Novel insights into the transcriptomic changes of the hypothalamus in broilers exposed to high-density stocking

Novel insights into the transcriptomic changes of the hypothalamus in broilers exposed to high-density stocking | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Novel insights into the transcriptomic changes of the hypothalamus in broilers exposed to high-density stocking Dongying Bai, Yi Zhang, Xianglong He, Yanli Wang, Bo Zheng, Xueqing Xiao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7091754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The prevalence of high stocking density ( HD ) in the broiler industry has been significantly increased, exerting profound implications for the physiology, behavior, and welfare of chickens, particularly concerning the regulation of their nervous system, specifically the central nervous system. Conversely, the impact of HD stress on the neurophysiological function of the broiler hypothalamus remained unknown. Results This study was conducted to investigate the effects of varying stocking densities on growth performance, serum biochemistry, and hypothalamic transcriptome in chickens across distinct developmental stages, specifically at 21, 28, 35, and 42 days of age. Two density conditions were utilized: normal density (ND) at 14 birds per square meter (m²) and high density (HD) at 22 birds per square meter (m²). Results indicated that no significant differences in growth performance were observed during the initial 21 days; however, significant reductions in body weight and feed intake were observed in the HD group from days 22 to 42. At 35 and 42 days of age, serum concentrations of IL-4 and TNF-α were significantly elevated in the HD group compared to the ND group. Gene mapping success rates were observed to range from approximately 37.44–92.93%. A total of 42.48 million raw sequencing reads were generated per sample. Significant differential enrichment in KEGG pathways was detected between the ND and HD groups on days 21, 28, 35, and 42. Significant alterations were identified across several key signaling pathways, including pyruvate metabolism; oxidative phosphorylation; alpha-linolenic acid metabolism; neuroactive ligand-receptor interactions; Wnt signaling; Notch signaling; and apoptosis signaling. Candidate genes including SMAD3 , PPY , SLCO1C1 , Wnt16 , and NMU were identified as critical for central nervous system immunity. Furthermore, NPY, STAT2, SLC26A3 , and ISL2 regulate feeding behavior. Conclusions These findings provide critical insights for investigating the effects of HD stress on broiler growth performance, serum biochemical parameters, and hypothalamic transcriptome. High-density stocking Hypothalamus Transcriptomic Growth performance Broiler Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The rising demand for poultry stimulated the development of highly productive chicken populations, resulting in a substantial enhancement of their productivity levels [ 27 ]. Within the poultry industry, awareness concerning avian health and welfare issues has markedly increased. This heightened awareness is particularly evident in poultry farming practices, owing to the significant impact of stocking density on the level of environmental factors to which birds are consistently exposed [ 37 ]. Nonetheless, the preservation and enhancement of poultry welfare was dependent upon the maintenance of appropriate stocking densities. However, high stocking density ( HD ) was frequently employed to enhance profitability by maximizing the number of laying hens reared within a confined area [ 46 ]. According to current feeding guidelines that accounted for aspects including animal welfare, growth efficiency, and other relevant factors, a density of 16 broilers per square meter or 39 kilograms per square meter was considered acceptable [ 55 ]. Elevated levels of oxidative stress were frequently induced by early manifestations of HD, as indicated by our study [ 28 , 55 ] and corroborating research [ 13 , 20 , 52 ] from other researchers, subsequently resulting in compromised immune function, an aberrant metabolic response, and reduced growth performance. The hypothalamus was recognized as a critical component within the brain. It was positioned anterior to the pituitary gland and inferior to the thalamus. Crucially, it functioned as an essential interface between the nervous and endocrine systems, enabling coordinated interactions and communication between these complex physiological systems [ 1 , 39 ]. Furthermore, a number of physiological functions were regulated by the hypothalamus, including body temperature, thirst and hunger, sleep patterns, circadian rhythms, and fatigue [ 48 ]. Hypothalamic developmental disorders were observed to arise with advancing growth and sexual maturity. These abnormalities were also found to cause physiological and neurological changes, which were linked to the occurrence of disorders such as obesity, autism, infertility, depression, and chronic oxidative stress [ 4 ]. However, the monitoring of these fundamental metabolic processes in avian species was demonstrated to be essentially dependent on the hypothalamus. A well-documented example was the secretion of growth hormone (GH), a crucial growth factor, by the pituitary gland. This secretion was triggered in response to stimulation of somatoliberin (GHRH) secretion by the hypothalamus [ 31 , 51 ]. Consequently, the molecular activity of the hypothalamus was shown to be controlled by a range of external stimuli. The possible effects of increased HD-induced oxidative stress on the response and molecular activity of the avian hypothalamus were identified as requiring thorough investigation.. In the life sciences, particularly within the study of industrialized animals, high-throughput sequencing technology was recognized as a well-established field whose significance was being increasingly acknowledged [ 23 , 36 ]. One such instance was the successful identification of a genetic candidate potentially associated with the growth rate of broiler chickens, which was achieved using a novel high-throughput RNA sequencing methodology [ 10 ]. Examination of the hypothalamic transcriptome in a recent study revealed that mRNAs were critical for regulating the timing of gonadal development in chickens [ 39 ]. Furthermore, research employing transcriptome analysis demonstrated that heat stress activated several metabolic pathways. The advancement of effective genetic strategies to improve thermotolerance in chickens was considered highly feasible through the identification of these specific target genes [ 48 ]. RNA-sequencing analysis of pituitary and hypothalamus tissues was established as an important new tool for elucidating the genetic mechanisms underlying elevated egg production rates in laying hens [ 53 ]. Therefore, the utilization of high-throughput sequencing to achieve specific targets in the avian hypothalamus was confirmed as a reliable technique. The molecular alterations occurring in the hypothalamus of broiler chickens under HD conditions over time, along with potential contributing mechanisms (e.g., energy fluctuations, metabolic shifts, and maintenance of inflammatory homeostasis), were poorly characterized. Therefore, to comprehend the intricate dynamics governing the hypothalamus in broiler chickens subjected to HD and to illuminate underlying mechanisms, a comprehensive investigation was required. The primary objective of this study was to determine whether exposure to HD conditions affected hypothalamic gene expression and triggered critical molecular pathways. Materials and methods Ethical treatment The Experimental Animal Care and Utilization Committee of Henan University of Science and Technology granted approval for the experimental protocol used in this study (AW20602202-1-2), which was carried out in accordance with the experimental animal guidelines of the Ministry of Science and Technology (Beijing, China). Animals and experimental design A total of 238 healthy one-day-old Arbor Acres male broilers were procured from a commercial hatchery (Henan Quanda Poultry Breeding Co., Ltd., Hebi, China). The research was conducted at the Animal Research Unit of Henan University of Science and Technology. During the initial week of the experimental period, birds were maintained at a controlled temperature of 33°C ± 1°C. A gradual temperature reduction of 1–2°C per week was then implemented, resulting in a final temperature of 25°C ± 2°C by day 42. Relative humidity was consistently maintained within a range of 60–70%. Additionally, lighting was provided for 23 hours daily, with a one-hour dark period from 19:00 to 20:00. On day 7, chicken body weights were measured, and birds were allocated to two distinct treatment groups: one group with a standard stocking density (ND) of 14 birds per square meter, and the other with a high stocking density (HD) of 22 birds per square meter. As previously described, each treatment was composed of three preparations and five replications [ 28 , 55 ]. In accordance with National Research Council (NRC) guidelines, birds were fed a corn-soybean meal-based diet (Table 1 ) [ 35 ], excluding any additional substances except for anticoccidial treatment administration. Birds were housed under controlled temperature and humidity conditions with ad libitum access to feed and water. To ensure welfare, all experimental subjects were vaccinated against infectious bronchitis virus and Newcastle disease virus on days 1 and 20, and against bursitis virus on day 14. Table 1 Composition and nutrient levels of basal (CON) diet (as-fed basis) %. Ingredients Starter phase Finisher phase Corn (8.7% CP) 52.79 57.78 Soybean meal (41% CP) 36.89 30.08 Soybean oil 4.02 4.37 Limestone 1.12 1.28 Sodium chloride 0.30 0.30 Choline chloride 0.30 0.26 Vitamin premix 1 0.03 0.03 Mineral premix 2 0.21 0.21 Stone powder 1.22 1.17 Dicalcium phosphate 1.71 1.62 DL-Methionine (99%) 0.21 0.12 L-Lysine (99%) 0.21 0.12 L-Arginine (99%) 0.02 - Calculated nutrient levels (%) Metabolizable energy(MJ/kg) 12.40 12.91 Crude protein 21.10 19.82 Lysine 1.24 1.09 Methionine 0.56 0.48 Calcium 0.91 0.91 Available P 0.45 0.42 Total P 0.69 0.63 Threonine 0.85 0.78 Arginine 1.39 1.21 1 Vitamin premix provided the following per kilogram of diet: VA (retinyl acetate) 10,000 IU, VD3 (cholecalciferol) 2,000 IU, VE (DL-a-tocopheryl acetate) 11.0 IU, VK 1.0 mg, VB1 1.2 mg, VB2 5.8 mg, VB6 2.6 mg, VB12 0.012 mg, niacin 66.0 mg, pantothenic acid (calcium pantothenate) 10.0 mg, biotin 0.20 mg, folic acid 0.70 mg. 2 Mineral premix provided the following per kilogram of diet: Mg 100 mg, Zn 75 mg, Fe 80 mg, I 0.65 mg, Cu 8.0 mg, Se 0.35 mg. Growth Performance and Sample Collection Throughout the experimental period, body weights of each broiler group were measured and recorded on days 21, 28, 35, and 42. Additionally, body weight (BW), average daily gain (ADG), and average feed intake (AFI) were assessed, and the feed conversion ratio (FCR) was calculated for the entire study duration. Six broilers were randomly selected from each experimental group at days 21, 28, 35, and 42. Wing vein puncture was employed to collect blood samples. After centrifugation at 3000 × g for 30 min at 4°C, serum was separated and immediately frozen at − 20°C for subsequent analysis. Subsequently, broilers (n = 4) with comparable body weights were euthanized via cervical dislocation, and brains were rapidly excised. The hypothalamus was dissected, snap-frozen in liquid nitrogen, and stored at − 80°C for further analysis. Serum Biochemical and Immunity Parameters Serum cortisol (CORT) and insulin-like growth factor I (IGF-I) concentrations were quantified on days 21, 28, 35, and 42 using chicken-specific enzyme immunoassay (ELISA) kits (Northern Institute of Biotechnology Co., Ltd., Beijing, China), respectively. Furthermore, serum levels of tumor necrosis factor-alpha (TNF-α), interleukin-1β (IL-1β), interleukin-4 (IL-4), and interleukin-10 (IL-10) were determined via ELISA. Kits specifically validated for chicken samples were employed, and all assays were performed in strict accordance with the manufacturer's protocols. Essential reagents were procured from Nanjing JianCheng Bioengineering Institute Co., Ltd. (Nanjing, China). RNA Isolation and cDNA Library RNA extraction from hypothalamic samples was conducted utilizing TRIzol reagent (Invitrogen, Carlsbad, CA). RNA concentration and purity were quantified, and library quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Following cDNA synthesis, mRNA was enriched using oligomagnetic beads. Subsequent library construction involved PCR amplification, yielding a library of approximately 450 base pairs. Upon completion of RNA extraction and library preparation, paired-end sequencing was performed on the Illumina platform at Personal Biotechnology Co., Ltd. (Biographical Notes, Shanghai, China). mRNA Sequencing and Data Analysis Raw sequencing data were filtered and subjected to quality control procedures, following which Q20, Q30, and GC content metrics—collectively defining the clean data—were calculated for the valid data. This clean data was subsequently aligned against the Gallus gallus (chicken) reference genome ( https://www.ncbi.nlm.nih.gov/ ) using HISAT2 software. The alignment process was conducted to evaluate sequence alignment efficiency and to map sequence information to the reference genome. Differential expression analysis was performed using DESeq2 software, employing thresholds of |log2(fold change)| ≥ 1 and an adjusted P-value < 0.05. Genes meeting both criteria were considered differentially expressed. Enrichment analysis for these genes was then conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases via R scripts. Fisher's exact test, combined with Bonferroni correction for multiple testing, was applied to control the false discovery rate. Pathways with a corrected P-value < 0.05 were considered significantly enriched. Verification of RNA-Seq with qRT-PCR The procedure for RNA extraction and real-time PCR was performed according to established methodologies [ 28 ]. Briefly, total RNA was extracted from hypothalamic tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's protocol. RNA purity and concentration were assessed by spectrophotometry using a NanoDrop 2000 instrument. Complementary DNA (cDNA) synthesis was accomplished using the PrimeScript™ RT reagent kit with gDNA Eraser (Takara Biotechnology Co., Ltd., Tokyo, Japan). The primers employed to amplify target genes are detailed in Table 2 . Relative gene expression levels were quantified using the 2 −ΔΔCT method, with GAPDH serving as the reference gene for normalization. Table 2 List of primers used for RT-PCR. Target Gene Forward primer Reverse primer 1 Accession number GHRH AGAATGCAACATCAGCTGGGA TGTAATTTCTGGGGCAGCGT NM_001201396.1 NMU AGTGATCATCAGCAGCGAGG CAGGGTCACAACTGATTTGCAG NM_001292045.2 WNT16 CCGACGACATCCACTATGGG TCAGCTTTGCTACAGCCTGC XM_040658113.2 NPY GTGCTGACTTTCGCCTTGTC GGGTCTTCAAACCGGGATCT NM_205473.2 AQP5 GGCCATTTGGTGGGGATCTA AGTACAGCAGAGCAGCCAAG XM_040693813.2 SLC26A3 CACCTACCCCAAGGGTGAAC ACACGGATGTCATGGTTGCT NM_000111.3 PTGS2 CTGCGATTTCGAGCGCATTT CCACGTGAAGAATTCCGGTGT NM_001167719.2 SMAD3 CTCCTGGGCTGGAAGAAAGG ATCCAGCGACCTGGGGAT XM_046935038.1 STAT2 CTGTCAGAGTACCCAGCGTG ATCCCCGTTTTGCGTCTTTTG XM_046934948.1 TLR4 TGGATCTTTCAAGGTGCCACA AGTGTCCGATGGGTAGGTCA NM_001030693.2 β-Actin TGCGTGACATCAAGGAGAAG