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. 2023 Nov 30;103(2):103340. doi: 10.1016/j.psj.2023.103340

Screening of heat stress-related biomarkers in chicken serum through label-free quantitative proteomics

Qijun Liang 1,1, Shuqian Huan 1,1, Yiduo Lin 1, Zhiqing Su 1, Xu Yao 1, Chengyun Li 1, Zeping Ji 1, Xiaohui Zhang 1,2
PMCID: PMC10770749  PMID: 38118221

Abstract

Heat stress (HS) can result in sudden death and is one of the most stressful and costly events in chicken. Currently, biomarkers used clinically to detect heat stress state in chickens are not optimal, especially for living ones. Analysis of changes in serum proteins of heat-stressed chickens can help to identify some novel convenient biomarkers for this. Twenty-four chickens were exposed to HS at 42°C ± 1°C with a relative humidity of 65% for continuous 5 h in a single day, and 10 birds were used as controls (Con). During HS, 15 dead chickens were categorized as heat stress death group (HSD), and 9 surviving ones served as heat stress survivor group (HSS). Label-free quantitative proteomics (LFQP) was used to analyze differentially expressed proteins (DEPs) in serum of tested animals. Candidate proteins associated with HS were validated by enzyme-linked immunosorbent assay (ELISA). Diagnostic value of candidate biomarkers was assessed using receiver operating characteristic (ROC) curve analysis. Source of the selected proteins was analyzed in liver tissues with immunohistochemistry and in cell culture supernatant of primary chicken hepatocytes (PCH) using ELISA. In this study, compared to Con, LFQP identified 123 and 53 significantly different serum proteins in HSD and HSS, respectively. Bioinformatics analysis showed that XDH, POSTN, and HSP90 were potential HS biomarkers in tested chickens, which was similar with results from serum ELISAs and immunohistochemistry in liver tissues. The ROC values of 0.793, 0.752, and 0.779 for XDH, POSTN, and HSP90, respectively, permitted the distinction of heat-stressed chickens from the control. Levels of 3 proteins above in the cell culture supernatant of PCH showed an increasing trend as HS time increased. Therefore, considering that mean concentration of POSTN in serum was higher than that of HSP90, XDH, and POSTN may be optimal biomarkers in serum for detecting HS level in chickens, and mainly secreted from hepatocytes. The former indicates that heat-stressed chickens are in a damaged state, and the latter implies that chickens can repair heat stress damage.

Key words: biomarker, serum, proteomics, heat stress, chicken

INTRODUCTION

Poultry are thermostatic animals with a thermal neutral zone (TNZ). Because of their hairy body surface and lack of sweat glands, poultry are very sensitive to high temperatures. When the ambient temperature exceeds the upper limit of the TNZ, poultry are exposed to heat stress (HS), which can seriously jeopardize poultry health, leading to a decrease in poultry production performance and even sudden death (Nawab et al., 2018; Saeed et al., 2019). With the intensification of global warming and the development of large-scale poultry farming, heat stress has caused enormous economic losses to the poultry industry. It has been estimated that heat stress causes up to $128 million in economic losses to the U.S. poultry industry each year (St-Pierre et al., 2003). Therefore, there is an urgent need to study the effects of heat stress on poultry to develop various effective mitigation strategies. The use of biomarkers that can indicate the state of heat stress in poultry has become an effective method that is being researched.

Currently, the most commonly used diagnostic method for heat stress in production is the temperature-humidity index (THI), but this method does not allow for real-time monitoring of heat stress and suffering animal status (Tao and Xin, 2003; Purswell et al., 2012; Kim et al., 2020). Available biomarkers from birds are expected to address this shortcoming. Many studies have reported biomarkers associated with heat stress in poultry. Studies have shown that when heat stress occurs in poultry, high levels of heat shock proteins (HSPs) can be detected in tissues such as the liver, heart and muscle, which are often referred to as cellular thermometers and can be used as biomarkers of heat stress in poultry (Craig and Gross, 1991; Xie et al., 2014; Al-Zghoul et al., 2015; Oladokun and Adewole, 2022). Heat stress signals can activate the avian HPA axis, which then secretes corticosterone in response to heat stress, and thus corticosterone levels are also a commonly used biomarker of heat stress in poultry (Mormède et al., 2007; Løtvedt et al., 2017; Oladokun and Adewole, 2022). Thyroid hormone is an important component of the poultry neuroendocrine system, which regulates body temperature and increases basal metabolic rate and metabolic adaptation during heat stress in poultry. Therefore, thyroid hormones are often used in the laboratory as biomarkers of heat stress in poultry (Bohler et al., 2021; Oladokun and Adewole, 2022). However, the above biomarkers are not specific to heat-stressed poultry, and other diseases can affect the expression of these biomarkers. In addition, most of the biomarkers require the animal to be dissected to detect and thus determine whether heat stress has occurred (Oladokun and Adewole, 2022). This is difficult to accomplish in practice, especially for determining the degree of heat stress in poultry that have not died. These limitations motivate us to continue to search for more specific and easily detectable biomarkers of heat stress in poultry.

