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. 2025 Jun 6;104(9):105401. doi: 10.1016/j.psj.2025.105401

The comparative physiological, biochemical and whole transcriptome analysis of Ross 308 broilers under different wind conditions

Md Mortuza Hossain a,1, Sanghoon Lee a,1, Yeonhee Park a,1, Jinhyun Ahn b, Woncheol Park c, Hyeran Kim d, Yujin Ko a, Soo-Youn Choi e, Sung-Pyo Hur f, Dajeong Lim g, Donghyun Shin h, Jong-Eun Park a,⁎
PMCID: PMC12414271  PMID: 40570460

Abstract

Chickens are one of the most economically and nutritionally important livestock species globally, yet they are highly vulnerable to environmental stressors which compromise their growth and physiological stability. In this study we investigated the effect of wind speed on growth performance, physiological responses, blood profiles, and transcriptomic adaptations in Ross-308 broilers under high-temperature conditions. A total of 240 broilers were divided into three groups: low wind, high temperature (LWH; 33°C, 60% RH, 0 m/s), medium wind, high temperature (MWH; 33°C, 60% RH, 1 m/s), and high wind, high temperature (HWH; 33°C, 60% RH, 2 m/s). The results showed that broilers in the HWH and MWH groups had significantly higher feed intake, body weight gain, and improved thermoregulation, as indicated by lower rectal temperatures and respiration rates compared to the LWH group. Additionally, serum potassium and phosphorus levels were higher in the HWH and MWH groups, indicating enhanced metabolic stability under heat stress. Transcriptomic analysis identified differentially expressed genes associated with the heat stress response, such as HSPB9, THBS1, TLR2, EHHADH, and PDK4. Functional enrichment analysis identified pathways related to vital processes, innate immune responses, and biosynthetic pathways, which contribute to thermal resilience. Upregulated genes like PDK4 and heat shock proteins were associated with improved energy efficiency and stress response, whereas downregulated genes in LWH indicated compromised growth. This study underscores the vital role of wind speed in ameliorating heat stress effects in broilers, laying the groundwork for enhanced environmental management and potential genetic and nutritional strategies in poultry production.

Keywords: Broiler, Heat stress, Thermoregulation, Transcriptome, Wind speed

Introduction

The poultry industry struggles with sustainability and productivity as rising global temperatures induce thermal stress in broilers, disrupting their physiological homeostasis, metabolism, and well-being (Brugaletta et al., 2022). When birds are exposed to ambient temperatures beyond their acceptable range, they struggle to maintain a stable body temperature, a condition referred to as "heat stress" (Lara and Rostagno, 2013). Under stressed conditions broilers exhibit a myriad of physiological responses including increased respiration rates, altered electrolyte concentrations, and perturbations in metabolic pathways (Toyomizu et al., 2005; Livingston et al., 2022). Moreover, fast-growing broiler strains exhibit greater sensitivity to heat stress compared to their slower-growing counterparts (Huerta et al., 2023). During heat stress conditions, birds exhibit marked physiological and behavioral changes, including reduced feed intake, altered metabolic processes, compromised immune function, and increased mortality rates (Bohler et al., 2021). Heat stress has been linked to changes in activity, drinking, and feeding patterns, with heat-stressed broilers, laying hens, and ducks showing decreased feed intake and increased water intake (Park et al., 2018; Rodrigues et al., 2019; Kim et al., 2022). The physiological response to heat stress in poultry activates the sympathetic adreno-medullary (SAM) pathway, releasing catecholamines like epinephrine and norepinephrine, followed by the activation of the hypothalamic–pituitary–adrenal (HPA) axis, leading to glucocorticoid (GC) secretion (Li et al., 2022; Oluwagbenga and Fraley, 2023). Heat stress triggers complex cellular responses, including the upregulation of heat shock proteins (HSP), which help maintain cell homeostasis by altering gene expression, reconstituting it to defend against stress, and addressing inflammation, immunity, oxidant stress, and thermoregulation (Varasteh et al., 2015; Kim et al., 2021). The HSPA (HSP70) gene stands out as the most thoroughly researched member and is frequently utilized as a stress biomarker within the HSP gene family (Varasteh et al., 2015). HSP expression is regulated by heat shock factors and has been identified in other animals, including mammals, fish, and poultry (Cedraz et al., 2017). Previous research has highlighted the interaction between environmental factors, such as temperature and humidity, and genetic predispositions in shaping broiler responses to heat stress (Nawab et al., 2018; Kim et al., 2022). These changes collectively result in significant economic losses and compromise animal welfare. Generations of selective breeding in commercial broiler lines, combined with their rearing in controlled environments, have increased their vulnerability to extreme environmental conditions (Rothschild and Plastow, 2014; Fleming et al., 2017), and this will be a hindrance for the expansion of the poultry industry in the next decades where the temperature is increasing. To address the adverse effects of heat stress, various mitigation strategies have been explored, including dietary modifications, water cooling, and environmental adjustments (Tsuji et al., 2016; Nawab et al., 2018; Onagbesan et al., 2023). Broilers exposed to 1.5–2.0 m/s had significantly higher weight gain, feed intake, and feed efficiency compared to those at lower air speed (Yahav et al., 2001; Hamrita and Conway, 2017). Increasing air circulation in poultry housing enhances convective heat loss, improving thermal comfort, reducing core body temperature, and alleviating the effects of heat stress in birds (Nawab et al., 2018; Onagbesan et al., 2023). To address environmental stresses using genetic approaches, it is essential to study livestock species that have naturally adapted to those conditions over time (Fleming et al., 2017). Higher environmental temperatures drive hyperthermia, increasing body temperature and respiration rate to boost evaporative heat loss (Tsuji et al., 2016). One primary defense against hyperthermia is the activation of HSPs, which are essential for cellular protection and adaptation to heat stress (Varasteh et al., 2015). Besides that, other genes activated during heat stress include those involved in antioxidant defense, inflammatory responses, metabolic regulation, and protein repair and genes encoding antioxidant enzymes like Superoxide dismutase and Catalase (Varasteh et al., 2015; Kim et al., 2021; Khan et al., 2024). To minimize the effect of heat stress, high wind speed works effectively. However, the underlying molecular and transcriptomic mechanisms have not been studied yet. Understanding these processes is essential for developing targeted interventions to improve poultry resilience in hot climates. Transcriptomic analysis using RNA Sequencing (RNA Seq) can enhance understanding of the molecular and functional mechanisms driving physiological changes in poultry under heat stress conditions. This study investigates the effects of wind speed on broiler physiological responses and electrolyte balance under high-temperature conditions. Additionally, RNA sequencing is used to identify differentially expressed genes in the blood of heat-stressed Ross 308 broilers, focusing on gene functions and metabolic pathways. This study aims to enhance understanding of transcriptomic mechanisms of adaptations to thermal challenges, enabling more effective heat stress mitigation strategies.