TGCCAGGGTACATTGTGGTA NM_205518 1 Accession number refers to Gene bank (NCBI). Growth hormone releasing hormone (GHRH); Neuromedin U (NMU); Wnt family member 16 (WNT16); Neuropeptide Y (NPY); Aquaporin 5 (AQP5); Solute carrier family 26 member 3 (SLC26A3); Prostaglandin-D synthase (PTGS2); Smad3 family member 3 (SMAD3); Signal transducer and activator of transcription 2 (STAT2); Toll-like receptor 4 (TLR4). Statistical Analysis Individual animals within each replicate were designated as experimental units for the analysis in this study. Additionally, each cage replicate was regarded as an experimental unit for evaluating growth performance and assessing serum biochemical indices. Data were reported as mean ± standard error of the mean (SEM) using SAS version 8.1 (SAS Institute Inc., Cary, NC). Mean separation was conducted using a t-test, and a statistically significant difference was indicated at a significance level of P < 0.05. Results Production Performance of Broiler Chickens Data detailing broiler production performance appear in Table 3 . No significant effects on broiler body weight, body weight gain, feed intake, or feed conversion ratio were detected between the ND and HD groups during the initial 21-day period ( P > 0.05). From day 22 to day 42, however, the HD group showed a significant decrease in both body weight and feed intake relative to the ND group ( P < 0.05). Conversely, the HD group demonstrated a significant increase in the feed conversion ratio compared to the ND group during this later phase ( P < 0.05). Table 3 Production performance of broiler chickens. Parameters 1 Experimental groups P -value ND HD 1-21d Body weight (g/bird) 654.73 ± 4.98 634.22 ± 2.41 0.38 Body weight gain (g/bird) 41.02 ± 5.02 39.57 ± 7.15 0.13 Feed intake (g/bird) 31.18 ± 3.11 30.20 ± 4.27 0.38 Feed conversion ratio (g/g) 1.32 ± 0.02 1.31 ± 0.08 0.49 22-42d Body weight (g/bird) 2527.83 ± 25.34 a 2325.28 ± 20.52 b <0.01 Body weight gain (g/bird) 153.61 ± 11.08 150.95 ± 13.59 0.18 Feed intake (g/bird) 89.20 ± 8.13 a 80.53 ± 9.42 b <0.01 Feed conversion ratio (g/g) 1.72 ± 0.04 b 1.86 ± 0.02 a <0.01 1 ND: normal stocking density; HD: high stocking density. a,b Different letters indicate a significant effect of treatment at P < 0.01. Serum Biochemical and Immunity Parameters Broiler serum biochemical and immunological parameters were presented in Fig. 1 . On day 21, a statistically significant increase ( P < 0.05) in IL-10 concentration was observed exclusively in the HD group compared to the ND group. On day 28, a significant higher concentration of IGF-1 was measured in the ND broiler group relative to the HD group ( P < 0.05). At 35 days of age, significant elevated concentrations of TNF-α, IL-4, and IL-10 were detected in the HD group compared to the ND group ( P < 0.05). Furthermore, at 42 days of age, significant increased levels of CORT, TNF-α, IL-1β, and IL-4 were recorded in the HD group relative to the ND group ( P < 0.05). RNA-Sequencing Analysis Quality assessment was performed on total RNA extracted from the hypothalamus of broilers at 21, 28, 35, and 42 days of age. The results were documented in Table S1 . An average read count of 42.48 million was yielded per sample from hypothalamic sequencing. The average percentage of bases with quality scores exceeding Q20 and Q30 across all hypothalamic samples was observed to be 97.31% and 92.90%, respectively. Subsequent filtering of the RNA-Seq data was conducted, and high-quality sequences, designated as clean reads, were identified. Post-filtering, an average of 40.28 million clean reads per chicken hypothalamic sample was determined. These clean reads were aligned to the chicken reference genome, and the alignment rate was calculated for each sample. An average of 37.44 million sequences matching the reference genome was demonstrated, corresponding to an average alignment rate of 92.93%. Notably, among these aligned sequences, an average of 36.73 million were aligned uniquely to one position, accounting for 98.12% of the total. The high quality of the sequencing data from chicken hypothalamic samples across all ages was confirmed, and the data were rendered suitable for subsequent alignment analysis. Differentially Expressed Genes In this study, separate analyses were conducted sequentially at 21, 28, 35, and 42 days of age based on sequencing results obtained at these four time points. At 21 days, principal component analysis (PCA) score plots revealed distinct clustering of ND and HD samples based on hypothalamic transcriptomes (Fig. 2 A). Similarly, PCA results at 28 (Fig. 3 A), 35 (Fig. 4 A), and 42 days (Fig. 5 A) demonstrated separation of ND and HD groups into discrete clusters when considering hypothalamic chicken transcriptomes. Hierarchical cluster analysis of differentially expressed genes ( DEGs ) at 21, 28, 35, and 42 days (Fig. 2 - 5 B) further indicated that samples within the same experimental group exhibited pronounced clustering tendencies. This group-specific clustering pattern was visually evident in heatmap representations, which effectively delineated divergent gene expression profiles between ND and HD groups. At day 21, hypothalamic analysis identified 45 DEGs (26 upregulated, 19 downregulated) between ND and HD groups (Fig. 2 C, Table S2 ). By day 28, 51 DEGs were detected (22 upregulated, 29 downregulated; Fig. 3 C, Table S3 ). During the 35-day assessment, 81 DEGs were identified (13 upregulated, 68 downregulated; Fig. 4 C, Table S4 ). Finally, comprehensive analysis at 42 days revealed 34 hypothalamic DEGs (14 upregulated, 20 downregulated) distinguishing ND and HD groups (Fig. 5 C, Table S5 ). Analysis of the GO and KEGG Enrichment for DEGs The Gene Ontology ( GO ) was developed as a thorough database by the Gene Ontology Consortium to classify gene functions into cellular components ( CC ), molecular functions ( MF ), and biological processes ( BP ). Enrichment associated with days 21, 28, 35, and 42 was demonstrated by comparing the ND and HD groups and performing GO analysis of the hypothalamic DEGs (Fig. 2 - 5 D). On day 21, GO annotations in the hypothalamus of broiler chickens from the ND and HD groups predominantly involved BP and MF, including the neuropeptide signaling pathway, neuropeptide receptor binding, opsonin receptor activity, complement component C5a signaling, regulation of TRAIL production, and neuromeric U-receptor binding. The GO annotations of the hypothalamus in broilers from the ND and HD groups at 28 days illustrated processes such as zymogen activation, substantia nigra development, serine-type endopeptidase activity, serine hydrolase activity, and cis-trans isomerase activity. Furthermore, host biological processes including DNA integration, cell aggregation, endonuclease activity, asparagine-type endopeptidase activity, and aspartic-type peptidase activity were displayed in the hypothalamus GO annotations of 35-day-old broiler chickens from the ND and HD groups. Additionally, distinct biological processes were revealed by GO annotations in the hypothalamus of 42-day-old broiler chickens from the ND and HD groups, encompassing NK-T cell proliferation, control over hepatocyte differentiation, positive regulation of cell-cell adhesion, regulation of protein O-linked glycosylation, extrathymic T cell differentiation, and chloride transmembrane transporter activity. Enrichment analysis of the KEGG database was conducted to elucidate the biological functions and gene interactions of DEGs. Broiler chickens in the ND and HD groups exhibited enhanced KEGG signaling pathways associated with DEGs in the hypothalamus across four developmental stages (Fig. 2 – 5 E). At 21 days of age, 34 DEGs demonstrated notable enrichment within two KEGG signaling pathways: the neuroactive ligand-receptor interaction pathway and the cytokine-cytokine receptor interaction pathway. Genes implicated in the neuroactive ligand-receptor interaction pathway included NMU , PY , and PRL (upregulated), while the cytokine-cytokine receptor interaction pathway involved PRL and TNFRSF8 (upregulated). At 28 days of age, significant enrichment of 51 DEGs was observed across five KEGG pathways: linoleic acid metabolism, alpha-linoleic acid metabolism, intestinal immune network for IgA production, and arachidonic acid metabolism. Key genes participating in these pathways comprised PLA2G2A and ICOS (upregulated). At 35 days of age, significant enrichment of 81 DEGs occurred within four KEGG signaling pathways: folate metabolism, tyrosine metabolism, oxidative phosphorylation, and the Notch signaling pathway. At 42 days of age, significant enrichment of 45 DEGs was detected within eight KEGG pathways, encompassing tyrosine metabolism, intestinal immune network for IgA production, retinol metabolism, cytochrome P450, fatty acid degradation, pyruvate metabolism, glycolysis/gluconeogenesis, and the apoptosis signaling pathway. Identification of DGEs across different developmental days An initial comprehensive evaluation of hypothalamic transcriptomes was conducted at 21, 28, 35, and 42 days post-hatch (Fig. 6 ). Principal Component Analysis ( PCA ) score plots were employed to classify the four age groups into distinct clusters based on hypothalamic transcriptome profiles (Fig. 6 A). Comparative analyses were performed between 21-day-old chickens and each of the other age groups. Additionally, a comprehensive overview of the gene circle map was generated (Fig. 6 B), with the outermost circle dedicated to chromosome bands and subsequent circles illustrating the results of differential expression analyses. DGEs analysis between 21- and 28-day hypothalamic samples revealed 56 upregulated genes and 24 downregulated genes. Comparison of the 21-day group with the 35-day group identified 24 upregulated genes and 41 downregulated genes. Analysis of the 21-day versus 42-day groups showed 54 upregulated genes and 38 downregulated genes (Fig. 6 C). Furthermore, a thorough investigation was carried out using a Venn diagram (Fig. 6 D), which identified an intersection of six genes common to the comparisons: TMOD1 , ALPK1 , UMODL1 , HBAD , FYB2 , and ENSGALG00000049341 . Identification of Candidate Gene To validate the expression levels of DEGs identified through RNA-seq analysis, selected upregulated genes ( NMU , NPY , PTGS2 , STAT2 , and GHRH ) and downregulated genes ( AQP5 , WNT16 , SMAD3 , TLR4 , and SLC26A3 ) were subjected to quantitative real-time polymerase chain reaction (qRT-PCR) validation, as illustrated in Fig. 7 A. A concordant regulatory pattern in the expression profiles of these genes was demonstrated by the qPCR results. A significant correlation was revealed through statistical analyses, including determination of the Pearson correlation coefficient (r = 0.9603) and linear regression, between the expression levels measured by qRT-PCR and RNA-Seq methodologies (Fig. 7 B). Discussion Broilers were housed at elevated densities to optimize cage space utilization and boost production efficiency. However, detrimental effects of high density ( HD ) on broilers, such as hindered growth and compromised physiological well-being, were documented by accumulating research [ 8 ]. Birds reared under HD conditions were noted to display significant adverse impacts on feed intake, body weight, and feed conversion ratio ( FCR ) relative to normal density ( ND ). Reductions in body weight and FCR were identified when stocking density exceeded 20 broilers/m² [ 9 ]. HD stress was recognized as a key contributor to diminished production performance [ 34 ]. At increased stocking densities, the birds' living environment was transformed through decreased available space per individual. This spatial limitation was associated with restricted daily behaviors, particularly mobility [ 28 ]. Overcrowding was evidenced to intensify with rising density. Foraging efficiency and feed accessibility were impeded by limited space [ 14 ]. No significant variations in growth performance were observed during the initial 21 days. Conversely, notable differences in body weight, feed intake, and FCR were detected from day 22 to 42. This outcome aligned with prior studies [ 26 , 28 ], implying that density stress emergence coincided with rapid growth. Thus, the focus on the 21- to 42-day interval in ensuing analyses was founded on this evidence. Blood biochemical indices were utilized as critical diagnostic tools for metabolic disorder identification in broilers [ 34 ]. Avian blood parameters were altered by HD through thermoregulatory mechanisms, inducing stress that impaired performance and physiological indicators [ 9 ]. A density-dependent increase in corticosterone ( CORT ) was corroborated by our data. Significantly elevated CORT levels were observed in HD versus ND groups at 42 days, though no differences were detected at 21, 28, or 35 days. While considered suboptimal for comprehensive welfare assessment [ 6 ], CORT was retained as valuable for chronic stress evaluation. Elevated CORT was associated with hyperglycemia induction, serum protein reduction, and immunity suppression, collectively impairing performance [ 24 , 29 ]. A significant serum IGF-1 reduction was documented in HD groups at 28 days versus ND, with no differences observed at other intervals. This pleiotropic factor was recognized to regulate tissue growth, modulate metabolism, and exhibit protective properties [ 11 , 42 ]. The 28-day reduction was noted to coincide with accelerated broiler growth, indicating heightened developmental sensitivity to density stress. Immune cell function was compromised by stress, delaying cytokine signaling. Inflammation was initiated by TNF-α and IL-1β, while anti-inflammatory effects were exerted by IL-4 and IL-10 [ 45 ]. Significantly elevated TNF-α and IL-1β levels were recorded in HD groups at 35 and 42 days, indicating sustained systemic immune impact during late growth and aligning with prior density studies [ 26 , 45 ]. Concurrent elevations in IL-4 and IL-10 suggested active inflammatory homeostasis regulation.. Regarding molecular analysis, hypothalamic transcriptome comparisons between HD and ND reared broilers were conducted across four distinct developmental time points. This investigation was identified as the first molecular-level examination of the hypothalamus at these specific ages. Transcriptomic data reliability was unaffected by individual variability or limited sample size. A distinct pattern of DEGs was observed in the transcriptomic results, exhibiting an initial increase followed by a decrease across the four intervals, with counts of 45, 51, 81, and 34 DEGs respectively. This pattern indicated that a higher number of DEGs were detected in the hypothalamus, dependent on stocking density and rearing duration, and that these DEGs demonstrated increased activity from 35 days of age under HD stress conditions. AA broilers, analogous to Ross 308 broilers, were classified as a fast-growing commercial chicken breed, exhibiting growth rates at 30–40 days of age that exceeded those of other commercial lines by 10–15% [ 39 ]. These observations suggested that density stress was more pronounced during periods of rapid growth and was reflected in hypothalamic molecular expression profiles. To obtain a comprehensive understanding of the biological functions of these DEGs, GO annotation and KEGG pathway analyses were performed. Several hypothalamic DEGs were associated with biological