With the rapid development of proteomics, MS-based proteomics technology has become the latest technology used to study differentially expressed proteins in the medical field (Geyer et al., 2016). Label-free quantitative proteomics (LFQP) technologies do not require any labeling. Proteolytic digestion was analyzed by mass spectrometry to generate data, and then the spectra were normalized by software. Then, the corresponding mass spectral peak intensities and peak areas were compared to determine the relative differences in protein expression in different sample groups. Label-free quantitative proteomics technology has good sensitivity for protein detection and is an effective method to obtain high-quality proteomic quantitative analysis with only a small amount of total protein extract (Sandin et al., 2014, 2015). Hence, LFQP is widely used in differentially expressed protein analysis studies.

In this study, we used LFQP to reveal different proteomes in the serum of control and heat-stressed chickens. Candidate biomarkers were identified by bioinformatics analysis and confirmed by enzyme-linked immunosorbent assay (ELISA). The biological source of the selected proteins was also preliminarily analyzed. This study will help to identify molecular or pathological processes associated with heat stress and discover novel biomarkers for evaluating the occurrence and level of heat stress in chickens, and even in other species, regardless of whether they are living or dead.

MATERIALS AND METHODS

Animal Treatment and Sample Collection

Forty 70-day-old Wenchang chickens weighing 700 to 750 g were purchased from Longquan Wenchang Chicken Company (Wenchang, China). The chickens were maintained under standard feeding conditions with a temperature of 22°C ± 2°C, relative humidity of 50 ± 10%, light period (12 h dark/12 h light), normal diet and water intake. After acclimatization for 1 wk, 34 of these chickens in good health and with similar body weights were selected for the formal trial. Among them, 10 chickens were used as the control group (Con) without heat stress treatment. The remaining 24 chickens were assigned to the heat stress group. The heat stress group was placed in an artificial climatic chamber (temperature at 42°C ± 1°C, relative humidity at 65%) for continuous 5 h in a single day, while the control group was placed in regular conditions. The 15 chickens that died during the heat stress were categorized as the heat stress death group (HSD), and the 9 chickens that survived at the end of the experiment were categorized as the heat stress survivor group (HSS). Blood was collected without anticoagulant from the wing vein for living animals and the jugular vein and/or carotid artery for chickens that had just died. Then, chickens were decapitated and dissected. The liver tissues were isolated and fixed in neutral formalin. The experimental protocol was conducted with the approval of the Animal Ethics Review Committee of Hainan University.

Culture of Primary Chicken Hepatocytes and Treatments

Primary chicken hepatocytes were isolated according to a modified protocol from Picardo (Picardo and Dickson, 1982). Briefly, livers from 12-day-old specific pathogen-free chicken embryos (Tanniu Wenchang Chicken Company, Wenchang, China) were cut into 1 mm3 pieces and rapidly digested twice at 37°C with 0.1 mg/mL type I collagenase. At the end of digestion, primary hepatocytes with different adhesion properties were obtained. The cells were cultured in Dulbecco's modified Eagle's medium containing 15% (v/v) fetal bovine serum and 1% (v/v) penicillin streptomycin in an incubator at 37°C with 5% CO2. When the cell fusion reached 90%, the primary hepatocytes were placed in a 5% CO2 incubator at 42°C with different heat stress times (0 h, 2 h, 4 h, 6 h). The culture medium supernatant was collected after the end of heat stress and used for ELISA analysis.

Submission for Detection of Serum Samples

The blood samples were centrifuged at 3,000 × g for 10 min at 4°C, and the obtained serum was stored at −80°C immediately. In the same group, 3 serum samples (each 100 μL) from 3 chickens were mixed into 1 tube. Thus, 3 sample tubes were submitted for the detection of label-free quantitative proteomics, which was conducted by Shanghai Biotree Biomedical Technology Co., Ltd.

Protein Extraction and Digestion

High-abundance protein and IgG were removed from serum using a de-high-abundance protein kit (Thermo Scientific, Rockford) to obtain a low-abundance protein solution. The low-abundance protein solution was pipetted into a centrifugal ultrafiltration tube and centrifuged at 12,000 × g for 10 min to obtain the protein concentrate. After determining the concentration of the protein concentrate using the BCA kit (Beyotime, Shanghai, China), the protein concentrate was placed in a centrifuge tube, dithiothreitol (DTT, 5 μL, 1 mol/L) solution was added, and the tube was shaken for 30 min at 50°C. At the end of the shaking process, the protein concentrate was incubated with iodoacetamide (IAA, 20 μL, 1 mol/L) for 30 min away from light and then digested with trypsin for 14 h at 37°C. Finally, the peptides were desalted on a Strata X C18 column and dried under vacuum at 4°C overnight.