Materials and methods

Experimental design

The animal experiments were performed following ethical guidelines, approval number 2020-429, in the National Institute of Animal Science (NIAS). A total of 240, female Ross 308 broilers of 25 days old with an average weight of 1100 ± 4 g, were randomly distributed into three experimental treatment groups. Each group consisted of four replicate pens, with each pen (1.05 m × 1.8 m) housing 20 birds. Pens were randomly assigned to each treatment group to minimize allocation bias. All environmental conditions (temperature, humidity, lighting, ventilation setup, and diet) were kept identical across chambers, with wind speed being the only variable altered. The birds were kept in a controlled environment chamber located at the Livestock Metabolism Research Center of the Agricultural Research Institute. During the experimental period, the chickens were provided ad libitum access to a test diet formulated primarily with corn and soybean meal, meeting the nutritional requirements outlined in the Korean Feeding Standard for Livestock (containing 19% crude protein and 2850 kcal ME). After a 3-day acclimatization period at a controlled temperature of 21°C and 60% relative humidity, the broilers were subjected to a high-temperature regime with a Temperature Humidity Index (THI) of 85, equivalent to 33°C and 60% relative humidity, from 25 to 38 days of age (Fig. 1). The THI was calculated based on the formula developed by the National Research Council (NRC, 1971): THI = (1.8 × T + 32) – [(0.55 – 0.0055 × RH) × (1.8 × T – 26.8)], where T is the temperature in°C and RH is the relative humidity in percent. According to the THI classification for laying hens (Xin and Harmon, 1998), values are categorized as comfort (THI < 70), alert (70 < THI < 75), danger (76 < THI < 81), and emergency (THI > 81). A THI of 85, therefore, represents severe heat stress (Lallo et al., 2018), and this condition was maintained for 14 consecutive days. The treatment groups were exposed to three wind speed levels: LWH (low wind speed, 0 m/s), MWH (moderate wind speed, 1 m/s), and HWH (high wind speed, 2 m/s). To simulate field conditions, a daily temperature cycle was implemented, starting at 26°C, increasing to 33°C for 8 h, and then returning to 26°C, while controlling both temperature and humidity levels throughout the experiment. Environmental conditions, including temperature and humidity, were recorded every 30 min using automatic data loggers (Model 174H; Testo, Lenzkirch, Germany), while wind speed was measured twice daily with an anemometer.