processes and molecular functions, with particular emphasis on cellular components. Activated GO terms related to brain development, serotonin transporter activity, cellular processes, epithelial development, neurogenesis, grooming behavior, and central nervous system development were identified at each of the four time points between the ND and HD groups. However, significantly fewer common GO terms were observed between the ND group and the other three age groups compared to the 21-day cohort. Temporal differences in hypothalamic growth and developmental processes at different stages were suggested by the GO terms in both BP and MF categories. Dynamic temporal variations in hypothalamic physiological activity over time were also indicated. These findings were found to differ from previous experimental results [ 39 , 48 ], a discrepancy that was potentially attributable to methodological variations, as no extended temporal molecular analyses of the hypothalamus were conducted in the cited studies.. Comparative analysis of KEGG signaling pathways across four postnatal time points under two stocking densities revealed temporally specific enrichment patterns. KEGG pathway enrichment analysis in 21-day-old HD and ND groups identified 14 significantly enriched pathways. The cytokine-cytokine receptor interaction, mTOR signaling pathway, Wnt signaling pathway, and neuroactive ligand-receptor interaction were found to contain the highest number of DEGs. Wnt signaling was recognized to play a critical role in embryonic development, adult brain morphogenesis, and the regulation of cell survival, proliferation, and differentiation [ 16 ]. Consequently, Wnt signaling was determined to be essential for hypothalamic neuroendocrine regulation and was demonstrated to significantly influence body weight and food intake [ 47 ]. Another significant pathway, the mTOR signaling pathway, was observed to colocalize with hypothalamic neurons expressing neuropeptide Y and proopiomelanocortin [ 7 ]. Hypothalamic mTOR signaling was directly implicated in the regulation of food intake and energy balance, with its activation established as a crucial anorexigenic signal in avian species [ 50 ]. KEGG analyses conducted on postnatal days 28, 35, and 42 demonstrated elevated enrichment activity relative to day 21. By day 28, hypothalamic molecular characteristics were largely determined by fatty acid metabolism pathways, which encompassed linoleic acid, α-linolenic acid, arachidonic acid, ether lipid, and glycerophospholipid metabolism. Fatty acids were confirmed to critically modulate hypothalamic neuronal function, which is indispensable for maintaining systemic energy balance [ 12 , 47 ]. Notably, the MAPK signaling pathway was detected in both ND and HD groups on days 28 and 35. Prior research had established the involvement of hypothalamic MAPK/ERK signaling in glucose homeostasis regulation, particularly in response to leptin, insulin, and FGF19 [ 3 ]. Consequently, the dynamic energy balance within the hypothalamus during this developmental window was found to be modifiable by density-associated stress, contingent upon MAPK signaling activation. Furthermore, heightened activity in tyrosine metabolism and folate biosynthesis was detected through KEGG enrichment analyses at 35 and 42 days. Tyrosine concentration within the brain was shown to influence catecholamine biosynthesis, a phenomenon specific to functionally engaged neurons [ 15 ]. Catecholamines were documented to be synthesized centrally in response to stress and to contribute to stress response modulation [ 44 ]. These findings collectively indicated that stocking density-induced pressure could elicit dynamic hypothalamic stress responses. Transcriptomic analysis revealed distinct hypothalamic gene expression profiles in ND and HD reared broilers, characterized by differential upregulation and downregulation patterns across four developmental stages (Table 4 ). Among the upregulated DEGs identified at day 21 between ND and HD groups was pancreatic polypeptide (PPY), which was predominantly expressed within the digestive system and distributed along the gut-brain and brain-gut axes [ 38 ]. The release of PPY and its biological effects on digestion and nutrient absorption were found to be modulated by neuropeptide Y (NPY) signaling [ 17 ]. Neuromedin U (NMU) was widely distributed throughout the central nervous system (CNS). Primary research foci were established on NMU distribution within the brain and intestine, as well as its function in central energy homeostasis and smooth muscle contraction [ 32 ]. Furthermore, NMU was potentially implicated in diverse pathophysiological processes, particularly inflammation and oncogenesis [ 30 ]. Additionally, fibroblast growth factor 2 (FGF2) and fibroblast growth factor 6 (FGF6) were observed to be upregulated at 28 and 35 days post-hatch. FGF2 was demonstrated to enhance neurite outgrowth, neuroblast migration, and survival in explanted chick embryo otocyst cultures [ 20 ]. It was established that FGF6 constituted an essential signaling molecule for midbrain and limb development in chick embryos [ 48 ]. Moreover, FGFs, functioning as neurotrophic factors, were shown to promote neuronal survival and neurite extension in vitro, while supporting neuronal function in vivo [ 54 ]. Notably, signal transducer and activator of transcription 2 (STAT2) and prostaglandin-endoperoxide synthase 2 (PTGS2) were found to be concurrently upregulated in both experimental groups at 42 days. STAT2, a critical mediator of immune responses to diverse stimuli including pathogen invasion, inflammation, and carcinogenesis [ 25 ], exhibited elevated expression under HD conditions. This elevation was interpreted as potentially representing an adaptive immune response to environmental stressors. The resultant upregulation was associated with indirect activation of the JAK-STAT signaling pathway, thereby facilitating transcription of interferon-stimulated genes with antiviral properties. Consequently, this process was determined to influence broiler immunocompetence and overall health status [ 33 ]. Substantial evidence confirmed PTGS2 as an oxidative stress biomarker [ 18 , 49 ], with which functional interaction with cyclooxygenase-2 (COX-2) was identified [ 2 ]. The absence of significant COX-2 differential expression suggested that density-induced hypothalamic plasticity during this stage specifically triggered oxidative stress pathways. Table 4 List of potential candidate genes in the hypothalamus of broilers under ND and HD condition. Group Expression type Genes Description log2FoldChange P value GO terms 1 KEGG terms 2 21d ND vs. HD Up Regulation TNFRSF8 TNF receptor superfamily member 8 1.871 0.041 GO:0005654 Enables transmembrane signaling receptor activity TNFR2 non-canonical NF-kB pathway NMU Neuromedin U 1.649 0.005 GO:0005576 Enables signaling receptor binding Neuropeptide signaling pathway PPY Pancreatic polypeptide 1.855 0.005 GO:0007218 Neuroactive ligand-receptor interaction GPCR downstream signalling pathway Down Regulation WNT16 Wnt family member 16 -1.406 0.010 GO:0090403 Oxidative stress-induced premature senescence mTOR signaling pathway SLCO1C1 Solute carrier transporter family member 1C1 -2.335 0.043 GO:0016323 Basolateral plasma membrane Focal adhesion signaling pathway GRM2 Glutamate metabotropic receptor 2 -2.674 1.47E-06 GO:0004930 G protein-coupled receptor activity CREB signaling pathway 28d ND vs. HD Up Regulation PLA2G2A Phospholipase A2 group IIA 2.202 0.011 GO:0003824 Catalytic activity Glycerophospholipid biosynthetic pathway ICOS Inducible T-cell costimulato 1.552 0.035 GO:0005886 Plasma membrane T-cell antigen receptor (TCR) pathway FGF2 Fibroblast growth factor 2 2.078 0.045 GO:0005104 Cytokine activity Angiogenesis Signaling Pathway Down Regulation MPZL2 Myelin protein zero like 2 -1.893 0.045 GO:0005515 Junctional adhesion molecule-like binding Calcium signaling pathway HHAT Hedgehog acyltransferase -2.109 0.034 GO:0005525 Protein amino acid binding, glycoprotein binding Hedgehog signaling pathway NPY Neuropeptide Y -0.454 0.015 GO:0001664 G protein-coupled receptor binding cAMP signaling pathway AQP5 Aquaporin 5 -2.574 0.038 GO:0015250 Water channel activity Salivary secretion signaling pathway BGLAP Bone gamma-carboxyglutamate protein -1.988 0.003 GO:0005179 cAMP generating peptide activity Parathyroid hormone synthesis signaling pathway 35d ND vs. HD Up Regulation USP21 Ubiquitin specific peptidase 21 1.767 0.048 GO:0006511 Ubiquitin-dependent protein catabolic process Necroptosis signaling pathway PIPOX Pipecolic acid and sarcosine oxidase 1.169 0.043 GO: 0008115 Sarcosine oxidase activity Glycine, serine and threonine metabolism pathway FGF6 Fibroblast growth factor 6 4.076 0.029 GO:0005615 Intercellular space Cell adhesion signaling pathway CTXN3 Cortexin 3 2.069 0.037 GO:0005515 Protein amino acid binding Autophagy pathway Down Regulation SMAD3 TGF-beta antagonist Smad3 -7.657 2.90E-06 GO: 0009785 RNA polymerase II cis-regulatory region TGF-beta signaling pathway PMCH Pro-melanin concentrating hormone -1.514 0.013 GO: 0005179 Manin-concentrating hormone activity Peptide ligand-binding receptors signaling pathway HNRNPK Heterogeneous nuclear ribonucleoprotein K-like -8.607 1.44427E-09 GO: 0003676 DNA binding SUMOylation of RNA binding signaling pathway HINT Histidine triad nucleotide binding protein -8.650 3.37338E-10 GO: 0005080 Protein kinase C binding Remdesivir signaling pathway PCDHB4 Protocadherin beta-15-like -2.150 0.020 GO: 00055095 Calcium ion binding PI3K-Akt-mTOR-signaling pathway NKX2-4 NK2 homeobox 4 -2.175 0.003 GO: 00009815 RNA polymerase II-specific Transcription factors signaling pathway 42d ND vs. HD Up Regulation STAT2 Signal transducer activator of transcription 2 1.082 0.044 GO: 0009815 DNA-binding transcription factor activity Jak-Stat signaling pathway PTGS2 Prostaglandin-endoperoxide synthase 2 1.319 0.024 GO: 0004666 Prostaglandin-endoperoxide synthase activity IL17 signaling pathway GUCA1B Guanylate cyclase activator 1B 1.996 0.035 GO: 0008048 Calcium sensitive guanylate cyclase activator Autophagy pathway CYSLTR2 Cysteinyl leukotriene receptor 2 1.570 0.034 GO: 0004974 Leukotriene receptor activity Neuroactive ligand-receptor interaction pathway Down Regulation SLC26A3 Solute carrier family 26 member 3 -3.317 0.033 GO: 0005452 Inorganic anion antiporter activity Transmembrane transporters signaling pathway ORM1 Orosomucoid 1 (ovoglycoprotein) -4.329 0.024 GO: 0005576 Collagen-containing extracellular matrix Vitamin D receptor pathway ISL2 ISL LIM homeobox 2 -1.584 0.023 GO: 0000987 Cis-regulatory region sequence-specific DNA binding Transcription factors signaling pathway IL15 Interleukin 15 -1.141 0.024 GO: 0005126 Cytokine receptor binding Olfactory signaling pathway 1,2 Abbreviations: GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes. Wnt family member 16 (WNT16) was identified as a downregulated, differentially expressed gene between the ND and HD groups at day 21. Wnt proteins regulate neuronal stem and progenitor cell activity within the nervous system, as evidenced by anatomical phenotypes in mouse Wnt mutants [ 22 ]. Furthermore, Wnt16 influences melanogenesis in chickens and is implicated in neural crest lineage development [ 48 ]. Evidence suggests interaction between Wnt and mTOR signaling pathways in governing cell differentiation, proliferation, and critical neurodevelopmental processes [ 41 ]. Additionally, the amino acid peptide neuropeptide Y (NPY) exerts a significant orexigenic effect within the central regulation of appetite, weight gain, and energy homeostasis in chickens [ 48 ]. This study established the involvement of candidate genes NPY and POMC in energy balance, food intake, and body metabolism, linking them to the adipocytokine signaling pathway—crucial for energy homeostasis and metabolic regulation [ 5 ]. The results indicate heightened hypothalamic activity on days 21 and 28, associated with physiological processes governing growth, development, and food intake. Significant differences in the expression of SMAD family member 3 (SMAD3) and solute carrier family 26 member 3 (SLC26A3) were also observed between groups at 35 and 42 days of age. The transcription factor SMAD3, essential for TGF-β signaling, possesses an N-terminal MH1 domain that interacts with diverse proteins and binds the Smad binding element (SBE), thereby regulating immune responses, growth, development, and neuronal maintenance via the TGFβ superfamily [ 21 ]. In the present study, the altered expression patterns in broilers reared under HD conditions correlate with their physiological transformations. Broilers in high-density environments encounter diverse stressors. Variations in SMAD3 expression may mediate immune responses to environmental stressors. The neurotransmitter SLC26A3, commonly termed a serotonin transporter, functions in both peripheral and central nervous system pathways [ 40 ]. Indirect evidence indicates SLC26 transporters may constitute targets or determinants of oxidative stress, particularly when dysregulated or deficient [ 43 ]. The temporal expression profiles of these DEGs demonstrate how elevated stocking density elicits dynamic alterations in stress responses throughout the growth cycle. Conclusion It was demonstrated that high stocking density (HD) significant influenced broiler growth and development, with distinct temporal patterns exhibited in serum indices and hypothalamic transcriptomics. Similar performance was displayed by ND and HD broilers from days 1 to 21, whereas significant differences were observed between days 21 and 42. Moreover, notable fluctuations in serum cortisol and insulin-like growth factor-1 were exhibited on days 28 and 42. The HD group exhibited elevated levels of serum inflammatory factors, including TNF-α, IL-1β, IL-4, and IL-10, particularly during days 35 to 42. Stage-dependent variations in hypothalamic molecular changes were demonstrated. At 21 days, neuroactive ligand-receptor and cytokine-cytokine receptor pathways were significantly enriched, accompanied by the upregulation of NMU and PY . At 28 days, fatty acid metabolism pathways were found to predominate. Between 35 and 42 days, Notch signaling, oxidative phosphorylation, and apoptosis pathways were enriched, with differential expression of SMAD3 and STAT2 observed, which were associated with stress-induced apoptosis, alterations in energy metabolism, and immune abnormalities. Stage-specific hypothalamic molecular changes were revealed by this study, indicating that broiler performance, serum indices, and hypothalamic mechanisms were adversely affected by high stocking density. Further investigation of hypothalamic physiological alterations was recommended. Declarations Acknowledgements The authors thank the College of Animal Science and Technology of Henan University of Science and Technology for the use of the experimental facilities and thank the International Joint Lab for Animal Welfare and Health Breeding of Henan Province and the Expat Scientist Studio for Animal Stress and Health Breeding of the Province Henan for the kind scientific advice on this experiment. Authors' contributions Investigation, D.B. (Dongying Bai) and Y.Z. (Yi Zhang); methodology, D.B. Y.Z. and X.H.; software, Y.W. and B.Z.; data curation, Y.W. and X.X.; writing and preparation of the original draft, D.B. and Y.Z.; result interpretation, D.B. Y.Z. and X.H; writing, reviewing and editing, Y.Z. X.H. D.B. W.Z. K.I. and B.Z.; supervision, Y.M.; and project administration, Y.M. All authors have read and agreed to the submitted version of the manuscript. Funding The National Key Research and Development Program of China (2024YFE0111600); the Key Research and Development Program of Henan Province (241111113800); the Program for International S&T Cooperation Projects of Henan Province (232102521012); the Key Research and Development and Promotion of Special (Science and Technology) Project of Henan Province (242102110018); and Natural Science Foundation of Henan Province (252300421652). Data availability All data generated or analyzed in this study are included in this published article as well as its supplementary materials. The raw sequencing data generated in this study are available in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1231983. The dataset is hosted in the NCBI SRA repository at: https://www.ncbi.nlm.nih.gov/sra?term=PRJNA1231983 Ethics approval and consent to participate The animal study protocol was approved by the Animal Care and Use Committee of Henan University of Science and Technology, and the management and experimental procedures of the experimental animals were in accordance with the regulations of the Institutional Animal Care and Use Committee. Disclosure of Conflicts of Interest The authors of the submitted manuscript declare that there are no conflicts of interest and that the manuscript has been approved for publication by all authors. I would like to declare on behalf of my co-authors that the work described is original research that has never been published and is not being considered for publication elsewhere. References Aderibigbe AS, Ajuwon KM, Adeola O. 