Nano LC­MS/MS Analysis

After separation of 2 μg of peptides using a nano-UPLC liquid phase system EASYnLC1200 (Thermo Scientific), data acquisition was performed using a mass spectrometer (QExactive HFX, Thermo Scientific) equipped with a nanoelectrospray ion source. The peptides were chromatographically separated using a 100 μm ID × 15 cm reversed-phase column (Reprosil-Pur 120 C18AQ, 1.9 μm, Dr. Maisch). The mobile phases were an acetonitrile-water-formic acid system, in which 0.1% formic acid, 98% water, and 2% acetonitrile were used as mobile phase A and 0.1% formic acid, 20% water, and 80% acetonitrile were used as mobile phase B. The column was equilibrated at 100% phase A. An autosampler added the sample into the column for gradient separation at a flow rate of 300 nL/min with a gradient duration of 2 h. After equilibration at 100% phase B, the column was filled with the sample by an autosampler. The autosampler adds the sample to the column and performs another gradient separation at a flow rate of 300 nL/min and a gradient duration of 2 h.

The data-dependent acquisition (DDA) was analyzed for 2 h using the positive ion detection mode. A full scan was performed with a setting range of 350 to 1,600 m/z, a resolution of 120 k (@ 200 m/z, and an AGC of 3E6, with 50 ms as the maximum ion implantation time (max IT). The first 20 ions in the full scan were screened using a quadrupole (1.2 m/z for isolation window, 27% for NCE, 1E5 for AGC, and 110 ms for max IT) and then cleaved with HCD and subjected to fragmentation ion scanning (15k resolution). The dynamic exclusion time was set to 45 s according to the peak width; singly charged and >6-valent ions were not subjected to the secondary scan.

Proteome Discoverer Database Search

The raw MS files from the scans were processed and analyzed by Proteome Discoverer (PD) software (Version 2.4.0.305) with the Sequest HT search engine. The mass spectral list was searched using the UniProt FASTA database (UniProt-Gallus gallus_9031-2021-09.fasta) with variable modifications such as oxidation (M) and acetyl (protein N-term) and fixed modifications such as carbamidomethyl (C). Proteins were digested by trypsin, and up to 2 missed cuts were allowed. The false discovery rate (FDR) was set to 0.01 for both PSM and peptide levels. Peptide identifications had a bias of no more than 10 ppm, and fragment mass bias was no more than 0.02 Da. Proteins were quantified using unique peptides and razor peptides and normalized using total peptide amounts. All other parameters were left at their default values. The t test was used to calculate fold change (FC) and P values between different comparison groups. The screening criteria for significantly upregulated proteins were FC ≥ 1.2 and P < 0.05, and the screening criteria for significantly downregulated proteins were FC ≤ 0.83 and P < 0.05.

Bioinformatics Analysis

Protein subcellular localization was predicted using the online website wolfpsort (https://wolfpsort.hgc.jp/). Gene Ontology (GO) enrichment analysis of differentially expressed proteins, including biological process (BP), cellular component (CC), and molecular function (MF), was performed with the ggplot2 package of R (version 3.6.3) and ClusterProfiler package. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/pathway.html) was used for signaling pathway analysis of differentially expressed proteins. A heatmap and a volcano plot were generated by R language (the gplots and ggplot2 packages, respectively).

Enzyme-Linked Immunosorbent Assay

The serum concentrations of heat shock protein 90 alpha (HSP90), xanthine dehydrogenase (XDH), and periostin (POSTN) were measured by ELISA kits (Fine Biotech, Wuhan, China). ELISA was performed according to the manufacturer's instructions. First, 100 μL of the corresponding sample or standard was added to the microplate and incubated at 37°C for 90 min. After washing twice, 100 μL of biotin-labeled antibody working solution was added and incubated for 60 min at 37°C. Following washing, SABC working solution and TMB substrate solution were mixed successively into the wells. Finally, 50 μL of stop solution was added. The OD450 value was immediately read in a microplate reader (Allsheng., Hangzhou, China). The sample concentration was calculated from the OD value of the sample and the standard curve.

Diagnostic Receiver Operating Characteristic Analysis

Receiver operating characteristic (ROC) analysis of the concentration of each serum protein was performed using the pROC package, and the results were presented using the ggplot2 package for R (version 3.6.3). The range of ROC area under the curve (AUC) values should be 0.5 to 1, with the greater the AUC value, the higher the diagnostic value. When the AUC value is in the range of 0.5 to 0.7, the diagnostic value is low; when the AUC value is in the range of 0.7 to 0.9, the diagnostic value is high; and when the AUC value is greater than 0.9, the diagnostic value is extremely high.

