
==== Front
Poult Sci
Poult Sci
Poultry Science
0032-5791
1525-3171
Elsevier

S0032-5791(24)00634-5
10.1016/j.psj.2024.104055
104055
GENETICS AND MOLECULAR BIOLOGY
Enhancing aggression in Henan gamecocks via augmentation of serotonergic-dopaminergic signaling and attenuation of neuroimmune response
Su Chuanchen *
Zhang Lin *
Pan Yuxian *
Jiao Jingya *
Luo Pengna *
Chang Xinghai †
Zhang Huaiyong *
Si Xuemeng *
Chen Wen *
Huang Yanqun hyanqun@aliyun.com
*1
⁎ College of Animal Science and Technology, Henan Agricultural University, Zhengzhou Henan 450046, China
† Henan Changxing Agriculture and Animal Husbandry co., LTD, Kaifeng, Henan 475000, China
1 Corresponding author: hyanqun@aliyun.com
02 7 2024
11 2024
02 7 2024
103 11 1040558 5 2024
27 6 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Animal aggression is one of the most conserved behaviors. Excessive and inappropriate aggression was a serious social concern across species. After long-term selection under strict stress conditions, Henan gamecock serves as a good model for studying aggressive behavior. In this research, we constructed a Henan game chicken backcross population containing 25% Rhode Island Red (RIR), and conducted brain transcriptomics and serum metabolomics analyses on Henan gamecock (HGR) through its comparison with its female encounters (HGH) and the male backcross birds (BGR). The study revealed that seven differential metabolites in serum and 172 differentially expressed genes in the brain were commonly shared in both HGR vs. HGH and HGR vs. BGR comparisons. They exhibited the same patterns of modulation in Henan gamecocks, following either HGH < HGR > BGR or HGH > HGR < BGR style. Therein, some neurological genes involving in serotonergic and dopaminergic signaling were upregulated, while the levels of many genes related with neuro-immune function were decreased in Henan gamecock. In addition, many unknown genes specifically or highly expressed in the brain of the Henan gamecock were identified. These genes are potentially key candidates for enhancing the bird's aggression. Multi-omics joint analysis revealed that tyrosine metabolism and neuroactive ligand-receptor interaction were commonly affected. Overall, our results propose that the aggressiveness of Henan gamecocks can be heightened by the activation of the serotonergic-dopaminergic metabolic process in the brain, which concurrently impairs the neuroimmune system. Further research is needed to identify the function of these unknown genes on the bird's aggressive behavior.

Key words

Henan game chicken
aggressive behavior
immune defense
brain transcriptomics
serum metabolomics
==== Body
pmcINTRODUCTION

Animal aggression is a behavioral adaptation that significantly aids in securing scarce resources. It is one of the most conserved behaviors across the animal species, such as insects, fish, lizards, birds, and most mammals including humans (Takahashi et al., 2014). However, excessive aggression can lead to a variety of maladaptive outcomes, including problems related to animal welfare and productivity. Even though the specific patterns of aggressive behavior vary across species, there exists a notable similarity in the neurobiology underlying aggression in birds, fishes, rodents, primates, and humans (Takahashi et al., 2014; Quintana et al., 2021). Due to natural selection, male birds generally display greater levels of aggression than females (Millman et al., 2000; Hashikawa et al., 2018). Specifically, cockfighting, an age-old sport, is solely a rooster's activity. The brain serves as the central control center for animal behavior, its multiple areas, such as the hypothalamus, dorsal raphe nucleus, prefrontal cortex, nucleus accumbens, and olfactory system have been found to be involved in animal aggression (Lin et al., 2011; Takahashi and Miczek 2014; Yang et al., 2023). Aggressive behavior could be regulated by various neuromodulators such as neuropeptides (Quintana et al., 2021) and biogenic amines. Biogenic amines, such as serotonin, noradrenaline, and dopamine act as the neurotransmitters, neuromodulators and neurohormones to play a very important role in the mediation of animal aggressiveness(Veroude et al., 2016). Therein, the serotonergic-dopaminergic metabolic process was a relatively conservative pathway related with aggressive behavior across species (Dennis et al., 2008; Li et al., 2016; Zhou et al., 2023). Dopamine is primarily synthesized from tyrosine under the action of Tyrosine hydroxylase (TH) by dopaminergic neurons in brain (Yuen et al., 2021). In addition, it has been demonstrated that dopamine could be formed from trace amines, such as tyramine (Hiroi et al., 1998). Serotonin is synthesized from the essential amino acid tryptophan under the action of Tryptophan hydroxylase 1 or 2 (TPH1, TPH2), it usually inhibits animal aggressiveness (Kastner et al., 2019; Zhou et al., 2023). In addition, the Dopa decarboxylase (DDC) catalyzes L-DOPA to dopamine and 5-hydroxytryptophan to serotonin (5-HT) (Monastirioti 1999).

Aggressive behavior is a heritable trait (Komai et al., 1959; Singleton and Hay 1982; Anholt and Mackay 2012; Pavlov et al., 2012; Takahashi et al., 2014). Gamefowls are known for their extremely high level of natural aggression. This trait has been enhanced over generations through selective breeding (Komiyama et al., 2020; Bendesky et al., 2023). Driven by huge benefits, breeders have exerted intense selection pressure on gamecocks (Komiyama et al., 2020; Bendesky et al., 2023). After a match, the male game birds that lost were euthanized, whereas the victorious gamecocks were bred with healthy females (Komiyama et al., 2020). Reversely, breeds that have been selectively bred for production purposes, tend to be more docile (https://www.chickens.allotment-garden.org/aggression-poultry/aggressive-poultry-breeds/). Although cockfighting has been banned in many countries, such as China, the U.S., Mexico, the Netherlands, and Japan, these bird varieties bred for fighting purposes still exist (Komiyama et al., 2003; Hata et al., 2021; Bendesky et al., 2023). They have become excellent models for aggression research.