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Effect of dietary aspirin eugenol ester on the growth performance, antioxidant capacity, intestinal inflammation, and cecal microbiota of broilers under high stocking density. Poult Sci. 2024; 103(7): 103825. https://doi.org/10.1016/j.psj.2024.103825 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7091754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485513347,"identity":"66aa0def-8878-4473-ae9e-526e088cca8d","order_by":0,"name":"Dongying Bai","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongying","middleName":"","lastName":"Bai","suffix":""},{"id":485513350,"identity":"db7386a6-f32d-47cf-94f6-3965c31f6891","order_by":1,"name":"Yi Zhang","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":485513351,"identity":"2bf769c3-28ed-4550-ba9a-dfcd58e2a0d7","order_by":2,"name":"Xianglong He","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xianglong","middleName":"","lastName":"He","suffix":""},{"id":485513352,"identity":"0a059d39-6a84-405c-942a-fbcfd71fa6d6","order_by":3,"name":"Yanli Wang","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yanli","middleName":"","lastName":"Wang","suffix":""},{"id":485513353,"identity":"77c00b90-0923-4b63-a993-b21ecd471b32","order_by":4,"name":"Bo Zheng","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zheng","suffix":""},{"id":485513356,"identity":"fa8ed0ca-e1b3-4029-8f3d-9ccaf0f91d90","order_by":5,"name":"Xueqing Xiao","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xueqing","middleName":"","lastName":"Xiao","suffix":""},{"id":485513357,"identity":"e588ffb8-7696-4885-8417-41f0a0d18814","order_by":6,"name":"Wenrui Zhen","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenrui","middleName":"","lastName":"Zhen","suffix":""},{"id":485513359,"identity":"506fc204-35f9-46dd-a873-ebf7be058216","order_by":7,"name":"Koichi Ito","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Ito","suffix":""},{"id":485513360,"identity":"e16463b4-957d-423e-8e39-7193df911805","order_by":8,"name":"Bingkun Zhang","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Bingkun","middleName":"","lastName":"Zhang","suffix":""},{"id":485513366,"identity":"788edaaa-563b-4004-b518-14130dc17ee7","order_by":9,"name":"Yanbo Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYLACxgYGBgMwq+IAVIiNWC0HzoC0MJOi5WAbEVp0288Yfi7cYcNgzn7G+PPHeXfkDc6fP8DwoewwA//sBqxazM7kGEvPPJPGYNmTYyZxcNszww03khkYZ5w7zCBx5wB2LQdyDKR52w4zGBzIMWM4uO0w44YbzAzMYBGJBOxazr8x/g1WAGR8ODjnsP2G84cZmP/i03Ijxwxiy40cA4mDDYcTNxxIZmBmxKvlWZk1L9AvBkCGxJljh5Nn3kg2ONhzLp1H4gYuhyVvvs0LDDEDIONDRc1h277zBx8++FFmLcc/A7sWBgYOcIzUNyCLHQBiHhzqgYD9AW65UTAKRsEoGAUgAAD7/Wm0OCemBQAAAABJRU5ErkJggg==","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yanbo","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-07-10 10:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7091754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7091754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86844266,"identity":"49b6f02a-df8c-48c7-8834-7e7c96bc6e9a","added_by":"auto","created_at":"2025-07-16 08:30:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":403413,"visible":true,"origin":"","legend":"\u003cp\u003eSerum biochemical and immunity parameters of broiler chickens. (A) Cortisol (CORT) (B) Insulin-like growth factor I (IGF-I) (C) tumor necrosis factor (TNF-α) (D) Interleukin (IL)-1β (E) Interleukin 4 (IL-4) (F) Interleukin 10 (IL-10). ND, normal stocking density fed basal diet; HD, high stocking density fed basal diet. Means with different letters are significantly different (n = 10, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/2b14b6a54d66ab376c80a193.jpg"},{"id":86845174,"identity":"f48895da-aa68-4f4e-b54a-cff5b280f5a9","added_by":"auto","created_at":"2025-07-16 08:38:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":392189,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional analysis of the DEGs from the chicken hypothalamus between ND and HD group at 21 day. (A) PCA score plot; (B) Hierarchical clustering analysis of DEGs; (C) Volcano plot analysis of DEGs; (D) GO enriched trems for DEGs; (E) KEGG enrichment bar plots of DEGs. ND, normal stocking density; HD, high stocking density (n = 4).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/5ab47b6d2b1124d78d77a119.jpg"},{"id":86845180,"identity":"b5815e86-5ef4-4bee-b34d-65197b345c99","added_by":"auto","created_at":"2025-07-16 08:38:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":428890,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional analysis of the DEGs from the chicken hypothalamus between ND and HD group at 28 day. (A) PCA score plot; (B) Hierarchical clustering analysis of DEGs; (C) Volcano plot analysis of DEGs; (D) GO enriched trems for DEGs; (E) KEGG enrichment bar plots of DEGs. ND, normal stocking density; HD, high stocking density (n = 4) .\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/a84d191fa2e903aa3ced411e.jpg"},{"id":86845179,"identity":"81c80c1d-5806-4a1d-97de-d95ee08b5cc6","added_by":"auto","created_at":"2025-07-16 08:38:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":423848,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional analysis of the DEGs from the chicken hypothalamus between ND and HD group at 35 day. (A) PCA score plot; (B) Hierarchical clustering analysis of DEGs; (C) Volcano plot analysis of DEGs; (D) GO enriched trems for DEGs; (E) KEGG enrichment bar plots of DEGs. ND, normal stocking density; HD, high stocking density (n = 4).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/76996754a2d645965b2c55b0.jpg"},{"id":86844271,"identity":"5027ab48-2dd5-4633-a145-7364b5052e76","added_by":"auto","created_at":"2025-07-16 08:30:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":424915,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional analysis of the DEGs from the chicken hypothalamus between ND and HD group at 42 day. (A) PCA score plot; (B) Hierarchical clustering analysis of DEGs; (C) Volcano plot analysis of DEGs; (D) GO enriched trems for DEGs; (E) KEGG enrichment bar plots of DEGs. ND, normal stocking density; HD, high stocking density (n = 4).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/887e3a452a057b8c411295f7.jpg"},{"id":86845843,"identity":"1b8c2585-69da-4390-8090-b257c8ec8d20","added_by":"auto","created_at":"2025-07-16 08:46:37","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":345922,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional analysis of the DEGs from the chicken hypothalamus among 21, 28, 35 and 42 day. (A) PCA score plot; (B) Venn diagrams of DEGs; (C) Summary analysis of DEGs; (D) Genome circle diagram for DEGs.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/213f0bb12a06e7424ec3425b.jpg"},{"id":86844282,"identity":"e5d6e04d-1e47-4c2e-9a26-4b7e56fc8f82","added_by":"auto","created_at":"2025-07-16 08:30:37","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":209185,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the mRNA expression levels for selected transcripts. (A) Comparison of the log\u003csub\u003e2\u003c/sub\u003efoldchange (n = 4). (B) Linear regression equation and correlation. RNA-Seq: RNA sequencing; qRT-PCR: real-time quantitative reverse transcription PCR.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/507fcf5762f3cf0bf44aed7c.jpg"},{"id":94062797,"identity":"95c670d3-e58a-4e68-bfeb-17fd2344a083","added_by":"auto","created_at":"2025-10-22 07:16:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3810991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/5a819c42-9d8b-4e0a-9bbe-d9c3e517f01a.pdf"},{"id":86844332,"identity":"10355f5f-78e9-4ca9-a0f0-79a3010de205","added_by":"auto","created_at":"2025-07-16 08:30:39","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21351185,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7091754/v1/7b6d498e4dfc76b9a6eb147a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel insights into the transcriptomic changes of the hypothalamus in broilers exposed to high-density stocking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rising demand for poultry stimulated the development of highly productive chicken populations, resulting in a substantial enhancement of their productivity levels [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Within the poultry industry, awareness concerning avian health and welfare issues has markedly increased. This heightened awareness is particularly evident in poultry farming practices, owing to the significant impact of stocking density on the level of environmental factors to which birds are consistently exposed [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Nonetheless, the preservation and enhancement of poultry welfare was dependent upon the maintenance of appropriate stocking densities. However, high stocking density (\u003cb\u003eHD\u003c/b\u003e) was frequently employed to enhance profitability by maximizing the number of laying hens reared within a confined area [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. According to current feeding guidelines that accounted for aspects including animal welfare, growth efficiency, and other relevant factors, a density of 16 broilers per square meter or 39 kilograms per square meter was considered acceptable [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Elevated levels of oxidative stress were frequently induced by early manifestations of HD, as indicated by our study [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and corroborating research [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] from other researchers, subsequently resulting in compromised immune function, an aberrant metabolic response, and reduced growth performance.\u003c/p\u003e\u003cp\u003eThe hypothalamus was recognized as a critical component within the brain. It was positioned anterior to the pituitary gland and inferior to the thalamus. Crucially, it functioned as an essential interface between the nervous and endocrine systems, enabling coordinated interactions and communication between these complex physiological systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, a number of physiological functions were regulated by the hypothalamus, including body temperature, thirst and hunger, sleep patterns, circadian rhythms, and fatigue [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Hypothalamic developmental disorders were observed to arise with advancing growth and sexual maturity. These abnormalities were also found to cause physiological and neurological changes, which were linked to the occurrence of disorders such as obesity, autism, infertility, depression, and chronic oxidative stress [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the monitoring of these fundamental metabolic processes in avian species was demonstrated to be essentially dependent on the hypothalamus. A well-documented example was the secretion of growth hormone (GH), a crucial growth factor, by the pituitary gland. This secretion was triggered in response to stimulation of somatoliberin (GHRH) secretion by the hypothalamus [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Consequently, the molecular activity of the hypothalamus was shown to be controlled by a range of external stimuli. The possible effects of increased HD-induced oxidative stress on the response and molecular activity of the avian hypothalamus were identified as requiring thorough investigation..\u003c/p\u003e\u003cp\u003eIn the life sciences, particularly within the study of industrialized animals, high-throughput sequencing technology was recognized as a well-established field whose significance was being increasingly acknowledged [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. One such instance was the successful identification of a genetic candidate potentially associated with the growth rate of broiler chickens, which was achieved using a novel high-throughput RNA sequencing methodology [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Examination of the hypothalamic transcriptome in a recent study revealed that mRNAs were critical for regulating the timing of gonadal development in chickens [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, research employing transcriptome analysis demonstrated that heat stress activated several metabolic pathways. The advancement of effective genetic strategies to improve thermotolerance in chickens was considered highly feasible through the identification of these specific target genes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. RNA-sequencing analysis of pituitary and hypothalamus tissues was established as an important new tool for elucidating the genetic mechanisms underlying elevated egg production rates in laying hens [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, the utilization of high-throughput sequencing to achieve specific targets in the avian hypothalamus was confirmed as a reliable technique.\u003c/p\u003e\u003cp\u003eThe molecular alterations occurring in the hypothalamus of broiler chickens under HD conditions over time, along with potential contributing mechanisms (e.g., energy fluctuations, metabolic shifts, and maintenance of inflammatory homeostasis), were poorly characterized. Therefore, to comprehend the intricate dynamics governing the hypothalamus in broiler chickens subjected to HD and to illuminate underlying mechanisms, a comprehensive investigation was required. The primary objective of this study was to determine whether exposure to HD conditions affected hypothalamic gene expression and triggered critical molecular pathways.