Immunohistochemistry

The fixed liver tissues were embedded in paraffin and sectioned (4 μm). Sections were deparaffinized and rehydrated, followed by antigen retrieval. Blocking was conducted successively with 0.1% Triton X-100 and goat serum at room temperature. The slides were then incubated with primary antibodies against XDH (sc-398548; Santa Cruz Biotechnology, Dallas, TX), POSTN (sc-398631; Santa Cruz Biotechnology, Dallas, TX) and HSP90 (ab2928; Abcam, Cambridge, UK) at a dilution of 1:100 at 37°C for 1 h, respectively. After the slides were washed with phosphate-buffered saline containing Tween-20 (PBST), the corresponding HRP-labeled secondary antibody was added dropwise and incubated at room temperature away from light for 30 min. The slides were washed 3 times with PBST and then restained with hematoxylin. Finally, images were acquired under a light microscope (Guangzhou Mingmei Optoelectronic Technology Co., Guangzhou, China).

Statistical Analysis

Differentially expressed proteins were statistically analyzed using PD software (Version 2.4.0.305). SPSS 25.0 software was used to process the ELISA data to evaluate the content of related differentially expressed proteins in serum and medium supernatants. Data are presented as the mean ± SEM. The P value for statistical significance was <0.05 (*, P < 0.05; **, P < 0.01; ***, P < 0.001).

RESULTS

Identification of Differentially Expressed Proteins by LFQP

The workflow for the identification and quantification of serum proteins in the control and heat stress animals is summarized in Figure 1A. Principal component analysis (PCA) emphasized the reproducibility between the 3 replicates and clearly categorized the 3 treatment groups (Figure 1B). PCA1 and PCA2 accounted for 35.1 and 12.3% of the major components, respectively. It is suggested that heat stress treatment had a significant effect on the expression and secretion of serum proteins in chickens, which differed among the proteomes of the 3 treatment groups. Differentially expressed proteins (DEPs) were strictly screened based on FC ≥ 1.2 or FC ≤ 0.83 and P < 0.05. We identified 75, 20, and 42 upregulated proteins and 48, 33, and 70 downregulated proteins in HSD vs. Con, HSS vs. Con, and HSS vs. HSD, respectively (Figure 1C).

Figure 1.

Figure 1

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Global screening of serum proteins with the LFQP method. (A) The workflow of LFQP analysis applied in this study. (B) PCA categorizes samples into 3 distinct groups based on serum proteome profiles. (C) Quantification of the upregulated and downregulated proteins in the 3 pairwise comparisons of chicken serum proteins.

Subcellular Localization of Differentially Expressed Proteins

We predicted the subcellular localization of the differentially expressed proteins in heat stress groups compared with normal controls. As shown in Figure 2A and B, the differentially expressed proteins were mainly distributed in the cytoplasm (HSD: 51/123, 41.46%; HSS: 8/53, 15.09%), extracellular matrix (HSD: 29/123, 23.58%; HSS: 28/53, 52.83%), and nucleus (HSD: 24/123, 19.51%; HSS: 11/53, 20.75%).

Figure 2.

Figure 2

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Subcellular localization analysis of differentially expressed proteins. (A) The analysis results of HSD vs. Con. (B) The analysis results of HSS vs. Con.

Enrichment Analysis

To further understand the biological functions of DEPs, we performed Gene Ontology enrichment analysis and KEGG enrichment analysis. The GO analysis of HSD vs. Con showed that multicellular organismal process, cytoplasm, and catalytic activity accounted for the largest proportions of biological process, cellular component, and molecular function, respectively (Figure 3A). In the comparison of HSS and Con, response to stimulus for BP, extracellular region for the CC and protein binding for MF were the most significantly increased factors (Figure 3B). Using a standard of P < 0.05 and rich factor >0, the pathway analysis results demonstrated that the top 3 KEGG pathways with the largest rich factor in the HSD vs. Con were metabolic pathways, focal adhesion and carbon metabolism (Figure 3C). Only 4 KEGG pathways were enriched in HSS vs. Con, including focal adhesion, ECM-receptor interaction, carbon metabolism, and biosynthesis of amino acids (Figure 3D).

Figure 3.

Figure 3

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GO and KEGG enrichment analysis of differentially expressed proteins. (A) GO enrichment analysis of differentially expressed proteins in HSD vs. Con. The top 10 biological processes, cellular components and molecular functions are presented. (B) GO enrichment analysis of differentially expressed proteins in HSS vs. Con. The top 10 biological processes, cellular components and molecular functions are presented. (C) KEGG pathway analysis of differentially expressed proteins in the HSD vs. Con comparison. (D) KEGG pathway analysis of differentially expressed proteins in the HSS vs. Con comparison.

Analysis of Differentially Expressed Proteins in Biological Process

Based on the effects of heat stress on chickens, we paid more attention to changes in biological processes (Belhadj Slimen et al., 2016; Nawab et al., 2018). Therefore, we further analyzed the enriched BP terms. As shown in Figure 4A, 19 upregulated proteins and 19 downregulated proteins were successfully enriched in the top 10 BP terms of HSD vs. Con. In HSS vs. Con, 8 upregulated proteins and 13 downregulated proteins were successfully enriched in the top 10 BP terms (Figure 4B). Based on the results of the enrichment analysis, we were more concerned about the expression of upregulated proteins. XDH, PBLD, and CA9 were the top 3 most significantly upregulated proteins in HSD vs. Con, and POSTN, SPP2, and GAPDH were the top 3 most significantly upregulated proteins in HSS vs. Con. These upregulated proteins are more closely related to diagnostic indicators and have the potential to be biomarkers for the detection of heat stress. Here, we selected the most significantly upregulated proteins, XDH and POSTN, as candidate biomarkers for HSD vs. Con and HSS vs. Con, respectively.