Fig. 1.

Fig 1

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Schematic experimental design.

Sample collection and measurements

All the birds were weighed on the starting, days 7 and 14 of the experiment and feed intake was recorded daily. From the body weight and feed intake data, average daily gain (g/day/bird), average daily feed intake (g/bird/day) and feed conversion ratio were calculated.

Physiological measurements and blood sampling were conducted between 13:00 and 14:00, during the peak heat period (33°C) in the diurnal cycle, to capture maximal heat stress responses. Respiration rate and rectal temperature were recorded on days 0, 7, and 14 from 10 randomly selected birds per treatment. Respiration rate was measured by counting thoracic movement over 1 min, and rectal temperature was measured with a thermometer inserted 1 cm into the rectum.

Blood samples were collected from the wing vein of 10 randomly selected birds per treatment on days 0 and 14. Serum was separated by centrifugation and analyzed for blood metabolites using an automatic blood analyzer (Hitachi 7180 Chemistry Analyzer, Hitachi High-Tech Corporation, Tokyo, Japan). Electrolyte concentrations (Na⁺, K⁺, Cl⁻) were measured with an electrolyte analyzer (I-Smart 300 Blood Gas Analyzer, i-SENS, Seoul, South Korea).

RNA isolation and sequencing

A total of nine random blood samples were collected via the wing vein from three birds in each group and preserved in blood RNA preservation tubes (TempusTM Blood RNAtubes, Life Technologies, Carlsbad, CA, USA) for RNA isolation. RNA was extracted from these samples using the TRIzol:Chloroform method, following the manufacturer’s instructions. The quality and purity of the RNA were assessed using an Agilent Bioanalyzer (Model 2100, Agilent Technologies, Santa Clara, CA, USA) with the RNA Nano 6000 Assay Kit. Samples with RNA integrity numbers (RIN) exceeding 8 were selected for library preparation. Libraries were created from random cDNA fragments, which were subsequently ligated to adapter fragments according to the RNA library preparation kit (TruSeq Stranded Total RNA LT Sample Prep Kit, Illumina, San Diego, CA, USA). Sequencing was performed on the Illumina HiSeq 2500 platform, generating paired end reads. The BCL image files were converted into FASTQ raw reads for analysis using Illumina’s bcl2fastq software.

Quality control, mapping, and counting

The quality of raw sequencing reads was initially assessed using FastQC v0.11.9 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc). Low-quality reads, including those with N bases or a quality score of <Q20, were trimmed using Trimmomatic v0.39 for quality filtering, employing the paired-end option (Bolger et al., 2014). Filtered reads were then utilized to create an index file, and alignment was performed against the chicken reference genome (GRCg6a) using Hisat2 v2.2.1 (Kim et al., 2019). The aligned reads were processed to generate a count matrix through FeatureCounts from the Subread package v2.0.1 (Liao et al., 2014). A principal component analysis (PCA) plot was used to visualize and compare the data before and after correction using the computed principal component values. Finally, the corrected count files were employed for differential gene expression (DEG) analysis.

Differentially expressed genes analysis

The DEGs between the two groups were determined using the edgeR package (v3.32.0) in R (v4.0.3) (Robinson et al., 2010). Normalization and dispersion estimates were obtained during the correction phase by applying the Cox–Reid profile-adjusted likelihood method. Genes were classified as DEGs if they met the criteria of a false discovery rate (FDR) < 0.05 and log base 2 of the fold change |log2FC| ≥ 1 (Benjamini and Hochberg, 1995). The identified DEGs were used to generate a heatmap to visualize differences between the two groups, with genes further categorized as upregulated or downregulated based on their fold change values. Data visualization, including PCA plots, bar plots, and heatmaps, was performed using ggfortify v0.4.11, ggplot2 v3.3.3, and the pheatmap package v1.0.12 in R.

Gene ontology, and network analysis

The identified DEGs underwent enrichment analysis through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform (Huang et al., 2009a). Gene annotation was carried out to classify their associated molecular functions, cellular components, biological processes, and pathways. For the analysis, Gallus species were selected as the category in the DAVID tool, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used under default settings (Huang et al., 2009b; Kanehisa et al., 2017). Additionally, ClueGO, a plugin, was used for pathway analysis in the GO and KEGG databases. This tool provided a visualization of biological term enrichment and generated functionally grouped networks related to the DEGs (Bindea et al., 2009; Kanehisa et al., 2017).