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eEthical treatment\u003c/b\u003e\u003c/p\u003e\u003cp\u003e The Experimental Animal Care and Utilization Committee of Henan University of Science and Technology granted approval for the experimental protocol used in this study (AW20602202-1-2), which was carried out in accordance with the experimental animal guidelines of the Ministry of Science and Technology (Beijing, China).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnimals and experimental design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 238 healthy one-day-old Arbor Acres male broilers were procured from a commercial hatchery (Henan Quanda Poultry Breeding Co., Ltd., Hebi, China). The research was conducted at the Animal Research Unit of Henan University of Science and Technology. During the initial week of the experimental period, birds were maintained at a controlled temperature of 33\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C. A gradual temperature reduction of 1\u0026ndash;2\u0026deg;C per week was then implemented, resulting in a final temperature of 25\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C by day 42. Relative humidity was consistently maintained within a range of 60\u0026ndash;70%. Additionally, lighting was provided for 23 hours daily, with a one-hour dark period from 19:00 to 20:00. On day 7, chicken body weights were measured, and birds were allocated to two distinct treatment groups: one group with a standard stocking density (ND) of 14 birds per square meter, and the other with a high stocking density (HD) of 22 birds per square meter. As previously described, each treatment was composed of three preparations and five replications [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In accordance with National Research Council (NRC) guidelines, birds were fed a corn-soybean meal-based diet (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], excluding any additional substances except for anticoccidial treatment administration. Birds were housed under controlled temperature and humidity conditions with ad libitum access to feed and water. To ensure welfare, all experimental subjects were vaccinated against infectious bronchitis virus and Newcastle disease virus on days 1 and 20, and against bursitis virus on day 14.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComposition and nutrient levels of basal (CON) diet (as-fed basis) %.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIngredients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStarter phase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFinisher phase\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorn (8.7% CP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoybean meal (41% CP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoybean oil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimestone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium chloride\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCholine chloride\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin premix\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMineral premix\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStone powder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDicalcium phosphate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDL-Methionine (99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL-Lysine (99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL-Arginine (99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalculated nutrient levels (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolizable energy(MJ/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrude protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLysine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethionine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailable P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThreonine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArginine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e1\u003c/sup\u003eVitamin premix provided the following per kilogram of diet: VA (retinyl acetate) 10,000 IU, VD3 (cholecalciferol) 2,000 IU, VE (DL-a-tocopheryl acetate) 11.0 IU, VK 1.0 mg, VB1 1.2 mg, VB2 5.8 mg, VB6 2.6 mg, VB12 0.012 mg, niacin 66.0 mg, pantothenic acid (calcium pantothenate) 10.0 mg, biotin 0.20 mg, folic acid 0.70 mg.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e2\u003c/sup\u003eMineral premix provided the following per kilogram of diet: Mg 100 mg, Zn 75 mg, Fe 80 mg, I 0.65 mg, Cu 8.0 mg, Se 0.35 mg.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGrowth Performance and Sample Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThroughout the experimental period, body weights of each broiler group were measured and recorded on days 21, 28, 35, and 42. Additionally, body weight (BW), average daily gain (ADG), and average feed intake (AFI) were assessed, and the feed conversion ratio (FCR) was calculated for the entire study duration. Six broilers were randomly selected from each experimental group at days 21, 28, 35, and 42. Wing vein puncture was employed to collect blood samples. After centrifugation at 3000 \u0026times; g for 30 min at 4\u0026deg;C, serum was separated and immediately frozen at \u0026minus;\u0026thinsp;20\u0026deg;C for subsequent analysis. Subsequently, broilers (n\u0026thinsp;=\u0026thinsp;4) with comparable body weights were euthanized via cervical dislocation, and brains were rapidly excised. The hypothalamus was dissected, snap-frozen in liquid nitrogen, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for further analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSerum Biochemical and Immunity Parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSerum cortisol (CORT) and insulin-like growth factor I (IGF-I) concentrations were quantified on days 21, 28, 35, and 42 using chicken-specific enzyme immunoassay (ELISA) kits (Northern Institute of Biotechnology Co., Ltd., Beijing, China), respectively. Furthermore, serum levels of tumor necrosis factor-alpha (TNF-α), interleukin-1β (IL-1β), interleukin-4 (IL-4), and interleukin-10 (IL-10) were determined via ELISA. Kits specifically validated for chicken samples were employed, and all assays were performed in strict accordance with the manufacturer's protocols. Essential reagents were procured from Nanjing JianCheng Bioengineering Institute Co., Ltd. (Nanjing, China).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA Isolation and cDNA Library\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRNA extraction from hypothalamic samples was conducted utilizing TRIzol reagent (Invitrogen, Carlsbad, CA). RNA concentration and purity were quantified, and library quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Following cDNA synthesis, mRNA was enriched using oligomagnetic beads. Subsequent library construction involved PCR amplification, yielding a library of approximately 450 base pairs. Upon completion of RNA extraction and library preparation, paired-end sequencing was performed on the Illumina platform at Personal Biotechnology Co., Ltd. (Biographical Notes, Shanghai, China).\u003c/p\u003e\u003cp\u003e\u003cb\u003emRNA Sequencing and Data Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRaw sequencing data were filtered and subjected to quality control procedures, following which Q20, Q30, and GC content metrics\u0026mdash;collectively defining the clean data\u0026mdash;were calculated for the valid data. This clean data was subsequently aligned against the \u003cem\u003eGallus gallus\u003c/em\u003e (chicken) reference genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using HISAT2 software. The alignment process was conducted to evaluate sequence alignment efficiency and to map sequence information to the reference genome. Differential expression analysis was performed using DESeq2 software, employing thresholds of |log2(fold change)| \u0026ge; 1 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Genes meeting both criteria were considered differentially expressed. Enrichment analysis for these genes was then conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases via R scripts. Fisher's exact test, combined with Bonferroni correction for multiple testing, was applied to control the false discovery rate. Pathways with a corrected P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVerification of RNA-Seq with qRT-PCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe procedure for RNA extraction and real-time PCR was performed according to established methodologies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Briefly, total RNA was extracted from hypothalamic tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's protocol. RNA purity and concentration were assessed by spectrophotometry using a NanoDrop 2000 instrument. Complementary DNA (cDNA) synthesis was accomplished using the PrimeScript\u0026trade; RT reagent kit with gDNA Eraser (Takara Biotechnology Co., Ltd., Tokyo, Japan). The primers employed to amplify target genes are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Relative gene expression levels were quantified using the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method, with GAPDH serving as the reference gene for normalization.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of primers used for RT-PCR.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTarget Gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward primer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReverse primer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Accession number\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGHRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGAATGCAACATCAGCTGGGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTGTAATTTCTGGGGCAGCGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNM_001201396.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNMU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGTGATCATCAGCAGCGAGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCAGGGTCACAACTGATTTGCAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNM_001292045.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWNT16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCGACGACATCCACTATGGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTCAGCTTTGCTACAGCCTGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eXM_040658113.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGTGCTGACTTTCGCCTTGTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGGTCTTCAAACCGGGATCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNM_205473.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGCCATTTGGTGGGGATCTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGTACAGCAGAGCAGCCAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eXM_040693813.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSLC26A3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCACCTACCCCAAGGGTGAAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACACGGATGTCATGGTTGCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNM_000111.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTGS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTGCGATTTCGAGCGCATTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCCACGTGAAGAATTCCGGTGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNM_001167719.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMAD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTCCTGGGCTGGAAGAAAGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCCAGCGACCTGGGGAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eXM_046935038.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTGTCAGAGTACCCAGCGTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eATCCCCGTTTTGCGTCTTTTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eXM_046934948.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLR4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGGATCTTTCAAGGTGCCACA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGTGTCCGATGGGTAGGTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNM_001030693.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eβ-Actin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGCGTGACATCAAGGAGAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTGCCAGGGTACATTGTGGTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNM_205518\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003eAccession number refers to Gene bank (NCBI). Growth hormone releasing hormone (GHRH); Neuromedin U (NMU); Wnt family member 16 (WNT16); Neuropeptide Y (NPY); Aquaporin 5 (AQP5); Solute carrier family 26 member 3 (SLC26A3); Prostaglandin-D synthase (PTGS2); Smad3 family member 3 (SMAD3); Signal transducer and activator of transcription 2 (STAT2); Toll-like receptor 4 (TLR4).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eIndividual animals within each replicate were designated as experimental units for the analysis in this study. Additionally, each cage replicate was regarded as an experimental unit for evaluating growth performance and assessing serum biochemical indices. Data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM) using SAS version 8.1 (SAS Institute Inc., Cary, NC). Mean separation was conducted using a t-test, and a statistically significant difference was indicated at a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eProduction Performance of Broiler Chickens\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData detailing broiler production performance appear in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. No significant effects on broiler body weight, body weight gain, feed intake, or feed conversion ratio were detected between the ND and HD groups during the initial 21-day period (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). From day 22 to day 42, however, the HD group showed a significant decrease in both body weight and feed intake relative to the ND group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the HD group demonstrated a significant increase in the feed conversion ratio compared to the ND group during this later phase (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProduction performance of broiler chickens.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eExperimental groups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1-21d\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody weight (g/bird)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e654.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e634.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody weight gain (g/bird)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.57\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeed intake (g/bird)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeed conversion ratio (g/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e22-42d\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody weight (g/bird)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2527.83\u0026thinsp;\u0026plusmn;\u0026thinsp;25.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2325.28\u0026thinsp;\u0026plusmn;\u0026thinsp;20.52\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody weight gain (g/bird)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153.61\u0026thinsp;\u0026plusmn;\u0026thinsp;11.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150.95\u0026thinsp;\u0026plusmn;\u0026thinsp;13.