Figure 4.

Figure 4

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Analysis of differentially expressed proteins in biological processes. (A) Differentially expressed proteins in the biological process of HSD vs. Con. (B) Differentially expressed proteins in the biological process of HSS vs. Con.

Differential Expression of Upregulated Proteins in Biological Process

A heatmap of upregulated proteins in biological processes is presented in Figure 5A and B. Cluster analysis of the abundance of proteins clearly showed that the abundance patterns of HSD and HSS differed from those of Con, and the protein expression in every group was clustered together. A volcano plot described the distribution of all the proteins based on the P values (Figure 5C and D). As shown in the figure, both XDH and POSTN are located in the upper right of the volcano plot axis, and these 2 proteins have good specificity [XDH, log2(fold change) = 8.45 and −log10(P value) =1.84; POSTN, log2(fold change) = 1.34 and −log10(P value) = 4.40]. Quantitative proteomics results showed significantly higher expression of XDH and POSTN in HSD and HSS, respectively, compared to that in Con (Figure 5E and F). Therefore, XDH and POSTN have the potential to serve as biomarkers of heat stress. In addition, based on literature reports, we also found that the classical heat stress biomarker HSP90 in tissue also appeared and was obviously increased in the serum (Figure 5G).

Figure 5.

Figure 5

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Expression of upregulated proteins in biological processes. (A) Heatmap of upregulated proteins in the biological process category of HSD vs. Con. (B) Heatmap of upregulated proteins in the biological process category of HSS vs. Con. (C) Volcano plot of DEPs between HSD and Con. The black arrow points to XDH. (D) Volcano plot of DEPs between HSS and Con. The black arrow points to POSTN. (E) LFQP quantitative results for XDH. (F) LFQP quantitative results for POSTN. (G) LFQP quantitative results for HSP90 (data are expressed as the mean ± SEM. *, P < 0.05; **, P < 0.01; ***, P < 0.001).

ELISA Validation of Protein Expression and ROC Analysis

We used ELISA to determine the selected protein levels in the serum of the heat-stressed and normal control groups. XDH levels were significantly higher in HSD than in Con and HSS, and XDH in HSS was increased but not significantly different from that in Con (Figure 6A). POSTN levels in HSS were significantly higher than those in Con and HSD, and POSTN was not significantly different between HSD and Con (Figure 6B). HSP90 levels in HSS were significantly higher than those in Con and HSD. After heat stress, HSP90 in the HSD group was significantly increased but was still remarkably lower than that in the HSS group (Figure 6C).

Figure 6.

Figure 6

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Validation of serum proteins as biomarkers for the diagnosis of heat stress. (A–C) Validation of 3 differentially expressed proteins in serum through ELISA. Data are expressed as the mean ± SEM. *, P < 0.05; **, P < 0.01; ***, P < 0.001. (D–F) AUC values for ROC curves were calculated from the serum concentrations of XDH, POSTN, and HSP90.

To understand if the levels of serum proteins could serve as diagnostic biomarkers, we evaluated the diagnostic efficacies of XDH, POSTN, and HSP90 in serum proteins. The discriminatory power of each putative biomarker was further evaluated using AUC analysis. In the ROC analysis (Figure 6D–F), the AUC values of XDH, POSTN, and HSP90 were 0.793, 0.752, and 0.779, respectively, indicating that XDH, POSTN, and HSP90 can provide good diagnostic value. The data showed that XDH had a higher AUC value than HSP90, and POSTN had the lowest AUC value of the 3 proteins. The mean concentrations of XDH, POSTN, and HSP90 were 135 ng/mL, 6,925 pg/mL, and 171 pg/mL, respectively.

Expression of XDH, POSTN, and HSP90 in Heat-Stressed Chicken Liver Tissue

The heart and liver are 2 especially important target organs to be attacked during heat stress in chickens, and their functional integrity is extremely critical for survival during heat stress (Tang et al., 2016, 2022a; Ma et al., 2022). A literature review revealed that the heart and liver of animals can express XDH, POSTN and HSP90 in response to changing conditions (Yu et al., 2008; Curek et al., 2010; Zhang et al., 2023). To determine whether heat stress affects the expression of XDH, POSTN, and HSP90 in chicken livers, we performed immunohistochemical assays on liver tissues from heat-stressed chickens. As shown in Figure 7A, the intensity of the XDH-positive signal in HSD and HSS was significantly higher than that in Con, and the positive signals were distributed in the cytoplasm. POSTN was distributed in the extracellular matrix, and the signal intensity of POSTN in HSD and HSS was significantly higher than that in Con. HSP90 was distributed in the cytoplasm and nucleus, and HSP90 signals were significantly stronger in HSD and HSS than in Con.

