ceRNAs, (B) Sankey diagram of interactive ceRNAs, (C) GO enrichment plot of interactive ceRNAs, (D) KEGG enrichment plot of interactive ceRNAs.

GO enrichment analysis revealed that mRNAs in the ceRNA network were significantly enriched (p≤ 0.05) in the categories of biological process, cellular process (BP) and molecular function (Fig. 7C). KEGG results (Fig. 7D) showed that mRNAs associated with the ceRNA network were significantly enriched (p≤ 0.05) in pathways related to immunity and signaling, such as cytokine-cytokine receptor interaction, TGF-beta signaling pathway and retinol metabolism, among others.

Discussion

The unique flight behavior, spatial orientation and endurance performance of racing pigeons are the result of artificial selection. The formation of these excellent traits not only originates from the regulation of the peripheral muscular system and energy metabolism, but is also closely related to the neural mechanism mechanisms of the HYP. HYP is one of the key centers regulating neural, endocrine and metabolism in the avian brain, located between the midbrain and the cerebrum, and although small in size, it is crucial in maintaining homeostasis within the organism, participating in the regulation of a wide range of physiological activities, and serving as a node connecting environmental signals and physiological responses. Research has shown that racing pigeons have a highly developed neurotransmitter system and perceptual network, and that the HYP is involved in energy distribution and stress response (Boswell, 2005; El-Sayed et al., 2023).

Neurotransmitters are powerful chemicals produced by neuronal cells in the brain that are involved in the regulation of animal behavior and whose dynamics directly affect motor and cognitive function. Neurotransmitters in the HYP (e.g., glutamate, GABA, dopamine, 5-hydroxytryptamine) play important roles in both flight behavior and stress response (El-Sayed et al., 2023). The Glutamate and GABA levels were higher in the FF group than in the BY group in this study. Glutamate is the predominant excitatory transmitter in the central nervous system and is involved in motor control and perceptual responses. Acetylcholine influences motor coordination by modulating neuromuscular excitability and learning memory pathways (Bai et al., 2002, Picciotto et al., 2012).

Comparative analysis of the HYP transcriptomes of tumbler pigeons and white king pigeons revealed the expression and differences in genes and regulatory pathways between the species. A total of 514 differential genes were screened in HYP and analyzed by GO and KEGG for functional enrichment and annotation, and the differential genes were mainly involved in metabolic regulation, neural signaling and immunity and other related pathways. PDK4, a central regulator of energy metabolism, promotes fatty acid oxidation by inhibiting pyruvate dehydrogenase complex activity, thereby maintaining energy homeostasis in the body under high energy demand (Sudden and Holness, 2003). As the primary inhibitor of glucose oxidation, the upregulation of PDK4 signals a metabolic shift toward prioritizing fatty acid utilization, preserving glycogen stores, and sustaining energy production during prolonged exercise (Pilegaard and Neufer, 2004; Wu et al., 2001). During prolonged flight, tumbler pigeons primarily rely on fatty acids as energy substrates, consistent with previous research findings. Additionally, PDK4 is a candidate gene associated with pigeon flight performance (Liu et al., 2020). POMC, a hypothalamic neuropeptide precursor gene, is involved in the regulation of energy balance, stress response and behavior, and it may play a role in modulating neural excitability and complex flight maneuvers (Mountjoy, 2010). Simultaneous involvement of pro-inflammatory mediators such as serum amyloid A2 (SAA2) and complement C3 (C3) is essential for mitigating collateral tissue damage associated with extreme physiological stress during endurance flight. This suggests that neurological function and overall homeostasis can be protected during prolonged flights by suppressing underlying systemic inflammation (Garton et al., 2025). In metabolic pathways, NUDT2 belongs to the Nudix superfamily and primarily catalyzes the hydrolysis of adenosine diphosphate tetraphosphate (AMP₄A), yielding adenosine monophosphate (AMP) and adenosine triphosphate (ATP). Ap₄A is a second messenger within cells, participating in stress responses, cell cycle regulation, and other processes(Abu-Rahmah et al., 2024). NUDT2 up-regulation rapidly reduces Ap₄A production during stress and prevents its interference with ATP synthesis and metabolic enzyme activities, thus helping cells to restore normal energy metabolism (Zegarra et al., 2023). ATP5PD is an important structural subunit of the mitochondrial ATP synthase complex responsible for maintaining peripheral integrity and thus ensuring efficient ATP synthesis. Enhanced mitochondrial capacity is positively correlated with the upregulation of ATP5PD, and increased energy output within hypothalamic neurons can meet the high energy demands of neurons during navigation (Alexandre et al., 2025). Thus, NUDT2 and ATP5PD are closely related to energy metabolism and mitochondrial function, providing security in terms of energy supply and demand balance in tumbler pigeons. It is shown that the functional role of neural excitability and metabolic adaptation during flight can be modulated by regulating key genes such as PDK4 and POMC.

Non-coding RNAs play important roles in various physiological activities in poultry and are involved in the regulation of metabolism, neuromodulation and immunity. Although it cannot code for proteins, lncRNA can participate in the regulation of gene expression and cellular functions through a variety of mechanisms, and it can also participate in the regulation of mammalian fat metabolism, muscle development, cellular development, and disease through a variety of roles, such as nucleic acid transcriptional templates, ribose activators, miRNA precursors, and competitive inhibitory RNAs (Quinn and Chang, 2016; Rinn and Chang, 2012). In addition, miRNAs act as core nodes in the ceRNA network and regulate the expression of both mRNAs and lncRNAs in the organism. For example, lncRNA XR_010466062.1 binds cli-miR-204-5p to regulate the expression of FAM124A, which may be associated with muscle growth and development (LI et al., 2022). This mechanism works by regulating muscle growth and development, which in turn improves flight ability. The miRNAs and lncRNAs, while they don't encode proteins, regulate mRNA translation through competitive binding.

Conclusions

Neurotransmitter expression and whole transcriptomics of the HYP of tumbler pigeons and white king pigeons revealed differences in molecular expression in flight behavior and energy metabolism. tumbler pigeons have a greater endurance and efficient energy metabolism during flight by regulating the expression of DEGs (PDK4, PCK1, POMC). Non-coding RNAs are involved in the regulation of a complex ceRNA network that coordinates multiple physiological processes such as neural, metabolic and stress responses in pigeon flight. These findings not only enrich the molecular mechanisms of behavioral regulation in pigeons, but also provide new ideas for genetic improvement and selection of functional traits in poultry breeds.

CRediT authorship contribution statement

Ying PENG: Writing – original draft. Xiaoyu ZHAO: Writing – original draft. Tiantian GU: Writing – review & editing, Formal analysis. Li CHEN: Visualization. Tao ZENG: Investigation. Yong TIAN: Conceptualization. Wenwu XU: Software. Haiying LI: Project administration. Lizhi LU: Supervision.

Disclosures

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with this work submitted.