Statistical analysis

The physiological parameters of the animals were compared among the three groups using one-way ANOVA, with statistical analyses conducted using SAS software (SAS Institute Inc., Cary, NC, USA). The significance of differences among the mean values of the investigated parameters was determined using Tukey’s multiple comparison test. Differentially expressed genes were analyzed as previously described. For the physiological parameters, P < 0.05 was considered as statistically significant.

Results

Broiler performance and physiological responses

The effect of wind speed levels on the growth performance of broilers in a high-temperature environment is illustrated in Fig. 2. On day 7 and 14, broilers in high wind and high temperature (HWH) and medium wind and high temperature (MWH) group exhibited higher (P < 0.05) body weights compared to those in low wind and high temperature (LWH). Average daily gain during days 0-7 increased (P < 0.05) in the HWH and MWH group compared to the LWH group. Additionally, daily feed intake during days 0-7 increased (P < 0.05) in HWH compared to the LWH group.

Fig. 2.

Fig 2

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Different levels of wind speed under high-temperature conditions on the growth performance of broilers. (a) body weight, (b) body weight gain, (c) average daily feed intake, (d) feed conversion ratio (FCR); LWH (Low Wind, High Temperature), MWH (Medium Wind, High Temperature), HWH (High Wind, High Temperature); Bars represent mean ± standard error; Bars with different letters (a, b, c) are significantly different from each other (P < 0.05).

On days 7 and 14, broilers in the LWH group exhibited the highest (P < 0.05) respiration rates, followed by the MWH and HWH groups, while the LWH group also had the highest (P < 0.05) rectal temperatures on day 7 (Fig. 3). Sodium (Na+) and Chloride (Cl-) levels remained unaffected (P > 0.05), while Potassium (K+) levels were significantly higher (P < 0.05) in the MWH and HWH groups compared to LWH on day 14 (Fig. 4). The impact of wind speed levels on serum metabolite concentrations in broilers within a high-temperature environment is shown in Fig. 5. Blood Phosphorus levels differed significantly on day 14, with higher concentrations in the HWH and MWH groups compared to LWH (P < 0.05). No significant differences (P > 0.05) were observed among different treatments groups for serum glucose, calcium, total protein, albumin, globulin, albumin/globulin ratio, alkaline phosphatase, gamma-glutamyl transpeptidase, cholesterol, amylase, and lipase during experimental period.

Fig. 3.

Fig 3

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Different levels of wind speed under high-temperature conditions on physiological of broilers. (a) respiration rate, (b) rectal temperature. LWH (Low Wind, High Temperature), MWH (Medium Wind, High Temperature), HWH (High Wind, High Temperature); Bars represent mean ± standard error; Bars with different letters (a, b, c) are significantly different from each other (P < 0.05).

Fig. 4.

Fig 4

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Different levels of wind speed under high-temperature conditions on the electrolyte concentrations in serum of broilers. (a) Sodium ion, concentration (B) potassium ion concentration. LWH (Low Wind, High Temperature), MWH (Medium Wind, High Temperature), HWH (High Wind, High Temperature); Bars represent mean ± standard error; Bars with different letters (a, b, c) are significantly different from each other (P < 0.05).

Fig. 5.

Fig 5

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Different levels of wind speed under high-temperature conditions on the blood metabolite concentrations in broilers; LWH (Low Wind, High Temperature), MWH (Medium Wind, High Temperature), HWH (High Wind, High Temperature); Bars represent mean ± standard error; Bars with different letters (a, b, c) are significantly different from each other (P < 0.05).

Construction of the raw reads, mapping

We investigated the impact of heat stress on broilers’ gene expression high throughput RNA-Seq analysis. A total of 9 samples from the birds generated raw read count from 60,178,662 to 86,546,554. The Q20 and Q30 percentages were 97.50% to 98.20% and 93.70% to 94.9%. The CG percentages were 43.70% to 44.40%, and the raw reads were mapped with a uniquely mapped percentage ranging from 85.30% to 89.26% to the chicken reference genome. Comprehensive details are available in Supplementary Table 1.