Despite scarce direct evidence in the literature regarding bird hybridization impacting aggression, the crossbred offspring could potentially display aggression levels somewhere between the 2 parent breeds (Guhl et al., 1960). In the breeding of gamecocks, locals typically crossbreed local gamecocks with other gamecock breeds like Thai gamecocks, Burmese gamecocks, and Vietnamese gamecocks to enhance the aggressiveness of the gamecocks (Yang et al., 2006). However, for the production of high-quality broilers, gamecocks are sometimes crossbred with more docile chicken breeds (Xu et al., 2001; Liu 2006; Uemoto et al., 2009).

In recent years, some scholars have attempted to uncover the aggressive mechanism of these gamecocks based on genomic selection signature combining transcriptome data and have made notable progress in this area (Luo et al., 2020; Bendesky et al., 2023; Ren et al., 2023; Zhou et al., 2023). Differentially expressed genes were mainly associated with muscle development and neuroactive-related pathways, and several specific genes associated with aggression have been identified, such as isoprenoid synthase domain-containing protein (ISPD), alkylglycerol monooxygenase (AGMO), and carboxypeptidase Z (CPZ) (Bendesky et al., 2023; Ren et al., 2023; Zhou et al., 2023).

Henan gamecocks, used in fighting competitions for entertainment, originated from Kaifeng. This city in Henan Province, China, famed as the ancient seat of seven dynasties, boasts a rich tradition of cockfighting (Huang et al., 2016). Analysis of genome-wide genetic structure revealed that they underwent aggressive trait inbreeding, making them a valuable model for aggression study (Zhi et al., 2023). In this study, we constructed a backcross population (Figure 1) containing 75% Henan Gamefowl lineage and 25% Rhode Island Red (RIR) lineage, an American breed widely used in the production of modern commercial laying hens (source: https://en.wikipedia.org/wiki/ Rhode_Island_Red). Our hypothesis posits that long-term selection resulted in significant modulation of neural transcripts and serum circulating substances in Henan gamecocks compared to their female counterparts and the backcross birds. This change, we propose, could offer insights into aggression modulation. Therefore, we aimed to uncover the potential mechanisms underlying aggression in gamecocks by performing brain transcriptomics and serum metabolomics profiling across different sexes and populations.Figure 1 Experiment design schematic diagram. (A) Constructed pattern of backcross population. (B) Experiment design.

Figure 1

MATERIALS AND METHODS

Construction of Backcross Population

The backcross population was constructed with male RIR and Henan game chickens as Su et al. described (Su et al., 2024). Firstly, the adult RIR roosters were hybridized with the female Henan game chickens, and then the male hybrids were backcrossed with the female game chickens. Finally, the backcross birds contain 25% RIR heritage and 75% Henan Gamefowl heritage (Figure 1A).

Experimental Animals

All experiment birds were from the Henan game chicken resource conservation farm (Kaifeng, Henan province, China). The Henan gamefowls were collected from Henan cockfighting enthusiasts and preserved as the germplasm resources in the farm. Eggs from both the Henan gamefowl population and the backcross population were hatched together in the same batch. After hatching, birds were raised in cages situated within an identical controlled environment. The room temperature was initially maintained at 32 to 35°C for the first 7 d, after which it was reduced by 2 to 3°C each week until reaching 21°C at 42-days-old. The lighting regimen implemented was 22 h daily for the initial 0 to 3 d, after which the duration of light exposure was incrementally reduced each week. By the 12th wk, it was brought down to 10 h and this schedule was sustained until the birds reached 120 d of age. All birds were provided the same diet, formulated according to the nutritional standards of local Chinese breeds (NY/T33—2004). For chickens aged 0 to 6 wk, the diet's metabolizable energy was 12.12 MJ/kg, with a protein content of 20%. Afterwards, a diet with a metabolizable energy of 11.70 MJ/kg and a protein content of 15% was used. The chickens were allowed to feed and drink water freely.

At 120 d of age, 3 roosters (named as HGR, 1.34 ± 0.08kg in weight) and three hens (named as HGH, 1.08 ± 0.08kg in weight) were randomly selected from the Henan game chickens population, and three roosters (named as BGR, 1.12 ± 0.15kg in weight) were randomly selected from the backcross population for slaughter. Their body weight was close to the average value of the respective groups. Brain tissues were collected and snap-frozen in liquid nitrogen, while serum samples were isolated and stored at -80℃ for use (Figure 1B). The experiment was performed in accordance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Henan Agricultural University (Permit Number: 12-1328; Date: 05-2021).

Detection of Serum Metabolites by Broad-Targeted Metabolomics

About 50 μL of isolated serum from HGR, BGR and HGH birds (n=3/group) was used for metabolomics profiling by high performance liquid chromatography-mass spectrometry (HPLC-MS/MS). Briefly, 300 μL of pure methanol (as the internal standard extract) was added to the serum, after centrifuging with 12,000 rpm and 4°C for 3 minutes, 150 μL of the supernatant was collected and analyzed using an LC-ESI-MS/MS system (UPLC, Exion LC AD; https://sciex.com.cn/; MS, QTRAP® System, https://sciex.com/). The quality control (QC) sample was prepared by mixing equal volumes of extracts from all samples within the same group (n = 3). All data analysis was conducted based on the self-built MWDB database (Metware Biotechnology Co., Ltd. Wuhan, China).

Identification of Differential Metabolites and Enrichment Analysis

Metabolomics data was used to conduct the Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA) within R software (www.r-project.org). Unsupervised PCA was performed by statistics function prcomp within R software (www.r-project.org)(Worley and Powers 2016). Significantly regulated metabolites between groups were determined by Variable Importance in Projection (VIP) score ≥ 1 and absolute Log2 fold change (| Log2FC |) ≥ 1. To prevent the model from being overly adapted to the data, a statistical method called “permutation test” was implemented, which involved 200 iterations of data reassignment. The identified metabolites were annotated using the Encyclopedia of Genes and Genomes (KEGG) Compound database (http://www.kegg.jp/kegg/compound/) and mapped to the KEGG pathway database (http://www.kegg.jp/kegg/pathway.html).