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeed intake (g/bird)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.42\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeed conversion ratio (g/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003e ND: normal stocking density; HD: high stocking density. \u003csup\u003ea,b\u003c/sup\u003eDifferent letters indicate a significant effect of treatment at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSerum Biochemical and Immunity Parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBroiler serum biochemical and immunological parameters were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. On day 21, a statistically significant increase (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in IL-10 concentration was observed exclusively in the HD group compared to the ND group. On day 28, a significant higher concentration of IGF-1 was measured in the ND broiler group relative to the HD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). At 35 days of age, significant elevated concentrations of TNF-α, IL-4, and IL-10 were detected in the HD group compared to the ND group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, at 42 days of age, significant increased levels of CORT, TNF-α, IL-1β, and IL-4 were recorded in the HD group relative to the ND group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA-Sequencing Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eQuality assessment was performed on total RNA extracted from the hypothalamus of broilers at 21, 28, 35, and 42 days of age. The results were documented in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. An average read count of 42.48\u0026nbsp;million was yielded per sample from hypothalamic sequencing. The average percentage of bases with quality scores exceeding Q20 and Q30 across all hypothalamic samples was observed to be 97.31% and 92.90%, respectively. Subsequent filtering of the RNA-Seq data was conducted, and high-quality sequences, designated as clean reads, were identified. Post-filtering, an average of 40.28\u0026nbsp;million clean reads per chicken hypothalamic sample was determined. These clean reads were aligned to the chicken reference genome, and the alignment rate was calculated for each sample. An average of 37.44\u0026nbsp;million sequences matching the reference genome was demonstrated, corresponding to an average alignment rate of 92.93%. Notably, among these aligned sequences, an average of 36.73\u0026nbsp;million were aligned uniquely to one position, accounting for 98.12% of the total. The high quality of the sequencing data from chicken hypothalamic samples across all ages was confirmed, and the data were rendered suitable for subsequent alignment analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferentially Expressed Genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, separate analyses were conducted sequentially at 21, 28, 35, and 42 days of age based on sequencing results obtained at these four time points. At 21 days, principal component analysis (PCA) score plots revealed distinct clustering of ND and HD samples based on hypothalamic transcriptomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Similarly, PCA results at 28 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), 35 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), and 42 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) demonstrated separation of ND and HD groups into discrete clusters when considering hypothalamic chicken transcriptomes. Hierarchical cluster analysis of differentially expressed genes (\u003cb\u003eDEGs\u003c/b\u003e) at 21, 28, 35, and 42 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) further indicated that samples within the same experimental group exhibited pronounced clustering tendencies. This group-specific clustering pattern was visually evident in heatmap representations, which effectively delineated divergent gene expression profiles between ND and HD groups. At day 21, hypothalamic analysis identified 45 DEGs (26 upregulated, 19 downregulated) between ND and HD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cb\u003eTable S2\u003c/b\u003e). By day 28, 51 DEGs were detected (22 upregulated, 29 downregulated; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cb\u003eTable S3\u003c/b\u003e). During the 35-day assessment, 81 DEGs were identified (13 upregulated, 68 downregulated; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cb\u003eTable S4\u003c/b\u003e). Finally, comprehensive analysis at 42 days revealed 34 hypothalamic DEGs (14 upregulated, 20 downregulated) distinguishing ND and HD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cb\u003eTable S5\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of the GO and KEGG Enrichment for DEGs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Gene Ontology (\u003cb\u003eGO\u003c/b\u003e) was developed as a thorough database by the Gene Ontology Consortium to classify gene functions into cellular components (\u003cb\u003eCC\u003c/b\u003e), molecular functions (\u003cb\u003eMF\u003c/b\u003e), and biological processes (\u003cb\u003eBP\u003c/b\u003e). Enrichment associated with days 21, 28, 35, and 42 was demonstrated by comparing the ND and HD groups and performing GO analysis of the hypothalamic DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). On day 21, GO annotations in the hypothalamus of broiler chickens from the ND and HD groups predominantly involved BP and MF, including the neuropeptide signaling pathway, neuropeptide receptor binding, opsonin receptor activity, complement component C5a signaling, regulation of TRAIL production, and neuromeric U-receptor binding. The GO annotations of the hypothalamus in broilers from the ND and HD groups at 28 days illustrated processes such as zymogen activation, substantia nigra development, serine-type endopeptidase activity, serine hydrolase activity, and cis-trans isomerase activity. Furthermore, host biological processes including DNA integration, cell aggregation, endonuclease activity, asparagine-type endopeptidase activity, and aspartic-type peptidase activity were displayed in the hypothalamus GO annotations of 35-day-old broiler chickens from the ND and HD groups. Additionally, distinct biological processes were revealed by GO annotations in the hypothalamus of 42-day-old broiler chickens from the ND and HD groups, encompassing NK-T cell proliferation, control over hepatocyte differentiation, positive regulation of cell-cell adhesion, regulation of protein O-linked glycosylation, extrathymic T cell differentiation, and chloride transmembrane transporter activity.\u003c/p\u003e\u003cp\u003eEnrichment analysis of the KEGG database was conducted to elucidate the biological functions and gene interactions of DEGs. Broiler chickens in the ND and HD groups exhibited enhanced KEGG signaling pathways associated with DEGs in the hypothalamus across four developmental stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). At 21 days of age, 34 DEGs demonstrated notable enrichment within two KEGG signaling pathways: the neuroactive ligand-receptor interaction pathway and the cytokine-cytokine receptor interaction pathway. Genes implicated in the neuroactive ligand-receptor interaction pathway included \u003cem\u003eNMU\u003c/em\u003e, \u003cem\u003ePY\u003c/em\u003e, and \u003cem\u003ePRL\u003c/em\u003e (upregulated), while the cytokine-cytokine receptor interaction pathway involved \u003cem\u003ePRL\u003c/em\u003e and \u003cem\u003eTNFRSF8\u003c/em\u003e (upregulated). At 28 days of age, significant enrichment of 51 DEGs was observed across five KEGG pathways: linoleic acid metabolism, alpha-linoleic acid metabolism, intestinal immune network for IgA production, and arachidonic acid metabolism. Key genes participating in these pathways comprised \u003cem\u003ePLA2G2A\u003c/em\u003e and \u003cem\u003eICOS\u003c/em\u003e (upregulated). At 35 days of age, significant enrichment of 81 DEGs occurred within four KEGG signaling pathways: folate metabolism, tyrosine metabolism, oxidative phosphorylation, and the Notch signaling pathway. At 42 days of age, significant enrichment of 45 DEGs was detected within eight KEGG pathways, encompassing tyrosine metabolism, intestinal immune network for IgA production, retinol metabolism, cytochrome P450, fatty acid degradation, pyruvate metabolism, glycolysis/gluconeogenesis, and the apoptosis signaling pathway.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of DGEs across different developmental days\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn initial comprehensive evaluation of hypothalamic transcriptomes was conducted at 21, 28, 35, and 42 days post-hatch (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Principal Component Analysis (\u003cb\u003ePCA\u003c/b\u003e) score plots were employed to classify the four age groups into distinct clusters based on hypothalamic transcriptome profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Comparative analyses were performed between 21-day-old chickens and each of the other age groups. Additionally, a comprehensive overview of the gene circle map was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), with the outermost circle dedicated to chromosome bands and subsequent circles illustrating the results of differential expression analyses. DGEs analysis between 21- and 28-day hypothalamic samples revealed 56 upregulated genes and 24 downregulated genes. Comparison of the 21-day group with the 35-day group identified 24 upregulated genes and 41 downregulated genes. Analysis of the 21-day versus 42-day groups showed 54 upregulated genes and 38 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Furthermore, a thorough investigation was carried out using a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), which identified an intersection of six genes common to the comparisons: \u003cem\u003eTMOD1\u003c/em\u003e, \u003cem\u003eALPK1\u003c/em\u003e, \u003cem\u003eUMODL1\u003c/em\u003e, \u003cem\u003eHBAD\u003c/em\u003e, \u003cem\u003eFYB2\u003c/em\u003e, and \u003cem\u003eENSGALG00000049341\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of Candidate Gene\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo validate the expression levels of DEGs identified through RNA-seq analysis, selected upregulated genes (\u003cem\u003eNMU\u003c/em\u003e, \u003cem\u003eNPY\u003c/em\u003e, \u003cem\u003ePTGS2\u003c/em\u003e, \u003cem\u003eSTAT2\u003c/em\u003e, and \u003cem\u003eGHRH\u003c/em\u003e) and downregulated genes (\u003cem\u003eAQP5\u003c/em\u003e, \u003cem\u003eWNT16\u003c/em\u003e, \u003cem\u003eSMAD3\u003c/em\u003e, \u003cem\u003eTLR4\u003c/em\u003e, and \u003cem\u003eSLC26A3\u003c/em\u003e) were subjected to quantitative real-time polymerase chain reaction (qRT-PCR) validation, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA. A concordant regulatory pattern in the expression profiles of these genes was demonstrated by the qPCR results. A significant correlation was revealed through statistical analyses, including determination of the Pearson correlation coefficient (r\u0026thinsp;=\u0026thinsp;0.9603) and linear regression, between the expression levels measured by qRT-PCR and RNA-Seq methodologies (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBroilers were housed at elevated densities to optimize cage space utilization and boost production efficiency. However, detrimental effects of high density (\u003cb\u003eHD\u003c/b\u003e) on broilers, such as hindered growth and compromised physiological well-being, were documented by accumulating research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Birds reared under HD conditions were noted to display significant adverse impacts on feed intake, body weight, and feed conversion ratio (\u003cb\u003eFCR\u003c/b\u003e) relative to normal density (\u003cb\u003eND\u003c/b\u003e). Reductions in body weight and FCR were identified when stocking density exceeded 20 broilers/m\u0026sup2; [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. HD stress was recognized as a key contributor to diminished production performance [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. At increased stocking densities, the birds' living environment was transformed through decreased available space per individual. This spatial limitation was associated with restricted daily behaviors, particularly mobility [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Overcrowding was evidenced to intensify with rising density. Foraging efficiency and feed accessibility were impeded by limited space [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. No significant variations in growth performance were observed during the initial 21 days. Conversely, notable differences in body weight, feed intake, and FCR were detected from day 22 to 42. This outcome aligned with prior studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], implying that density stress emergence coincided with rapid growth. Thus, the focus on the 21- to 42-day interval in ensuing analyses was founded on this evidence.\u003c/p\u003e\u003cp\u003eBlood biochemical indices were utilized as critical diagnostic tools for metabolic disorder identification in broilers [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Avian blood parameters were altered by HD through thermoregulatory mechanisms, inducing stress that impaired performance and physiological indicators [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A density-dependent increase in corticosterone (\u003cb\u003eCORT\u003c/b\u003e) was corroborated by our data. Significantly elevated CORT levels were observed in HD versus ND groups at 42 days, though no differences were detected at 21, 28, or 35 days. While considered suboptimal for comprehensive welfare assessment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], CORT was retained as valuable for chronic stress evaluation. Elevated CORT was associated with hyperglycemia induction, serum protein reduction, and immunity suppression, collectively impairing performance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A significant serum IGF-1 reduction was documented in HD groups at 28 days versus ND, with no differences observed at other intervals. This pleiotropic factor was recognized to regulate tissue growth, modulate metabolism, and exhibit protective properties [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The 28-day reduction was noted to coincide with accelerated broiler growth, indicating heightened developmental sensitivity to density stress. Immune cell function was compromised by stress, delaying cytokine signaling. Inflammation was initiated by TNF-α and IL-1β, while anti-inflammatory effects were exerted by IL-4 and IL-10 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Significantly elevated TNF-α and IL-1β levels were recorded in HD groups at 35 and 42 days, indicating sustained systemic immune impact during late growth and aligning with prior density studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Concurrent elevations in IL-4 and IL-10 suggested active inflammatory homeostasis regulation..