Identification of DEGs

Using the edgeR package with thresholds of FDR < 0.05 and |log2FC| ≥ 1, a total of 338 DEGs were identified from 25,466 genes analyzed. In Fig. 6(a), PCA plot illustrates the clustering of samples based on gene expression profiles. The clear separation of samples indicates distinct transcriptional profiles among the three groups. And Fig. 6(b) Venn diagram displaying the distribution of DEGs identified in pairwise comparisons among the three conditions (LWH vs. HWH, LWH vs. MWH, and MWH vs. HWH). The numbers in each section represent the count of DEGs unique or shared among comparisons. Upregulated (↑) and downregulated (↓) genes are indicated in green and red, respectively. In the comparison between LWH and HWH, 166 genes were significantly up-regulated, while 172 genes were down-regulated. The comparison between LWH and MWH revealed 207 DEGs, comprising 88 upregulated and 119 downregulated genes. Lastly, the comparison between MWH and HWH comparison identified 76 DEGs, including 34 upregulated and 42 downregulated genes. The top 10 DEGs were up regulated in all three groups are given in Table 1 and the top 10 downregulated DEGs in all three groups are given in Table 2 and Fig. 07 for each comparison. Fig. 08 illustrates the expression patterns as a heatmap, along with the clustering of DEGs and samples. Excluding LOC identifiers, the top five upregulated DEGs were Transmembrane Protein 45A (TMEM45A), Synaptotagmin 1 (SYT1), Spermine Oxidase (SMOX), Marker of Proliferation Ki-67 (MKI67), and Synaptotagmin Like 5 (SYTL5), while the top five downregulated DEGs were MicroRNA 12261 (MIR12261), Phospholipid Scramblase 5 (PLSCR5), LIM Homeobox 2 (LHX2), Vertebrae Development Associated (VRTN), and Usher Syndrome Type 1C Protein (USH1C). Detailed information on DEGs from different group comparisons can be found in Supplementary Tables 2, 3 and 4.

Fig. 6.

Fig 6

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Transcriptomic response in different levels of wind speed under high-temperature conditions; (a) Principal Component Analysis (PCA) plot of sample relationships based on individual transcriptomes before separation into differentially expressed genes (DEGs) under LWH (red), MWH (light green), and HWH (dark green) conditions. (b) Venn diagram of significant DEGs. DEGs were identified based on the cutoff thresholds of absolute log2 fold change >1.0 and false discovery rate (FDR) <0.05. Circle sizes correspond to the total number of significant DEGs in each comparison: LWH vs HWH, LWH vs MWH, and MWH vs HWH. Numbers indicate significantly upregulated (↑) and downregulated (↓) genes in each set. Shared regions represent overlapping DEGs across the comparisons.

Table 1.

List of upregulated genes in all three groups.

LWH vs HWH LWH vs MWH MWH vs HWH
TMEM45A EPHX3 ERNI
SYT1 CYP1A2 RHOV
SMOX SYTL5 MKI67
MKI67 SIDT1 PTPRE
SYTL5 CD69L EPB41L1
VDHAP CERS6 FAM81A
PTPRE CLRN2 ZFPM2
NKD1 TRAIP V-BG
FAM173A PHLDA1 OSCP1
PLCH1 MHCYL GNAT3
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Table 2.

List of down regulated genes in all three groups.

LWH vs HWH LWH vs MWH MWH vs HWH
MIR12261 ERNI POU3F4
PLSCR5 C3orf67 USH1C
LHX2 PLSCR5 PARVA
VRTN TMEM255B PTPN13
USH1C GALT LENG9L7
TMEM255B ZFPM2 MHCY11
PLSCR1 PLSCR1 RASSF6
MANSC1 HPGDS RNF217
HBBR TTC12 RXRA
SLC5A7 SAMD9L TM4SF4
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Fig. 7.

Fig 7

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Volcano plots showing differentially expressed genes (DEGs) in pairwise comparisons: (a) LWH vs HWH, (b) LWH vs MWH, and (c) MWH vs HWH. The x-axis represents log2 fold changes (log2FC), and the y-axis represents the -log10 false discovery rate (FDR). Upregulated genes (red) and downregulated genes (blue) were determined based on thresholds of |log2FC| > 1.0 and FDR < 0.05, while non-significant genes are shown in gray. Top DEGs are labeled in each plot.

Fig. 8.

Fig 8

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Heatmaps of differentially expressed genes (DEGs) across experimental comparisons: (a) LWH vs HWH, (b) LWH vs MWH, and (c) MWH vs HWH. Rows represent DEGs, while columns represent individual samples. Expression values are z-score normalized and depicted on a color scale, with red indicating upregulation and blue indicating downregulation. Clustering dendrograms show group similarities based on gene expression profiles.

Function and pathway analysis of the DEGs

The 338 identified DEGs were analyzed for functional annotation using Gene Ontology (GO) analysis through the DAVID server, with a significance cutoff of FDR < 0.05 and |log2FC| ≥ 1. The analysis revealed that the DEGs were associated with five biological processes: viral process (GO:0016032), innate immune response (GO:0045087), triterpenoid biosynthetic process (GO:0016104), cholesterol biosynthetic process (GO:0006695), and ergosterol biosynthetic process (GO:0006696). Additionally, three molecular functions were identified: GTP binding (GO:0005525), lanosterol synthase activity (GO:0000250), and aspartic-type endopeptidase activity (GO:0004190). For cellular components, the DEGs were linked to lipid droplets (GO:0005811). Detailed GO annotations are presented in Fig. 9 and Supplementary Table 5. Further pathway analysis was conducted to explore the molecular interactions and biological roles of these DEGs.