Library Construction of Brain Transcriptomics

We employed RNA sequencing (RNA-seq) to identify the gene expression profiles in the brains of HGR, BGR and HGH birds (n = 3 for each group), using the same individuals for serum metabolomics. Total RNA was extracted from the brain tissues with Trizol reagent (Invitrogen, Carlsbad, CA) and quantified using a Nanodrop spectrophotometer (DE). The integrity of RNA was evaluated using the RNA Nano 6000 Assay Kit on a Bioanalyzer 2100 instrument (CA). In addition, it is worth mentioning that due to multiple brain areas may be involved in aggression behavior in animals (Lin et al., 2011; Takahashi and Miczek 2014; Yang et al., 2023), the RNA samples for brain transcriptome profiling were based on the mixed brain tissues of birds in this research.

RNA-seq libraries were generated with NEB Next® Ultra TM RNA Library Prep Kit for Illumina (NEB, Ipswich, MA) according to the manufacturer's recommendations. Briefly, mRNA was purified from 1 µg of total RNA using poly-T oligo-attached magnetic beads, then cDNA was synthesized following the manufacturer's instructions (with the Prime Script RT Reagent Kit, Takara, Japan), cDNA fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, CA) to preferentially select fragments of 250 to 300 bp in length. After PCR amplification, the PCR products were purified using the AMPure XP system, and the library quality was assessed on the Agilent Bioanalyzer 2100 system (CA). The constructed libraries were subsequently sequenced using an Illumina HiSeq platform which generated 150 bp paired-end reads. The initial data was processed using Fastp version 0.19.3. High-quality reads were gained after removing raw reads with low-quality base, adapter, and poly (N). The evaluating indicator used was Q30. All following analyses were conducted using clean reads.

Data Analysis of Brain Transcriptomics

The clean reads were mapped to the chicken reference genome (version: Galgal 6.0: ftp://ftp.ensembl.org/pub/release-101/fasta/gallus_gallus/dna/) with HISAT v2.1.0. Gene expression levels were quantified by feature Counts v1.5.0-p3. The Fragments Per Kilobase Million (FPKM) value for each gene was calculated based on its length and read count. In order to verify the accuracy of the transcriptome sequencing, seven genes were randomly selected to conduct Quantitative Reverse Transcription PCR (qRT-PCR), whose expression levels were analyzed using the 2−ΔΔCt method and normalized to β-actin (Livak and Schmittgen 2001). The optimized primers of target genes for qRT-PCR are presented in Table 1.Table 1 qRT-PCR primer information.

Table 1Primer name	Sequence (5ˊ–3ˊ)	Product length	
TPH2	F: GGACCTCCGCAGTGATCTAA	203	
	R: TACACAATGACACAAGCCGC		
SLC2A6	F: TTCCTTGGGGTTGTGGAGTT	77	
	R: AAGACATTCCCAGCGCAGAT		
ZAP70	F: GCTGGACCTACAGTTGGGAAGA	78	
	R: CAATGCTGTAGTAGTAGGTGCGGA		
P2RX7	F: AGTTCGCGTTACCCTGAAAG	84	
	R: TCTCTTGTCTGCGTTGGTATG		
IRF7	F: TACACTGAGGACTTGCTGGAGGT	170	
	R: AAGATGGTGGTCTCCTGATCC		
TAPBP	F: TGGGCACAGAAGGAACCATCA	143	
	R: ACGCCACTCGAGGACAAA		
B2M	F: TCCTTCAACGACGACTGGAC	126	
	R: ATCCCACTTGTAGACCTGCG		
BLB1	F: GGCGGTCTGTGCTCTTCTAT	137	
	R: CGACTCTCTCCACCACTCCTA		
β-actin	F: GTCCACCGCAAATGCTTCTAA	78	
	R: TGCGCATTTATGGGTTTTGTT		
F, forward primer; R, reverse primer.

Differential expression analyses in HGR vs. BGR and HGR vs. HGH were performed using the DESeq2 v1.22.1. To identify genes that were differentially expressed, we first adjusted the resulting P-values using the Benjamini-Hochberg method. This adjustment helps to control the false discovery rate, thus ensuring the statistical validity of our results. Next, we defined genes having a corrected P-value (padj) ≤0.05 and an absolute log2 fold change (|Log2FC|) ≥1, as differentially expressed genes (DEGs). Following the identification of DEGs, to further understand the underlying patterns in the expression of these genes, we conducted K-Means clustering for a total of 6 clusters. This clustering analysis incorporated the DEGs that exhibited a P-value of less than 0.05 in either of the two combinations. Gene Ontology (GO) and KEGG enrichment analyses of DEGs were performed on the bioinformatics platform (https://www.bioinformatics.com.cn) for data analysis and visualization. Protein-protein interaction analyses (Set combined score ≥ 500) were based on the STRING database (http://string-db.org), which included known and predicted protein interactions. The protein interaction network maps were visualized using Cytoscape software (version 3.5.1). The prediction of novel transcripts was performed with StringTie v1.3.4d (Pertea et al., 2015).

Integrated Analysis of Brain Transcriptomics and Serum Metabolomics

To identify the relationship between genes and metabolites, we further conducted KEGG enrichment analyses for both the differential metabolites and genes in each group. Then the common KEGG pathways enriched in both analyses were identified. Finally, an integrated KEGG enrichment pathways were displayed based on the top 25 pathways ranked by their P-value in transcriptome data. To build the core genes-metabolites regulation network. Pearson correlation analyses (|r| > 0.8) were performed on the Metware Cloud (https://cloud.metware.cn/) to detect associations between DEGs known to be related to animal aggression and the detected metabolites, associations with a P-value ≤ 0.05 were selected. The interaction network of DEGs and differential metabolites (DEMs) was constructed using Cytoscape software (version 3.5.1) with the MetScape plug-in (version 3.1.3)(Gao et al., 2010) .