Figure 7.

Figure 7

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Detection of XDH, POSTN, and HSP90 in chicken liver tissues and the supernatant of primary chicken hepatocytes. (A) The positive signals of XDH, POSTN, and HSP90 in liver tissues from the control and heat-stressed chickens by immunohistochemical staining. (B–D) Analysis of 3 differentially expressed proteins in the supernatant of heat-stressed primary chicken hepatocytes. Data are expressed as the mean ± SEM. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Secretion of XDH, POSTN, and HSP90 From Heat-Stressed Primary Chicken Hepatocytes

In addition, we also established a primary chicken hepatocyte model and performed ELISA detection on the supernatant of cells subjected to heat stress for different times. The results showed that the levels of XDH, POSTN and HSP90 in the supernatant of heat-stressed hepatocytes showed a significant and gradually increasing trend with the duration of heat stress (Figure 7B–D).

DISCUSSION

In this study, we applied the LFQP technique to analyze and compare the proteomes of chickens in the control, heat stress death and heat stress survivor groups. A total of 917 proteins were identified, of which 123, 53, and 112 proteins were defined as differentially abundant proteins in HSD vs. Con, HSS vs. Con and HSS vs. HSD, respectively. Differently expressed proteins with a fold change of ≥1.2 or ≤0.83 and P < 0.05 were upregulated by 75, 20, and 42 proteins and downregulated by 48, 33, and 70 proteins for HSD vs. Con, HSS vs. Con, and HSS vs. HSD, respectively. Subcellular localization analysis showed that the differentially expressed proteins were mainly from the cytoplasm, extracellular matrix and nucleus. XDH, POSTN, and HSP90 were identified as potential biomarkers of heat stress by enrichment analysis. For this purpose, we performed ELISA analysis, which confirmed that XDH, POSTN, and HSP90 were present at higher levels in the serum of heat-stressed chickens compared with the control. ROC analysis showed that the AUC values of XDH, POSTN, and HSP90 were 0.793, 0.752, and 0.779, respectively, indicating that these proteins have good clinical diagnostic value. In addition, we examined the positive signals of XDH, POSTN, and HSP90 in paraffin sections of chicken liver tissues and their levels in the supernatants of primary chicken hepatocytes and found a significant elevation of XDH, POSTN, and HSP90.

Poultry have a TNZ, and the body does not require additional energy for thermoregulation when the ambient temperature is the same as the thermal neutral zone. If the ambient temperature exceeds the upper limit of the TNZ, the body temperature and metabolic responses of the poultry increase, which can induce heat stress in poultry (Saeed et al., 2019). Studies have shown that heat stress can accelerate the release of ions from ferritin, which in turn leads to the overproduction of transition metal ions (TMI). These overproduced TMIs can provide electrons to oxygen, leading to the production of superoxide anion (O2·−) and hydrogen peroxide (H2O2). Superoxide anion is highly reactive and is a precursor of most reactive oxygen species (ROS) and a mediator of oxidative chain reactions. Hydrogen peroxide can be further reduced to highly reactive hydroxyl radicals (OH·) via the Fenton reaction (Freeman et al., 1990; Powers et al., 1992; Liochev and Fridovich, 1999). These overproduced ROS can cause severe damage to hepatocytes by promoting lipid peroxidation, disrupting protein structure and inducing mitochondrial dysfunction. Subsequently, Kupffer cells and neutrophils are recruited in response to hepatocyte death, further leading to liver damage (Powers et al., 1992; Emami et al., 2020; Tang et al., 2022b). Therefore, liver damage is one of the most serious symptoms in heat-stressed poultry.