Fig. 9.

Fig 9

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Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) in LWH vs HWH comparison. The bar chart shows the number of DEGs associated with enriched GO terms across three categories: Biological Processes (blue), Cellular Components (green), and Molecular Functions (red).

Network enrichment analysis

Enrichment analysis of the identified DEGs was conducted to pinpoint significant pathways, utilizing the hypergeometric test and applying the Benjamini–Hochberg method for multiple comparison correction (P < 0.10). Elliptical shapes and triangles represent upregulated and downregulated genes, respectively, while pathways are depicted in distinct colors (Fig. 10). Key pathways included Arginine and proline metabolism, Fatty acid catabolic process, Negative regulation of viral genome replication, Phospholipid translocation, and Neuroblast division. These pathways highlight critical biological processes and molecular functions impacted by the experimental conditions.

Fig. 10.

Fig 10

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Enriched gene network of Gene Ontology (GO) terms associated with differentially expressed genes (DEGs) in (a) LWH vs HWH and (b) LWH vs MWH comparison. Each cluster represents interconnected GO terms, grouped by biological relevance. Ellipse represent upregulated genes, while triangles represent downregulated genes. Node size corresponds to the number of associated genes, and edges indicate shared genes between terms.

Discussion

This study investigated the effects of wind speed on broilers exposed to high-temperature conditions, focusing on growth performance, physiological responses (respiration rate and rectal temperature), blood electrolyte levels, and transcriptomics. The results demonstrated that higher wind speeds significantly alleviated heat stress, leading to improved growth, thermoregulation, and metabolic stability. Our findings demonstrate that higher wind speeds significantly improved broiler growth performance, as indicated by higher body weight gain and feed intake in the HWH and MWH groups. The increased intake suggests that higher wind speeds alleviate heat stress, allowing birds to consume more nutrients and sustain growth. This aligns with previous research showing that broilers exposed to higher air velocities (1.5–2.0 m/s) at 35°C exhibited higher weight gain than those in lower wind speed conditions (Yahav et al., 2001). The reduction in thermal discomfort likely enabled better feed utilization and energy allocation towards growth rather than stress adaptation.

Broilers in the LWH group exhibited higher respiration rates and rectal temperatures, indicating greater difficulty in maintaining body temperature under low wind conditions. In contrast, the HWH group showed lower respiration rates and rectal temperatures, suggesting improved thermoregulation through convective heat loss. Previous studies confirm that heat stress leads to hyperthermia, triggering increased respiration as a compensatory mechanism for evaporative cooling (Tsuji et al., 2016). In addition to the decline in feed intake, respiratory alkalosis is a contributing factor to the reduced growth rate observed under heat stress (Teeter et al., 1985; Ahmad and Sarwar, 2006; Hamrita and Conway, 2017). Excessive panting leads to CO₂ loss, disrupting blood pH and electrolyte balance, which can impair metabolic function and divert energy away from growth. The HWH group’s lower respiration rate suggests that higher wind speeds mitigate these effects, reducing respiratory distress and preserving energy for productive functions.

Heat stress is known to disrupt mineral balance, leading to potassium depletion and altered metabolic function. The higher potassium and phosphorus levels observed in the HWH and MWH groups indicate better electrolyte homeostasis and metabolic efficiency, which are essential for energy production and cellular stability. This supports previous findings that heat stress-induced respiratory alkalosis accelerates mineral loss, impacting growth and physiological function (Onagbesan et al., 2023). The improved metabolic stability in higher wind speed conditions may have contributed to better overall performance and resilience to heat stress (Nawab et al., 2018). While temperature and humidity were carefully regulated across all chambers, other environmental variables such as air pressure, noise from fans, or potential airflow inconsistencies were not directly measured. However, all chambers were constructed and equipped identically, and every effort was made to ensure wind speed was the only variable altered among groups. Future studies should consider evaluating these additional factors to further strengthen the interpretation of wind-mediated thermal relief.