RESULTS

Serum Metabolomic profiling

We conducted broad-targeted metabolomics to explore the modulation of serum metabolites in HGR, by drawing comparisons with HGH, as well as with BGR. The backcross population was constructed as presented in Figure 1A. The backcross birds contain 25% RIR heritage and 75% Henan Gamefowl heritage. The OPLS-DA and cluster analysis indicated the high reliability of the metabolomics data (Supplementary Figure 1A-D). Biological replicates within each group clustered together, while there was a distinct separation between different groups.

In HGR birds, we observed 45 down-regulated DEMs and 44 up-regulated DEMs in HGR vs. HGH comparison (Figure 2A, Table S1), while only 19 DEMs were decreased and 10 DEMs were increased in HGR vs. BGR comparison (Figure 2B, Table S1). The top 5 down-/up- regulated DEMs in HGR birds were presented in Table 2 (for HGR vs. HGH comparison) and Table 3 (for HGR vs. BGR comparison) respectively. It was observed that the DEMs (based on the first-level classification of substances) show distinct distributions in HGR vs. HGH and HGR vs. BGR comparisons (Figure 2C). A large number of DEMs belong to Fatty acyl (FA) and glycerophospholipid (GP) in HGR vs. HGH, but DEMs belonging to Glycerolipid (GL), alcohol and amines are more abundant in HGR vs. BGR (Figure 2C). The DEMs in HGR vs. HGH comparison were mainly enriched in signaling pathways such as bile acids, cholesterol metabolism, and serotonergic synapse (Figure 2D). On the other hand, the DEMs in HGR vs. BGR comparison were mainly enriched in signaling pathways such as bile acids, light signaling, salivary secretion and thermogenesis (Figure 2E).Figure 2 Serum DEMs analysis based on broad-targeted metabolomics. Volcano diagram for HGR vs. HGH (A) and HGR vs. BGR (B). Classification of differential metabolites in HGR vs. HGH and HGR vs. BGR comparisons based on first-level classification of substances (C). KEGG enrichment bubble chart for HGR vs. HGH (D) and HGR vs. BGR (E). Venn diagram of differential metabolites between HGR vs. HGH and HGR vs. BGR comparisons (F). Abbreviations: HGR, male Henan game chicken; HGH, female Henan game chicken; BGR, male backcross bird. The upregulated/downregulated are based on the changes observed in HGR.

Figure 2

Table 2 TOP 5 DEMs in HGR vs. HGH.

Table 2Metabolite	Class I	VIP	Log2FC	Type*	
5(S),15(S)-DiHETE	FA	1.39	-3.60	down	
11β-13,14-dihydro-15-keto Prostaglandin F2α	FA	1.34	-3.05	down	
11-HEDE	FA	1.12	-2.99	down	
15-HEDE	FA	1.12	-2.99	down	
PGF2α	FA	1.32	-2.86	down	
3-hydroxyphenylacetic acid	Organic acid and its derivatives	1.19	2.78	up	
Taurocholic acid	Bile acids	1.54	3.28	up	
Glycochenodeoxycholic Acid	Bile acids	1.15	3.38	up	
Cyclo (Tyr-Leu)	Amino acid and its metabolites	1.36	4.23	up	
Cyclo (Tyr-Ala)	Amino acid and its metabolites	1.21	11.53	up	
TOP 5 DEMs in HGR vs. HGH is based on the absolute Log2FC value.

⁎ The up-regulated or down-regulated for HGR birds; FA: Fatty acyl.

Table 3 TOP 5 DEMs in HGR vs. BGR.

Table 3Metabolite	Class I	VIP	Log2FC	Type*	
Uric acid	Organic acid and its derivatives	1.25	-2.45	down	
Cortexolone	Hormones and hormone related compounds	1.50	-2.00	down	
Guanosine 3′,5′-Cyclic Monophosphate	Nucleotide and its metabolomics	1.11	-1.81	down	
N6-Succinyl Adenosine	Nucleotide and its metabolomics	1.81	-1.81	down	
DL-Carnitine	FA	1.31	-1.75	down	
MG (0:0/22:6/0:0)	GL	2.01	1.29	up	
MG (22:6/0:0/0:0)	GL	2.01	1.29	up	
LPE (17:1/0:0)	GP	1.34	1.40	up	
MG (18:1/0:0/0:0)	GL	1.60	1.58	up	
All trans-Retinal	Coenzyme and vitamins	2.00	1.93	up	
FA, fatty acyl; GP, glycerophospholipid; GL, glycerolipid.

TOP 5 DEMs in HGR vs. BGR is based on the absolute Log2FC value.

⁎ The up-regulated or down-regulated for HGR birds.

The Venn analysis revealed that 7 DEMs were shared by HGR vs. BGR and HGR vs. HGH comparisons (Figure 2F, Table 4). Amazingly, all of them showed the same change pattern in HGR compared to both HGH and BGR (Table 4). The serum levels of metabolites including All Trans-Retinal (ATR) and Lyso Phosphatidyl Ethanolamine (LPE) (17:1/0:0) were the highest in HGR birds (i.e., BGR < HGR > HGH). On the other hand, the levels of metabolites including tyramine, guanosine 3′,5′-Cyclic Monophosphate (cGMP), N6-Succinyl Adenosine, carnitine C18:2-OH and carnitine C8:1, were the lowest in HGR birds (i.e., BGR > HGR < HGH) (Table 4). Amid them, some metabolites (including tyramine, ATR, and cGMP) have been reported to play multiple roles (Table 4), such as neuroactive ligand-receptor interaction (Takahashi and Miczek 2014), tyrosine metabolism (Anwar et al., 2012; Roeder 2020), phototransduction (von Lintig et al., 2010; Reierson et al., 2011) and long-term depression (Reierson et al., 2011), which may be related to bird's aggression.Table 4 Intersection of DEMs in HGR vs. HGH and HGR vs. BGR.