\u003c/p\u003e\u003cp\u003eRegarding molecular analysis, hypothalamic transcriptome comparisons between HD and ND reared broilers were conducted across four distinct developmental time points. This investigation was identified as the first molecular-level examination of the hypothalamus at these specific ages. Transcriptomic data reliability was unaffected by individual variability or limited sample size. A distinct pattern of DEGs was observed in the transcriptomic results, exhibiting an initial increase followed by a decrease across the four intervals, with counts of 45, 51, 81, and 34 DEGs respectively. This pattern indicated that a higher number of DEGs were detected in the hypothalamus, dependent on stocking density and rearing duration, and that these DEGs demonstrated increased activity from 35 days of age under HD stress conditions. AA broilers, analogous to Ross 308 broilers, were classified as a fast-growing commercial chicken breed, exhibiting growth rates at 30\u0026ndash;40 days of age that exceeded those of other commercial lines by 10\u0026ndash;15% [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These observations suggested that density stress was more pronounced during periods of rapid growth and was reflected in hypothalamic molecular expression profiles.\u003c/p\u003e\u003cp\u003eTo obtain a comprehensive understanding of the biological functions of these DEGs, GO annotation and KEGG pathway analyses were performed. Several hypothalamic DEGs were associated with biological processes and molecular functions, with particular emphasis on cellular components. Activated GO terms related to brain development, serotonin transporter activity, cellular processes, epithelial development, neurogenesis, grooming behavior, and central nervous system development were identified at each of the four time points between the ND and HD groups. However, significantly fewer common GO terms were observed between the ND group and the other three age groups compared to the 21-day cohort. Temporal differences in hypothalamic growth and developmental processes at different stages were suggested by the GO terms in both BP and MF categories. Dynamic temporal variations in hypothalamic physiological activity over time were also indicated. These findings were found to differ from previous experimental results [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], a discrepancy that was potentially attributable to methodological variations, as no extended temporal molecular analyses of the hypothalamus were conducted in the cited studies..\u003c/p\u003e\u003cp\u003eComparative analysis of KEGG signaling pathways across four postnatal time points under two stocking densities revealed temporally specific enrichment patterns. KEGG pathway enrichment analysis in 21-day-old HD and ND groups identified 14 significantly enriched pathways. The cytokine-cytokine receptor interaction, mTOR signaling pathway, Wnt signaling pathway, and neuroactive ligand-receptor interaction were found to contain the highest number of DEGs. Wnt signaling was recognized to play a critical role in embryonic development, adult brain morphogenesis, and the regulation of cell survival, proliferation, and differentiation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consequently, Wnt signaling was determined to be essential for hypothalamic neuroendocrine regulation and was demonstrated to significantly influence body weight and food intake [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Another significant pathway, the mTOR signaling pathway, was observed to colocalize with hypothalamic neurons expressing neuropeptide Y and proopiomelanocortin [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hypothalamic mTOR signaling was directly implicated in the regulation of food intake and energy balance, with its activation established as a crucial anorexigenic signal in avian species [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKEGG analyses conducted on postnatal days 28, 35, and 42 demonstrated elevated enrichment activity relative to day 21. By day 28, hypothalamic molecular characteristics were largely determined by fatty acid metabolism pathways, which encompassed linoleic acid, α-linolenic acid, arachidonic acid, ether lipid, and glycerophospholipid metabolism. Fatty acids were confirmed to critically modulate hypothalamic neuronal function, which is indispensable for maintaining systemic energy balance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Notably, the MAPK signaling pathway was detected in both ND and HD groups on days 28 and 35. Prior research had established the involvement of hypothalamic MAPK/ERK signaling in glucose homeostasis regulation, particularly in response to leptin, insulin, and FGF19 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, the dynamic energy balance within the hypothalamus during this developmental window was found to be modifiable by density-associated stress, contingent upon MAPK signaling activation. Furthermore, heightened activity in tyrosine metabolism and folate biosynthesis was detected through KEGG enrichment analyses at 35 and 42 days. Tyrosine concentration within the brain was shown to influence catecholamine biosynthesis, a phenomenon specific to functionally engaged neurons [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Catecholamines were documented to be synthesized centrally in response to stress and to contribute to stress response modulation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. These findings collectively indicated that stocking density-induced pressure could elicit dynamic hypothalamic stress responses.\u003c/p\u003e\u003cp\u003eTranscriptomic analysis revealed distinct hypothalamic gene expression profiles in ND and HD reared broilers, characterized by differential upregulation and downregulation patterns across four developmental stages (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the upregulated DEGs identified at day 21 between ND and HD groups was pancreatic polypeptide (PPY), which was predominantly expressed within the digestive system and distributed along the gut-brain and brain-gut axes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The release of PPY and its biological effects on digestion and nutrient absorption were found to be modulated by neuropeptide Y (NPY) signaling [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Neuromedin U (NMU) was widely distributed throughout the central nervous system (CNS). Primary research foci were established on NMU distribution within the brain and intestine, as well as its function in central energy homeostasis and smooth muscle contraction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, NMU was potentially implicated in diverse pathophysiological processes, particularly inflammation and oncogenesis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, fibroblast growth factor 2 (FGF2) and fibroblast growth factor 6 (FGF6) were observed to be upregulated at 28 and 35 days post-hatch. FGF2 was demonstrated to enhance neurite outgrowth, neuroblast migration, and survival in explanted chick embryo otocyst cultures [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It was established that FGF6 constituted an essential signaling molecule for midbrain and limb development in chick embryos [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, FGFs, functioning as neurotrophic factors, were shown to promote neuronal survival and neurite extension in vitro, while supporting neuronal function in vivo [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Notably, signal transducer and activator of transcription 2 (STAT2) and prostaglandin-endoperoxide synthase 2 (PTGS2) were found to be concurrently upregulated in both experimental groups at 42 days. STAT2, a critical mediator of immune responses to diverse stimuli including pathogen invasion, inflammation, and carcinogenesis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], exhibited elevated expression under HD conditions. This elevation was interpreted as potentially representing an adaptive immune response to environmental stressors. The resultant upregulation was associated with indirect activation of the JAK-STAT signaling pathway, thereby facilitating transcription of interferon-stimulated genes with antiviral properties. Consequently, this process was determined to influence broiler immunocompetence and overall health status [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Substantial evidence confirmed PTGS2 as an oxidative stress biomarker [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], with which functional interaction with cyclooxygenase-2 (COX-2) was identified [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The absence of significant COX-2 differential expression suggested that density-induced hypothalamic plasticity during this stage specifically triggered oxidative stress pathways.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of potential candidate genes in the hypothalamus of broilers under ND and HD condition.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExpression type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003elog2FoldChange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO terms\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKEGG terms\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e21d ND vs. HD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eUp Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTNFRSF8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTNF receptor superfamily member 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005654 Enables transmembrane signaling receptor activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTNFR2 non-canonical NF-kB pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNMU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeuromedin U\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005576 Enables signaling receptor binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNeuropeptide signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePPY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePancreatic polypeptide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0007218 Neuroactive ligand-receptor interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGPCR downstream signalling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDown Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWNT16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWnt family member 16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0090403 Oxidative stress-induced premature senescence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emTOR signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSLCO1C1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSolute carrier transporter family member 1C1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0016323 Basolateral plasma membrane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFocal adhesion signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGRM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGlutamate metabotropic receptor 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.47E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0004930 G protein-coupled receptor activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCREB signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e28d ND vs. HD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eUp Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePLA2G2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhospholipase A2 group IIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0003824 Catalytic activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGlycerophospholipid biosynthetic pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInducible T-cell costimulato\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005886 Plasma membrane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eT-cell antigen receptor (TCR) pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFGF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFibroblast growth factor 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005104 Cytokine activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAngiogenesis Signaling Pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDown Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMPZL2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMyelin protein zero like 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005515 Junctional adhesion molecule-like binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCalcium signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHHAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHedgehog acyltransferase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005525 Protein amino acid binding, glycoprotein binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHedgehog signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNPY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeuropeptide Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0001664 G protein-coupled receptor binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ecAMP signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAQP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAquaporin 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0015250 Water channel activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSalivary secretion signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBGLAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBone gamma-carboxyglutamate protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005179 cAMP generating peptide activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eParathyroid hormone synthesis signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003e35d ND vs. HD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eUp Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUSP21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUbiquitin specific peptidase 21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0006511 Ubiquitin-dependent protein catabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNecroptosis signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePIPOX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePipecolic acid and sarcosine oxidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0008115 Sarcosine oxidase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGlycine, serine and threonine metabolism pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFGF6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFibroblast growth factor 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005615 Intercellular space\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCell adhesion signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTXN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCortexin 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO:0005515 Protein amino acid binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAutophagy pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eDown Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSMAD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTGF-beta antagonist Smad3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.90E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0009785 RNA polymerase II cis-regulatory region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTGF-beta signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePMCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePro-melanin concentrating hormone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0005179 