XDH, also known as xanthine oxidoreductase (XOR) and xanthine oxidase (XO), is a key enzyme in the catabolism and metabolism of purines. The XDH gene is expressed in most tissues and is regulated at the transcriptional level by a variety of factors, such as hormones, cytokines, inflammatory factors, and irritant stimuli (Harrison, 2002; Wang et al., 2016). Post-translationally, XDH can be converted to XO by irreversible protein hydrolysis. XO, the oxidase form of XDH, can utilize molecular oxygen as an electron acceptor, as it releases large quantities of O2·− and H2O2, which leads to oxidative stress (Saksela et al., 1999; Nishino et al., 2008; Bortolotti et al., 2021). This is analogous to heat stress-mediated oxidative stress, as both cause a free radical-mediated chain reaction by releasing large amounts of O2·− and H2O2, which induces oxidative cellular damage and activates apoptotic and necrotic pathways (Emami et al., 2020; Tang et al., 2022b). In our study, enrichment analysis showed that XDH is the most upregulated protein among the top 10 BP terms of HSD vs. Con. XDH is involved in 5 biological processes, including the multicellular organismal process, anatomical structure morphogenesis, reactive oxygen species metabolic process, positive regulation of reactive oxygen species metabolic process and regulation of reactive oxygen species metabolic process. These 5 biological processes are associated with ROS metabolism, suggesting that XDH may be a key contributor to mortality in heat-stressed chickens. High levels of XDH have been observed in diseases involving liver injury, such as hepatitis, cirrhosis, and nonalcoholic fatty liver disease (Battelli et al., 2001; Toledo-Ibelles et al., 2021). It has been reported that XDH in SCD knockout mice can be released from the liver into the circulation, and then XDH is converted to XO by plasma proteases and immobilized on the vascular endothelium (Houston et al., 1999; Aslan et al., 2001). These XOs are the main source of O2− and H2O2 in the vascular endothelium. Large amounts of XDH synthesis were observed in the liver of ischemia‒reperfusion rats, and these XDH were released into the bloodstream through the vascular system and then converted to XO (Curek et al., 2010; Unal et al., 2017). Due to the long circulating half-life of XO, XO-dependent ROS production causes severe damage to the vascular endothelium. These ROS also reach various parts of the body through the circulation, causing severe tissue damage (Yokoyama et al., 1990; Schmidt et al., 2019). In our study, serum XDH levels were significantly elevated in the heat stress death group compared to the control group. Immunohistochemistry showed that the level of XDH in the liver was significantly higher in the heat stress group than in the control group. In addition, gradually higher levels of XDH were detected in the supernatant of primary chicken hepatocytes after exposure to heat stress, which is consistent with the above results. We hypothesize that heat stress-induced death of chickens is caused by liver damage and the consequent release of XDH into the circulation. Therefore, we identified XDH as a biomarker of increased injury in heat-stressed chickens.

As already mentioned, liver damage is one of the most serious symptoms of heat-stressed poultry. As the main metabolic organ of the body, the liver is sensitive to various injuries and is involved in various injury syndromes (Malhi et al., 2010). However, due to its regenerative properties, the liver can be restored to its original size, thus ensuring survival. Liver regeneration usually undergoes 3 processes: initiation, proliferation and growth termination. The extracellular matrix (ECM) plays a key role in all 3 stages and is essential for normal and adaptive liver regeneration (Arteel and Naba, 2020). The ECM is a highly complex network of fibers composed of proteins, proteoglycans, glycosaminoglycans, and growth factors. Cells initiate pathways that regulate survival, proliferation, and differentiation through the interaction of molecules such as integrins with the ECM. ECM proteins can be functionally categorized into adhesive proteins and matricellular proteins (MCP). POSTN is a matricellular protein as well as an extracellular matrix protein belonging to the fasciclin family. POSTN can bind to cell surface receptors such as growth factors or cytokines and transduce cellular signals, playing an important role in the development and repair of tissues or organs such as the liver, heart, and lungs (Conway et al., 2014; Wang et al., 2022). It has been shown that serum POSTN levels are elevated in pathological remodeling after cardiac injury or hypertension, which accelerates myeloid cell production and promotes tissue implantation and differentiation into cardiac fibroblasts (Kühn et al., 2007). In addition, POSTN can bind to integrins αvβ3 and αvβ5 via the Akt/protein kinase B pathway, regulate cell adhesion and migration, and promote cell survival (Gillan et al., 2002; Shimazaki et al., 2008). In our study, POSTN was the most upregulated protein among the top 10 BP terms of HSS vs. Con. POSTN is involved in 4 biological processes, including response to stimulus, response to stress, cell adhesion and biological adhesion, suggesting that POSTN is critical for chicken survival during heat stress. In different types of patients, serum and tissue levels of POSTN are strongly correlated with organ functional decline and pathological stage. A study showed that the livers of partial hepatectomy (PHx) mice synthesize POSTN in large amounts, that POSTN overexpression promotes hepatocyte proliferation and that POSTN deficiency impairs angiogenesis during liver regeneration (Wu et al., 2018). Zhang et al. performed immunohistochemical and immunofluorescence staining in mice fed ethanol and found that POSTN was significantly increased in the liver and that upregulation of POSTN activates autophagy, thereby attenuating alcohol-related liver disease (ALD) (Zhang et al., 2023). In addition, POSTN interacts with αvβ1, αvβ3, or αvβ5 integrins on the membranes of myocytes and vascular endothelial cells to activate the PI3K-Akt pathway after myocardial infarction (MI), which promotes cardiomyocyte regeneration and angiogenesis (Lindner et al., 2005; Kühn et al., 2007; Shimazaki et al., 2008; Chen et al., 2017). These studies show that the expression of POSTN is important for cell survival, especially in the liver and heart. In our study, POSTN levels were significantly elevated in the serum and liver of heat-stressed chickens compared to those in the control. POSTN in the supernatant of primary chicken hepatocytes also increased significantly during the process of heat stress, which is consistent with the above results. Therefore, we identified POSTN as a biomarker of injury remission and even death resistance in heat-stressed chickens.