Table 4Metabolite	HGR vs. HGH	HGR vs. BGR	KEGG pathway	
log2FC	Type*	log2FC	Type	
Guanosine 3′,5′-Cyclic Monophosphate	-2.73	down	-1.81	down	Long-term depression, Bile secretion, Thermogenesis, Platelet activation, Circadian entrainment, Phototransduction, cGMP-PKG signaling pathway, etc.	
Tyramine	-1.45	down	-1.27	down	Neuroactive ligand-receptor interaction, Tyrosine metabolism, Protein digestion and absorption, Metabolic pathways.	
N6-Succinyl Adenosine	-2.11	down	-1.81	down	–	
Carnitine C18:2-OH	-1.99	down	-1.08	down	–	
Carnitine C8:1	-2.58	down	-1.31	down	–	
LPE (17:1/0:0)	1.52	up	1.40	up	–	
All trans-Retinal	1.39	up	1.93	up	Vitamin digestion and absorption, Retinol metabolism, Phototransduction, Metabolic pathways.	
⁎ The up-regulated or down-regulated for HGR birds.

Brain Transcriptomics Revealed by RNA-seq

RNA-seq was conducted to further reveal the modulation of brain transcriptomics in HGR by comparing it with BGR and HGH groups. The results demonstrate that the brain transcriptomic data are highly reliable (Figure 3A, Supplementary Figure 1E). The 2D-PCA (Figure 3A) showed that the distance between genders (HGR vs. HGH) is larger than that between populations (HGR vs. BGR), and there was a high correlation between the samples (R > 0.9, Supplementary Figure 1E). The 867 DEGs were detected from HGR vs. HGH comparison. Therein, 543 DEGs were down-regulated and 324 DEGs were up-regulated in HGR birds (Figure 3B, Table S2). While only 272 DEGs were detected in HGR vs. BGR, including 228 dropped genes and 44 elevated genes in HGR birds (Figure 3C, Table S2). The qRT-PCR detection on seven randomly selected genes, further confirmed the reliability of the RNA-seq data (Supplementary Figure 2). K-Means clustering analysis (n = 6) revealed these DEGs presented distinct expression patterns among 3 groups. Therein, HGR birds exhibited the highest level in Sub class 2 and 3, and the lowest level in Sub class 5 and 6 (Figure 3D, Table S3).Figure 3 Identification of DEGs based on Brain transcriptomics. 2D-PCA plot analysis of samples for brain transcriptomics (A). Volcano plot of brain transcriptomics in HGR vs. HGH (B) and HGR vs. BGR (C). K-means cluster analysis based on brain DEGs from HGR vs. HGH and HGR vs. BGR comparisons (D).

Figure 3

We carried out GO and KEGG enrichment analysis with the up-regulated and down-regulated DEGs in HGR vs. HGH and HGR vs. BGR comparisons separately. In HGR vs. HGH, the decreased DEGs in HGR birds were mainly enriched in terms related to immune defense, such as antigen processing and presentation, human T-cell leukemia virus І infection, Th1 and Th2 cell differentiation (Figure 4A), tumor necrosis factor superfamily cytokine production and its regulation (Supplementary Figure 3A). In contrast, the escalated DEGs in HGR birds were mainly associated with oxidative phosphorylation, Parkinson disease, folic acid biosynthesis, cocaine addiction, midbrain development, cellular respiration, and skeletal system morphogenesis (Figure 4B, Figure supplement 3B).Figure 4 KEGG enrichment analysis of brain DEGs based on up/down-regulated patterns in HGR vs. HGH and HGR vs. BGR comparisons. Top 20 KEGG enrichment pathways of down-regulated (A) and up-regulated (B) DEGs in the HGR vs. HGH comparison, along with down-regulated (C) and up-regulated (D) DEGs in the HGR vs. BGR comparison. The upregulated/downregulated are based on the changes observed in HGR.

Figure 4

Similarly, in HGR vs. BGR comparison, the KEGG (Figure 4C) and GO analysis (Figure supplement 3C) showed that the reduced DEGs in brain of HGR birds were mainly enriched in terms related to organismal immune defense. Conversely, the elevated DEGs in HGR vs. BGR were significantly enriched in terms including dopaminergic synapses, cocaine addiction, tryptophan metabolism, and tyrosine metabolism (Figure 4D), as well as processes related to neurotransmitter and monoamine metabolism (Supplementary Figure 3D).

Analysis on Common Brain DEGs Shared by HGR vs. BGR and HGR vs. HGH

The Venn analysis showed that 172 brain DEGs were shared by HGR vs. BGR and HGR vs. HGH combinations (Figure 5A, Table S2). It was interesting that similar to the modulation in serum metabolomics, the expression of these common DEGs in HGR birds presented the same change pattern in two comparisons. Therein, 29 were up-regulated (i.e., BGR < HGR > HGH) and 143 were down-regulated (i.e., BGR > HGR < HGH) in HGR birds (Figure 5A, Table S2). These elevated DEGs belong to Sub class 2, while the lowered DEGs mainly belong to Sub class 6 (Table S3).Figure 5 Enriched GO terms of common DEGs in brain tissues shared by HGR vs. HGH and HGR vs. BGR comparisons. (A) Venn diagram of brain DEGs. Top 10 GO terms of commonly up-regulated DEGs (B) and down-regulated DEGs (C) shared by HGR vs. HGH and HGR vs. BGR comparisons. The upregulated/downregulated are based on the changes observed in HGR.