Transcriptomic analysis between LWH and HWH groups revealed key molecular mechanisms behind heat stress alleviation by high wind speed. A total of 338 DEGs were identified, with 166 upregulated and 172 downregulated under high-temperature conditions. Enriched pathways and GO terms highlighted biological processes, molecular functions, and cellular components linked to physiological adaptation, thermoregulation, and improved growth performance in broilers. Among the upregulated genes, MKI67, THBS1, and PDK4 are of particular interest. Among the upregulated genes, MKI67 serves as a marker for cellular proliferation, implying enhanced capacity for tissue growth and repair in the HWH group. This is particularly significant under heat stress conditions, where cellular damage and repair mechanisms are often impaired. Similarly, the upregulation of THBS1, known for its role in angiogenesis and tissue remodeling, suggests that high wind speed conditions may facilitate improved vascular adaptation. Enhanced vascularization can contribute to better thermoregulation by supporting more efficient heat dissipation (Isenberg and Roberts, 2020). The upregulation of PDK4, a key regulator of energy metabolism, highlights an adaptive metabolic shift under high wind speed conditions. PDK4 inhibits pyruvate dehydrogenase activity, promoting the utilization of fatty acids over glucose for energy production (Zhang et al., 2014). This metabolic reprogramming likely reduces metabolic heat production, aligning with reduced thermal stress and improved energy efficiency in broilers exposed to high wind speeds.

The HWH group exhibited upregulation of genes associated with cellular stress responses, including HOPX and CD28. HOPX is known to regulate cellular differentiation and apoptosis, promoting tissue stability and adaptation to environmental challenges. Similarly, CD28, a key molecule in immune signaling, may enhance cellular resilience by modulating stress-induced inflammatory pathways and maintaining immune balance under heat stress. These gene expression changes suggest that high wind speed contributes to the stabilization of cellular functions, enabling broilers to better cope with heat-induced physiological stress. Notably, the downregulation of HSPB9 in the HWH group further supports the idea that higher wind speeds alleviate cellular stress. Heat shock proteins, such as HSPB9, are typically upregulated under severe stress to protect cells from damage (Kim et al., 2021; Hu et al., 2022). Therefore, reduced expression of HSPB9 implies that birds in the HWH group experienced less physiological stress due to improved environmental conditions. Given that heat shock proteins are widely used as biomarkers of animal stress and welfare, the decreased expression of HSPB9 provides molecular evidence of improved well-being in birds exposed to higher wind speeds (Basaki et al., 2020). This reinforces the practical value of wind speed control as a strategy to enhance both performance and animal welfare in poultry production systems. Additionally, enrichment analysis highlighted significant activation of cholesterol and ergosterol biosynthetic processes in the HWH group, suggesting enhanced lipid metabolism. These pathways play a crucial role in stabilizing cellular membranes, maintaining fluidity and structural integrity, and supporting efficient nutrient transport and growth under heat stress (Gil-Ramírez et al., 2016).

This study identifies key upregulated genes associated with thermoregulation and cellular adaptation under heat stress. TMEM45A is associated with intracellular organelles, potentially regulating membrane integrity and signaling pathways, while EPHX3 plays a crucial role in detoxification, influencing lipid metabolism and oxidative stress to maintain endoplasmic reticulum homeostasis (Hoopes et al., 2017; Chen et al., 2021). SYT1, a calcium-dependent protein crucial for synaptic transmission, suggests heightened neural activity, while CYP1A2, essential for drug metabolism via its monooxygenase activity, indicates potential changes in xenobiotic metabolism and environmental response (Thorn et al., 2012; Melland et al., 2022). RHOV, involved in protein kinase binding, may trigger signaling related to cell proliferation and differentiation, implicating a role in oncogenesis or tissue repair (Shepelev and Korobko, 2013). Together, these genes illustrate complex networks influencing physiological and pathological states, providing insights into cellular functions and potential therapeutic targets. SMOX regulates spermine catabolism, influencing cellular signaling, apoptosis, and polyamine metabolism for cell growth and survival, while SYTL5 enhances neurotransmitter release and receptor recycling through exocytosis and endocytosis, and MKI67 supports RNA binding, cell cycle progression, and proliferation, crucial for development and tumor biology (Xiong et al., 2019; Wu et al., 2021; Furbish et al., 2022; Huang et al., 2022). SIDT1 functions as an RNA transmembrane transporter, crucial for RNA regulation during viral infections or cellular stress. PTPRE (Protein Tyrosine Phosphatase Receptor Type E) enhances cell communication, affecting immune responses, while CD69L (Gamma-Glutamyl Phosphate Reductase) aids amino acid metabolism, influencing cell signaling (Peng et al., 2023). EPB41L1 is vital for maintaining cell shape and integrity, potentially associated with erythropoiesis pathologies. The common genes upregulated across all three groups, several key players emerge, highlighting their multifaceted roles in cellular functions. MKI67 is involved in cell cycle progression and proliferation, likely through RNA binding. PTPRE is a pivotal component in signal transduction as a protein tyrosine phosphatase receptor type, indicating enhanced cellular communication pathways. PLCH1, with its phosphatidylinositol phospholipase C activity, plays a critical role in phosphoinositide signaling pathways, affecting various cellular processes. LSM10, associated with U7 small nuclear RNA, is crucial for snRNA binding, potentially influencing RNA processing and stability. PTTG2, known for its ankyrin repeat domain, is involved in the trafficking between early and late endosomes, hinting at its role in intracellular transport mechanisms. GNAT3, a part of the heterotrimeric G-protein complex, is vital for signal transduction associated with G-protein-coupled receptors. Lastly, IL20RA, as the interleukin 20 receptor subunit alpha, participates in cytokine-mediated signaling pathways, underscoring its importance in immune response modulation. Together, these upregulated genes highlight a complex network of signaling pathways and cellular processes critical for maintaining cellular homeostasis and responding to extracellular stimuli.