HSPs are crucial for cell recovery after the damage caused by heat stress. HSPs are expressed under normal conditions, and when environmental stimuli (e.g., heat, transport, and intimidation) lead to tissue damage, HSPs are produced in large quantities in response to the damage. Thus, heat shock protein expression has been used as an indicator of environmental stress in the body (Shehata et al., 2020; Hu et al., 2022). The upregulation and accumulation of HSPs improves the repair and replacement of damaged cells under stress conditions. Studies have shown that HSP90, 70, 60 and 40 are rapidly synthesized in the liver, heart, muscle and brain of chickens following exposure to heat stress (Xie et al., 2014; Al-Zghoul et al., 2015; Kang and Shim, 2021). Yao and Xu et al. found that the stable production of HSP90 and HSP70 contributed to the recovery of chicken cardiomyocytes from heat stress (Xu et al., 2017, 2019; Yao et al., 2023). These HSPs can be released into the blood or culture medium by a selective release mechanism (Hightower and Guidon, 1989; Hunter-Lavin et al., 2004). In our study, the LFQP results showed that HSP90 was significantly elevated in the HSD group, and there was a nonsignificant increase in the HSS group compared to the Con group. ELISA results showed that the HSP90 levels in both the HSD and HSS groups were increased remarkably and that those in the HSS group were the highest and significantly higher than those in the Con and HSD groups. Comparison of the ELISA results between Con and HSD was consistent with that from LFQP, while a difference was observed in HSS. In proteomics, proteins are digested as peptides by enzymes and then the molecular weights are analyzed by mass spectrometry to determine the kind of protein. The ELISA method uses the antigen-antibody reaction to directly detect the content of the target protein. In our opinion, it is the uncertainty of protein digestion in proteomics that negatively influences the quantification of HSP90. Of course, further study is needed to determine the real reason for the difference between the results obtained by proteomics and ELISA and the applicability of HSP90 as a clinical testing biomarker. In addition, we examined the levels of HSP90 in the livers of heat-stressed chickens and in the supernatants of primary chicken hepatocytes and found that the levels of HSP90 were also significantly elevated, thus illustrating that hepatocytes should be a source of serum HSP90.

In this study, we paid more attention to the changes in serum proteins in the heat stress death group and heat stress survivor group. We screened XDH and POSTN based on enrichment analysis. XDH and POSTN were the most significant proteins in the heat stress death group and heat stress survivor group, respectively. XDH had a better diagnostic value than the classical heat stress biomarker HSP90 (XDH, AUC = 0.793, mean concentration: 135 ng/mL; HSP90, AUC = 0.779, mean concentration: 171 pg/mL). Although the AUC value of POSTN was smaller than that of HSP90, the mean concentration of POSTN in serum was higher than the mean concentration of HSP90 (POSTN, AUC = 0.752, mean concentration: 6,925 pg/mL; HSP90, AUC = 0.779, mean concentration: 171 pg/mL). Therefore, POSTN is more likely to be detected clinically than HSP90. As previously described, XDH can induce cell death by promoting oxidative stress, and POSTN can promote cell survival by activating the relevant mechanisms. The serum ELISA results for XDH and POSTN were consistent with the results of the LFQP analysis. Immunohistochemistry showed that XDH and POSTN were significantly elevated in the livers of heat-stressed chickens. The ELISA detection data from the supernatant of primary chicken hepatocytes showed a gradually increasing tendency with the extension of heat stress time. Therefore, we believe that XDH and POSTN can serve as potential biomarkers for detecting heat stress conditions in chickens. XDH may be used to indicate that heat-stressed chickens are in an injury exacerbation phase, and POSTN may be used to indicate that heat-stressed chickens are in an injury remission or death resistance phase.

CONCLUSIONS

In this study, we successfully screened serum proteins XDH, POSTN and HSP90 associated with heat stress in chickens by LFQP and ELISA analysis, determined that XDH and POSTN could be potential biomarkers for detecting the degree of heat stress in live chickens, and found that hepatocytes should be a primary source of serum XDH, POSTN and HSP90 in chickens. This study provides not only information on specific and convenient biomarkers for heat stress in poultry but also novel clues for further researching the injury mechanism of heat stress. Of course, a single or few specific biomarkers might not fit all, more research is needed for developing useful biomarkers that permit quick and accurate analysis and prevent the adverse effects on animal welfare, such as the noninvasive biomarkers from feces or feathers of birds.

ACKNOWLEDGMENTS

This research was funded by the National Natural Science Foundation Regional Fund Project of China (grant number 31802157), Youth Fund Project of Hainan Natural Science Foundation (grant number 322QN246), Collaborative Innovation Center Project in 2022 of Hainan University (grant number XTCX2022NYC05), and the Initial Scientific Research Foundation in Hainan University (grant number KYQD(ZR)-22006).

DISCLOSURES

The authors declare no conflicts of interest.

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