Figure 5

In the escalated DEGs in HGR birds, some genes, including TH, TPH2, DDC and SLC18A2 have been reported to be associated with animal aggressive behavior through the dopaminergic-serotonergic signaling pathway (Komiyama et al., 2014; Takahashi and Miczek 2014; Komiyama et al., 2020; Smagin et al., 2022) (Supplementary Figure 4). Meanwhile, some neural transcription factors, including engrailed Homeobox family members (EN1 and EN2) and POU-IV class members (POU4F1 and POU4F2), were coordinately elevated in HGR birds (Figure 6C and D). However, nearly half of escalated genes in HGR birds (14/29) belong to unknown genes. In that, some unknown genes, including ENSGALG00000054424, novel.1328 and novel.1329, showed undetectable expression levels in HGH and BGR birds (Figure 6B). They potentially serve as the key candidate genes involved in the positive regulation of aggressive behavior in birds.Figure 6 The protein network interaction and correlation analysis of genes related to aggressive behavior. (A) The known aggressive-related genes involved in serotonergic-dopaminergic systems. (B) Unknown genes specifically expressed in Henan gamecock. The enhanced transcription factors in the engrained family (C) and POU family (D). ** indicated padj value <0.01 in the RNA-Seq data. “P < 0.05″ indicated the difference between groups is considered significant based on the uncorrected P value. FPKM, Fragments Per Kilobase Million. (E) Protein network interaction analysis with known DEGs related to aggressive behavior, where interactions with a score ≥ 500 are presented. The color intensity of each target node represents the interaction score, with darker colors indicating higher scores. (F) Pearson correlation of the known and the potential genes associated with bird's aggressive behavior.

Figure 6

On the other hand, for the inhibited DEGs in HGR birds, many immune-related genes were involved, such as MHC-II, interferon (IFN), and cluster of differentiation (CD) molecule family members (Table S4). Thereinto, the neurological levels of many transcription factor family members, such as interferon-regulatory factors (IRF1, IRF4, IRF5, IRF7 and IRF9), Signal transducer and activator of transcription (STAT1 and STAT2), and Tubby family members (CX3CR1 and ENSGALG00000043947), as well as certain receptors like Fc Epsilon Receptor Ig (FCER1G) and Toll Like Receptor 1 family member B (TLR1B) (Table S4), were significantly reduced. In addition, Solute Carrier Family 2 Member 6 (SLC2A6), a transporter reported to affect the metabolic shift in macrophages without mediating glucose uptake (Maedera et al., 2019), was down-regulated in HGR (Table S2).

Considering the potentially key functions of these common neural DEGs, we further conducted the GO and KEGG enrichment analysis based on their expression patterns. It showed that elevated DEGs (29) in HGR birds were mainly enriched in pathways related to monoamine metabolism, such as tyrosine metabolism, amphetamine addiction, tryptophan metabolism, serotonergic synapse, dopaminergic synapse, and hindbrain development (Figure 5B, Table S5). However, the decreased DEGs (143) in HGR birds were mainly related to immune defense functions, such as type I interferon signaling, positive regulation of cytokine production, and primary immunodeficiency (Figure 5C, Table S5).

To predict the potential genes associated with aggressive behavior, we performed protein interaction analysis using known genes (TH, DDC, SLC18A2, TPH2) affecting animal aggression, as well as other DEGs (Set parameter score ≥ 500) (Figure 6E). It is predicted that Orthopedia (OTP), EN1, Vasoactive Intestinal Peptide (VIP), Dihydrofolate reductase (DHFR), and ENSGALG00000003466 genes might be involved in bird's aggressive behavior. Furthermore, the correlation analysis also revealed a strong correlation between these genes (Figure 6F).

Combined Analysis of Brain Transcriptome and Serum Metabolome

We combined the brain transcriptomics data with the serum metabolome data for joint KEGG analysis. It revealed that tyrosine metabolism and neuroactive ligand-receptor interaction were enriched in both HGR vs. HGH and HGR vs. BGR comparisons (Figures 7A and 7B).Figure 7 Joint analysis of KEGG pathways and network construction with multi-omics data. KEGG pathways collectively enriched by brain transcriptome and serum metabolome data in HGR vs. HGH (A) and HGR vs. BGR (B). (C) Constructed network diagram illustrating the interactions between known DEGs related with aggressive behavior and DEMs (| r | > 0.8). The red line represents positive correlation and the blue line represents negative correlation.

Figure 7

We conducted the correlation analysis between genes known to be related to animal aggressive behavior (TH, DDC, SLC18A2, TPH2) and all differential metabolites, and the parameter was set to Pearson correlation coefficient | r | > 0.80 (Figure 7C). It showed (Figure 7C) that SLC18A2 was negatively correlated with several carnitine metabolites (belonging to FA), while cGMP showed a negative correlation with known aggression-related genes, including DDC, SLC18A2, and TPH2. In addition, TH was negatively correlated with S-(Methyl) glutathione, TPH2 was negatively correlated with N-Caffeoyl Putrescine and positively correlated with ATR, and DDC was negatively correlated with 12-Hydroxystearic acid (a metabolite belonging to FA).

DISCUSSION

In this study, we constructed a backcross population to narrow down the candidate interval for affecting the aggressive behavior of Henan gamecock. Some common brain DEGs and serum DEMs shared by HGR vs. BGR and HGR vs. HGH comparisons were identified through brain transcriptomics and serum metabolomics. Unexpectedly, all these neural DEGs and serum DEMs were modulated in HGR following either an HGH < HGR > BGR or HGH > HGR < BGR pattern. It seems that long-term selection enhances the aggression of Henan gamecocks through prompting the neural serotonergic-dopaminergic signaling pathway, while simultaneously impairing neuro-immune function extensively. These common genes/metabolites, which are either elevated or reduced in HGR birds, may be involved in affecting the aggressive behaviors of birds through certain pathways.