The improvements observed in physiological responses and growth performance are closely reflected in the transcriptomic changes. For example, the upregulation of PDK4 suggests a metabolic shift toward fatty acid oxidation, which likely contributed to reduced heat production and improved energy efficiency (Pettersen et al., 2019). The gene MKI67, involved in cellular proliferation (Hu et al., 2020), may underline the enhanced tissue growth seen in birds from the HWH group, while THBS1 is linked to vascular remodeling, which supports better thermoregulation. These molecular adaptations are consistent with improved rectal temperatures, feed intake, and body weight gain observed under higher wind speed conditions.

While previous studies have shown that improved air circulation can enhance poultry productivity under heat stress (Yahav et al., 2001), our findings provide a mechanistic explanation for these benefits by linking increased wind speed to specific physiological and transcriptomic adaptations. These findings have practical implications for both poultry housing engineers and breeders. The advantages of increased wind speed on growth, thermoregulation, and metabolic stability emphasize the need to incorporate adjustable airflow systems into broiler house design, particularly in areas prone to heat stress. For breeders, identifying key genes associated with heat tolerance, such as PDK4, MKI67, and THBS1, provides potential genetic markers that can enhance selective breeding programs, leading to the development of more heat-resilient broiler lines. Furthermore, the insights from transcriptomic studies may support future genetic selection or nutritional interventions to improve broiler resilience in high-temperature conditions.

Conclusion

This study demonstrated that increasing wind speed significantly alleviates heat stress in Ross 308 broilers, enhancing their growth performance and physiological stability. Broilers exposed to higher wind speeds exhibited improved feed intake, body weight gain, and thermoregulation, as indicated by lower rectal temperatures and respiration rates. The observed increase in serum potassium and phosphorus levels suggests enhanced metabolic balance under high wind conditions. Transcriptomic analysis revealed 338 DEGs between the LWH and HWH groups. Key pathways involved in the heat stress response included viral processes, innate immunity, and biosynthetic pathways for triterpenoids, cholesterol, and ergosterol. Upregulated genes in the HWH group (PDK4, ASAH2, HSPs) indicated enhanced energy efficiency and stress response, while downregulated genes in the LWH group (FGFR2, CEP63) suggested impaired growth. Genes like MKI67, THBS1, and PDK4 highlighted the molecular mechanisms by which wind speed improves broiler performance. This study strongly supports the crucial role of wind speed in mitigating heat stress and improving broiler performance. Optimizing environmental management, particularly airflow in poultry houses, is crucial for enhancing poultry production under high temperatures. Future research should explore genetic and nutritional interventions to further improve heat tolerance in broilers.

Conflict of interest

The authors declare that there are no conflicts of interest associated with the research presented in this article.

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2023-00245099), and by the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ014916 or RS-2020-RD009051) of the Rural Development Administration, Republic of Korea.

Declaration of competing interest

The corresponding author, on behalf of all the authors, declares that there are no financial or personal relationships with other individuals or organizations that could inappropriately influence or bias the work presented in this manuscript.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2025.105401.

Appendix. Supplementary materials

mmc1.xlsx (9.6KB, xlsx)
mmc2.xlsx (21.5KB, xlsx)
mmc3.xlsx (17.9KB, xlsx)
mmc4.xlsx (12.2KB, xlsx)
mmc5.xlsx (10.9KB, xlsx)

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Associated Data

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Supplementary Materials

mmc1.xlsx (9.6KB, xlsx)
mmc2.xlsx (21.5KB, xlsx)
mmc3.xlsx (17.9KB, xlsx)
mmc4.xlsx (12.2KB, xlsx)
mmc5.xlsx (10.9KB, xlsx)

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