Manin-concentrating hormone activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePeptide ligand-binding receptors signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHNRNPK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeterogeneous nuclear ribonucleoprotein K-like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.44427E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0003676 DNA binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSUMOylation of RNA binding signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHINT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHistidine triad nucleotide binding protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.37338E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0005080 Protein kinase C binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRemdesivir signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCDHB4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProtocadherin beta-15-like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 00055095 Calcium ion binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePI3K-Akt-mTOR-signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNKX2-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNK2 homeobox 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 00009815 RNA polymerase II-specific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTranscription factors signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e42d ND vs. HD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eUp Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSTAT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignal transducer activator of transcription 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0009815 DNA-binding transcription factor activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eJak-Stat signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePTGS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProstaglandin-endoperoxide synthase 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0004666 Prostaglandin-endoperoxide synthase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eIL17 signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGUCA1B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGuanylate cyclase activator 1B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0008048 Calcium sensitive guanylate cyclase activator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAutophagy pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCYSLTR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCysteinyl leukotriene receptor 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0004974 Leukotriene receptor activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNeuroactive ligand-receptor interaction pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDown Regulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSLC26A3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSolute carrier family 26 member 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0005452 Inorganic anion antiporter activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTransmembrane transporters signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eORM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOrosomucoid 1 (ovoglycoprotein)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0005576 Collagen-containing extracellular matrix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVitamin D receptor pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eISL2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eISL LIM homeobox 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0000987 Cis-regulatory region sequence-specific DNA binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTranscription factors signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIL15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterleukin 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGO: 0005126 Cytokine receptor binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOlfactory signaling pathway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e1,2\u003c/sup\u003e Abbreviations: GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWnt family member 16 (WNT16) was identified as a downregulated, differentially expressed gene between the ND and HD groups at day 21. Wnt proteins regulate neuronal stem and progenitor cell activity within the nervous system, as evidenced by anatomical phenotypes in mouse Wnt mutants [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, Wnt16 influences melanogenesis in chickens and is implicated in neural crest lineage development [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Evidence suggests interaction between Wnt and mTOR signaling pathways in governing cell differentiation, proliferation, and critical neurodevelopmental processes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, the amino acid peptide neuropeptide Y (NPY) exerts a significant orexigenic effect within the central regulation of appetite, weight gain, and energy homeostasis in chickens [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This study established the involvement of candidate genes NPY and POMC in energy balance, food intake, and body metabolism, linking them to the adipocytokine signaling pathway\u0026mdash;crucial for energy homeostasis and metabolic regulation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The results indicate heightened hypothalamic activity on days 21 and 28, associated with physiological processes governing growth, development, and food intake. Significant differences in the expression of SMAD family member 3 (SMAD3) and solute carrier family 26 member 3 (SLC26A3) were also observed between groups at 35 and 42 days of age. The transcription factor SMAD3, essential for TGF-β signaling, possesses an N-terminal MH1 domain that interacts with diverse proteins and binds the Smad binding element (SBE), thereby regulating immune responses, growth, development, and neuronal maintenance via the TGFβ superfamily [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the present study, the altered expression patterns in broilers reared under HD conditions correlate with their physiological transformations. Broilers in high-density environments encounter diverse stressors. Variations in SMAD3 expression may mediate immune responses to environmental stressors. The neurotransmitter SLC26A3, commonly termed a serotonin transporter, functions in both peripheral and central nervous system pathways [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Indirect evidence indicates SLC26 transporters may constitute targets or determinants of oxidative stress, particularly when dysregulated or deficient [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The temporal expression profiles of these DEGs demonstrate how elevated stocking density elicits dynamic alterations in stress responses throughout the growth cycle.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIt was demonstrated that high stocking density (HD) significant influenced broiler growth and development, with distinct temporal patterns exhibited in serum indices and hypothalamic transcriptomics. Similar performance was displayed by ND and HD broilers from days 1 to 21, whereas significant differences were observed between days 21 and 42. Moreover, notable fluctuations in serum cortisol and insulin-like growth factor-1 were exhibited on days 28 and 42. The HD group exhibited elevated levels of serum inflammatory factors, including TNF-α, IL-1β, IL-4, and IL-10, particularly during days 35 to 42. Stage-dependent variations in hypothalamic molecular changes were demonstrated. At 21 days, neuroactive ligand-receptor and cytokine-cytokine receptor pathways were significantly enriched, accompanied by the upregulation of \u003cem\u003eNMU\u003c/em\u003e and \u003cem\u003ePY\u003c/em\u003e. At 28 days, fatty acid metabolism pathways were found to predominate. Between 35 and 42 days, Notch signaling, oxidative phosphorylation, and apoptosis pathways were enriched, with differential expression of \u003cem\u003eSMAD3\u003c/em\u003e and \u003cem\u003eSTAT2\u003c/em\u003e observed, which were associated with stress-induced apoptosis, alterations in energy metabolism, and immune abnormalities. Stage-specific hypothalamic molecular changes were revealed by this study, indicating that broiler performance, serum indices, and hypothalamic mechanisms were adversely affected by high stocking density. Further investigation of hypothalamic physiological alterations was recommended.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank the College of Animal Science and Technology of Henan University of Science and Technology for the use of the experimental facilities and thank the International Joint Lab for Animal Welfare and Health Breeding of Henan Province and the Expat Scientist Studio for Animal Stress and Health Breeding of the Province Henan for the kind scientific advice on this experiment.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eInvestigation, D.B. (Dongying Bai) and Y.Z. (Yi Zhang); methodology, D.B. Y.Z. and X.H.; software, Y.W. and B.Z.; data curation, Y.W. and X.X.; writing and preparation of the original draft, D.B. and Y.Z.; result interpretation, D.B. Y.Z. and X.H; writing, reviewing and editing, Y.Z. X.H. D.B. W.Z. K.I. and B.Z.; supervision, Y.M.; and project administration, Y.M. All authors have read and agreed to the submitted version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe National Key Research and Development Program of China (2024YFE0111600); the Key Research and Development Program of Henan Province (241111113800); the Program for International S\u0026amp;T Cooperation Projects of Henan Province (232102521012); the Key Research and Development and Promotion of Special (Science and Technology) Project of Henan Province (242102110018); and Natural Science Foundation of Henan Province (252300421652).\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed in this study are included in this published article as well as its supplementary materials. The raw sequencing data generated in this study are available in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1231983. The dataset is hosted in the NCBI SRA repository at: https://www.ncbi.nlm.nih.gov/sra?term=PRJNA1231983\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe animal study protocol was approved by the Animal Care and Use Committee of Henan University of Science and Technology, and the management and experimental procedures of the experimental animals were in accordance with the regulations of the Institutional Animal Care and Use Committee.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisclosure of Conflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors of the submitted manuscript declare that there are no conflicts of interest and that the manuscript has been approved for publication by all authors. I would like to declare on behalf of my co-authors that the work described is original research that has never been published and is not being considered for publication elsewhere.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAderibigbe AS, Ajuwon KM, Adeola O. Dietary phosphorus level regulates appetite through modulation of gut and hypothalamic expression of anorexigenic genes in broiler chickens. Poult Sci. 2022; 101(2): 101591. https://doi.org/10.1016/j.psj.2021.101591 \u003c/li\u003e\n\u003cli\u003eAguado A, Gal\u0026aacute;n M, Zhenyukh O, et al. Mercury induces proliferation and reduces cell size in vascular smooth muscle cells through MAPK, oxidative stress and cyclooxygenase-2 pathways. Toxicol Appl Pharmacol. 2013; 268(2): 188-200. https://doi.org/10.1016/j.taap.2013.01.030 \u003c/li\u003e\n\u003cli\u003eBalland E, Dam J, Langlet F, \u003cem\u003eet al\u003c/em\u003e. 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Poult Sci. 2024; 103(7): 103825. https://doi.org/10.1016/j.psj.2024.103825\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"High-density stocking, Hypothalamus, Transcriptomic, Growth performance, Broiler","lastPublishedDoi":"10.21203/rs.3.rs-7091754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7091754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe prevalence of high stocking density (\u003cb\u003eHD\u003c/b\u003e) in the broiler industry has been significantly increased, exerting profound implications for the physiology, behavior, and welfare of chickens, particularly concerning the regulation of their nervous system, specifically the central nervous system. Conversely, the impact of HD stress on the neurophysiological function of the broiler hypothalamus remained unknown.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThis study was conducted to investigate the effects of varying stocking densities on growth performance, serum biochemistry, and hypothalamic transcriptome in chickens across distinct developmental stages, specifically at 21, 28, 35, and 42 days of age. Two density conditions were utilized: normal density (ND) at 14 birds per square meter (m\u0026sup2;) and high density (HD) at 22 birds per square meter (m\u0026sup2;). Results indicated that no significant differences in growth performance were observed during the initial 21 days; however, significant reductions in body weight and feed intake were observed in the HD group from days 22 to 42. At 35 and 42 days of age, serum concentrations of IL-4 and TNF-α were significantly elevated in the HD group compared to the ND group. Gene mapping success rates were observed to range from approximately 37.44\u0026ndash;92.93%. A total of 42.48\u0026nbsp;million raw sequencing reads were generated per sample. Significant differential enrichment in KEGG pathways was detected between the ND and HD groups on days 21, 28, 35, and 42. Significant alterations were identified across several key signaling pathways, including pyruvate metabolism; oxidative phosphorylation; alpha-linolenic acid metabolism; neuroactive ligand-receptor interactions; Wnt signaling; Notch signaling; and apoptosis signaling. Candidate genes including \u003cem\u003eSMAD3\u003c/em\u003e, \u003cem\u003ePPY\u003c/em\u003e, \u003cem\u003eSLCO1C1\u003c/em\u003e, \u003cem\u003eWnt16\u003c/em\u003e, and \u003cem\u003eNMU\u003c/em\u003e were identified as critical for central nervous system immunity. Furthermore, NPY, STAT2, \u003cem\u003eSLC26A3\u003c/em\u003e, and \u003cem\u003eISL2\u003c/em\u003e regulate feeding behavior.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings provide critical insights for investigating the effects of HD stress on broiler growth performance, serum biochemical parameters, and hypothalamic transcriptome.\u003c/p\u003e","manuscriptTitle":"Novel insights into the transcriptomic changes of the hypothalamus in broilers exposed to high-density stocking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 08:30:32","doi":"10.21203/rs.3.rs-7091754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b594d644-cd64-40b1-a78a-5c93684e9bb2","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T07:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-16 08:30:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7091754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7091754","identity":"rs-7091754","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}