Activated Brain Dopaminergic-Serotonergic Systems in HGR Birds

Genes that are dominantly or even specifically expressed in the brain tissue of Henan gamecocks, are the potentially important candidates for enhancing the cocks’ aggressiveness. Serotonergic and dopaminergic metabolic processes are classical signaling pathways that influence aggressive behavior across species (Komiyama et al., 2014; Takahashi and Miczek 2014; Komiyama et al., 2020; Smagin et al., 2022). Our research revealed that genes involving in the synthesis of both serotonin and dopamine (DDC, TH, TPH1 and TPH2) (Portaro et al., 2018; Kastner et al., 2019; Li et al., 2020), as well as the amine neurotransmitter transporter SLC18A2 (Vergo et al., 2007), were dramatically escalated in HGR birds (Figure 6A, Table S2). In addition, searching the transcriptome data presented by Ren et al. (Ren et al., 2023), it was found that these genes (including TH, DDC, SLC18A2, EN2 and EN1) were also elevated in the hypothalamus of adult Luxi gamecocks (compared to adult male RIR). It suggested that the neurological dopaminergic signaling and serotonergic signaling are simultaneously enhanced in gamecocks in a universal manner, potentially under the coordinate induction of engrailed family members (EN1 and EN2) (Kouwenhoven et al., 2016) (Alves dos Santos and Smidt 2011)and POU-IV members (POU4F1 and POU4F2). Transcription factors EN1 and EN2 have been described to crucially regulate differentiation of dopaminergic and serotonergic neurons during vertebrate central nervous system development (Tripathi and Bozzi 2015; Kouwenhoven et al., 2016). POU4 family transcription factors were reported to play critical roles in the development and functioning of the nervous system, such as retinal ganglion cell (Huang et al., 2014; Kiyama et al., 2023). More in-depth and specific studies are needed to establish a clear relationship between them.

In addition, Zhou et al. reported that some neurological transporters and receptors involved in the serotonergic and dopaminergic signaling pathway, including solute carrier family 6 member 4 (SLC6A4), dopamine receptor D1 (DRD1) and D2 (DRD2), and adrenoceptor alpha 2A (ADRA2A), were elevated in the midbrain of Luxi game chickens (Zhou et al., 2023). Komiyama et al. reported that the genetic differentiation value of dopamine receptor DRD4 gene was distinctively larger than that of other dopamine receptor genes in Japanese Shamo game birds(Komiyama et al., 2014), and their midbrain levels of norepinephrine were significantly higher (Komiyama et al., 2014; Komiyama et al., 2020). The research indicates that there is a relatively conserved pathway among gamecock breeds to augment their aggression through the activation of dopaminergic-serotonergic systems in the brain.

However, the function of most escalated genes in the Henan gamecock remains unknown, and many are indeed novel genes. They may enhance animal aggression through undiscovered pathways. Therefore, further research is required to elucidate the roles of these unidentified genes in the regulation of aggressive behavior in birds.

Dysregulation of Neuroimmune System in Henan Gamecock

Our brain transcriptomics revealed that many genes related to neuroimmune function, especially those involved in the type I IFN signaling cascade and cytokine production regulation (Figure 5C), were seriously impaired in Henan gamecocks. Therein, multiple neural transcription factors involved in the regulation of IFN-mediated signaling were inhibited in Henan gamecocks, such as IRFs and STAT family members. It seems that Henan gamecocks enhance their aggressiveness at the expense of dysregulated neuroimmune function. It was well known that type I IFNs play essential roles in establishing and modulating host defense against microbial infections under the regulation of the IRF family (Chen et al., 2017; Negishi et al., 2018). IRFs act as the master regulators of Toll-like receptor and cytosolic pattern-recognition receptor signaling (Honda and Taniguchi 2006). STAT1 and STAT2 proteins could serve as the key mediators of type I and type III IFN signaling and are essential components of the cellular antiviral response and adaptive immunity (Negishi et al., 2018; Zuo et al., 2020).

Animal aggression behavior could be considered as a form of neurological disorder. Increasing evidence suggests that there is an important relationship between neurological disorders/animal aggression behavior and the immune system across species (Takahashi et al., 2018). Whole genomic resequencing revealed that Toll-like receptor cascade was significantly modulated in Luxi gamecocks (Zhou et al., 2023). The copy number of SOCS2 gene, a member of the suppressor of cytokine signaling (SOCS) family, was increased in several Chinese gamecocks (Bi et al., 2017). It was reported that immunotherapy with interferon alpha (IFN-α) could lead to increased irritability and feelings of anger/hostility in some patients (Lotrich et al., 2013). IRF1 and IRF2 could act as antagonists to regulate the transcription of DRD2 which is associated with aggressive behavior in weaned pigs (Zhao et al., 2022) . Genetic variants in the human leukocyte antigen and killer cell immunoglobulin-like receptor regions have been associated with many brain-related diseases and neurological development (Romagnoli et al., 2020; Bian et al., 2022). In addition, brain cytokines could act as neuromodulators to regulate neuronal plasticity and transmission. Animals with high aggression showed heightened proinflammatory cytokines levels (Takahashi et al., 2018). Cytokines IL-1 and IL-2 could modulate the defensive rage behavior through 5-HT2 receptors and GABA(A) receptors respectively (Zalcman and Siegel 2006).

The immune system consists of a complicated network responsible for maintaining the body's homeostasis and responding to aggression in general (Cruvinel Wde et al., 2010). Brain neuroinflammation is a critical factor in the progression of neurodegenerative diseases (Dexter and Jenner 2013). Infection and immune response can activate immunoreactive cells in the brain (Zalcman and Siegel 2006; Khandaker and Jones 2011; Williamson et al., 2011), impacting neuronal signal transduction and potentially influencing behavior and cognition (Chen et al., 2008; Bland et al., 2010).

Serum DEMs Specifically Modulated in HGR Were the Potential Indicator for Bird Aggression

Serum metabolomics has been widely applied to explore the metabolites and associated metabolic pathways in organisms, thereby discovering biomarkers or sensitive indicators for detecting certain pathophysiological activities (Wishart 2019). Our metabolomic profiling showed that several serum metabolites were specifically modulated in Henan gamecocks. These metabolites may play a role in mediating aggressive behavior in birds and hold the potential as biomarkers reflecting bird aggression. Tyramine is a naturally occurring trace amine, it is derived from tyrosine via the action of tyrosine decarboxylase and is physiologically metabolized by monoamine oxidases (Anwar et al., 2012; Roeder 2020). It also participates in the synthesis of dopamine, the key neurotransmitters affecting animal aggression (Hiroi et al., 1998; Komiyama et al., 2014). In addition, tyramine itself has been found to play a role in behavioral and motor functions in invertebrates (Roeder 2020). In our research, the reduced level of serum tyramine in Henan gamecock was accompanied with the increased expression level of neurological genes involving in dopamine synthesis (TH, DDC) and transport (SLC18A2) (Figure 7C). Considering the potential importance of tyrosine and tyrosine metabolism on animal aggressive traits, we further investigated the tyrosine-related metabolites in our serum metabolomics data. It was found that the levels of serum Cyclo (Tyr-Ala) and Cyclo (Tyr-Leu) were elevated in Henan gamecocks overall, especially relative to HGH birds (Table 2). These observations suggest a potential role of tyrosine-related metabolites in modulating bird aggression.

The cGMP is recognized as a vital second messenger involved in numerous crucial signaling cascades, such as olfaction, phototransduction, calcium homeostasis, and vasodilation (Jehle and Garaschuk 2022). Additionally, it contributes to regulating the efficacy of neurotransmitter release in the striatum (Fieblinger et al., 2022). Experiments involving dibutyryl cGMP infusions in rats and mice have demonstrated dose-dependent increases in brain cGMP, correlating with both the facilitation and inhibition of aggressive behavior (Kantak et al., 1981). In our study, a decrease was observed in the levels of serum cGMP in Henan gamecock, which displayed a prominent negative correlation with well-known aggression-related genes (Figure 7C). These findings imply that cGMP could influence the aggression of Henan gamecocks by playing a part in regulating the neural dopaminergic-serotonergic pathway (Wong et al., 2012). It has also been noted that the inhibition of dopamine signaling suppresses cGMP accumulation in rd1 retinal organ cultures(Zhang et al., 2014). Moreover, cGMP can ameliorate the inflammatory response to lipopolysaccharide stimulation and augment the expression of CD11, MHC II, and pro-inflammatory cytokines (Jehle and Garaschuk 2022).

Carnitine, a well-documented facilitator of energy production and fat metabolism, has been implied in modulating behavior patterns (Virmani and Cirulli 2022). Our researches showed that circulating Carnitine C18:2-OH and Carnitine C8:1 seems to be the negative indicator of aggression in birds, and SCL18A2 may not only serve as a monoamine transporter but also partake in the transportation of other substances, including various carnitines (Figure 7C). Interestingly, analogous associations between lowered carnitine concentrations and increased aggression tendencies have been reported in humans as well. Notably, among patients with neurodevelopmental disorders like autism spectrum disorder, aggression and anxiety are prevalent complications. Supporting this, carnitine deficiencies have been detected in some children with autism. Moreover, carnitine supplementation has been evidenced to alleviate attention concerns and aggressive demeanor in boys diagnosed with attention-deficit hyperactivity disorder (Van Oudheusden and Scholte 2002; Goin-Kochel et al., 2019; Aslan et al., 2021). Another prominent aspect of carnitine's beneficial influence is exhibited in its neuroprotective capabilities. Acetyl-l-carnitine has been found to prevent neuronal loss and enhance memory functions by upregulating dopamine receptor DRD1 and mitigating microglial activation in Parkinsonian (Singh et al., 2018).

Overall, it seems that there is a conservative mechanism for the aggression behavior across animals, including humans. The relevant research in Henan gamecock model may eventually help us to understand the neurogenetic architecture of aggression in animals. It's important to underscore that, owing to the treasure of Henan chicken resources, our study utilizes a comparatively compact sample size. This fact could potentially introduce an element of bias. However, we have taken stringent measures to mitigate this potential limitation. To minimize background interference, we employed birds hatched from the same batch and implemented a double comparison strategy. These diligent steps ensure the relative reliability of our data, despite the smaller sample size.

CONCLUSIONS

A comprehensive analysis of multi-omics data between HGR and HGH/BGR has shed light on the distinct modulation of several serum metabolites and a multitude of neural transcripts in Henan gamecocks. Our findings suggest that long-term selective breeding has enhanced the aggression of Henan gamecocks by accelerating the dopamine-serotonin metabolic process in the brain. This enhancement appears to occur simultaneously with a weakening of their neuroimmune system at the transcriptional level, and it impacts the circulating levels of certain serum substances. The elevated genes detected in the brains of HGR birds may serve as the key facilitators of aggression in bird species. Likewise, these modulated circulating metabolites in the serum, including cGMP and tyramine, may function as secondary messengers in the regulation of avian aggression, potentially serving as biomarkers for aggression monitoring. However, the functions of many identified genes/metabolites remain elusive, necessitating further investigation to elucidate their specific roles.

Appendix Supplementary materials

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ACKNOWLEDGMENTS

This work was supported by Key Natural Science Foundation of Henan Province (232300421109 ).

Author Contributions: Chuanchen Su performed the experimental assays, analyzed genomic data and drafted the manuscript. Chuanchen Su and Lin Zhang generated and analyzed omics data. Yuxian Pan provided technical support, Jingya Jiao and Pengna Luo provided statistical support. Xinghai Chang contributed to the animal experiment design and sample provision. Huaiyong Zhang and Xuemeng Si contributed to writing-review and editing. Yanqun Huang and Wen Chen commonly designed and supervised the study and edited the manuscript. All authors have read and approved the final manuscript.

Ethics: The experiment was performed in accordance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Henan Agricultural University (Permit Number: 12-1328; Date: 05-2021).

Data Availability: RNA-seq data have been deposited in SRA database under accession number PRJNA1084891, PRJNA1083292 and PRJNA1083308. All other data generated or analyzed during this study are included in the manuscript and supporting files.

DISCLOSURES

The authors declare no conflicts of interest.

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2024.104055.
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