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. 2024 Jan 11;103(3):103410. doi: 10.1016/j.psj.2023.103410

Transcriptomic analysis of the hypoxia-inducible factor 1α impact on the gene expression profile of chicken fibroblasts under hypoxia

Aikebaier Reheman *,1, Qijun Wu †,1, Jianing Xu *, Jiang He *, Meng Qi *, Kai Li *, Gang Cao †, Xinwei Feng *,2
PMCID: PMC10840346  PMID: 38277890

Abstract

Hypoxia-inducible factor 1 (HIF-1) is a transcriptional regulator that mediates cellular adaptive responses to hypoxia. Hypoxia-inducible factor 1α (HIF-1α) is involved in the development of ascites syndrome (AS) in broiler chickens. Therefore, studying the effect of HIF-1α on the cellular transcriptome under hypoxic conditions will help to better understand the mechanism of HIF-1α in the development of AS in broilers. In this study, we analyzed the gene expression profile of the chicken fibroblast cell line (DF-1) under hypoxic conditions by RNA-seq. Additionally, we constructed the HIF-1α knockdown DF-1 cell line by using the RNAi method and analyzed the gene expression profile under hypoxic conditions. The results showed that exposure to hypoxia for 48 h had a significant impact on the expression of genes in the DF-1 cell line, which related to cell proliferation, stress response, and apoptosis. In addition, after HIF-1α knockdown more differential expression genes appeared than in wild-type cells, and the expression of most hypoxia-related genes was either down-regulated or remained unchanged. Pathway analysis results showed that differentially expressed genes were mainly enriched in pathways related to cell proliferation, apoptosis, and oxidative phosphorylation. Our study obtained transcriptomic data from chicken fibroblasts at different hypoxic times and identified the potential regulatory network associated with HIF-1α. This data provides valuable support for understanding the transcriptional regulatory mechanism of HIF-1α in the development of AS in broilers.

Key words: Hypoxia, HIF-1α, knockdown, chicken fibroblast, transcriptome

INTRODUCTION

Broiler ascites syndrome (AS), also referred to as broiler pulmonary hypertension syndrome (PHS), is a prevalent nutritional and metabolic disease observed in fast-growing commercial broilers (Kalmar et al., 2013). Many studies have reported that various factors such as nutrition, management, environment, and genetics are related to the occurrence of AS (Julian, 2000; Olkowski, 2007; Baghbanzadeh and Decuypere, 2008). The above factors lead to a series of pathophysiological changes by cause hypoxia in chickens, including high pulmonary arterial blood pressure, pulmonary artery remodeling, right ventricular hypertrophy and failure, free radical production, and increased lipid peroxidation (Wideman et al., 2013). Studies have shown that low-temperature, high-energy, high-protein, and high-sodium diets promote the metabolic rate and tissue oxygen consumption of broilers, and finally cause relative hypoxia in broilers (Julian, 1993). Broiler hypoxia can cause tissue oxygen-free radical damage, increased cardiac output, insufficient pulmonary blood volume, right heart hypertrophy, pulmonary artery hypertension (PAH), and other symptoms. Recent studies have demonstrated that the progression of PAH is influenced by pulmonary vascular remodeling, which involves various vasoactive molecules derived from endothelial cells. These molecules include endothelin-1 (EDN1), insulin-like growth factor-II (IGF-II), vascular endothelial growth factor (VEGF), transforming growth factor-beta (TGF-β), platelet-derived growth factor receptor (PDGFR), erythropoietin (EPO), and hypoxia-inducible factor-lα (HIF-1α) (Dahal et al., 2011; Papamatheakis et al., 2013), which have been identified as crucial regulators and potential targets for the treatment of PAH.

Hypoxia-inducible factor 1 (HIF-1) plays a crucial role in responding to both cellular and systemic hypoxia (Wang and Semenza, 1993). HIF-1 consists of O2-regulated HIF-1α and HIF-1β subunits. Under hypoxic conditions, HIF-1α promotes the transcription of genes such as VEGF, EPO, and glycolytic enzymes, thereby adapting to hypoxic conditions (Semenza, 2000; Semenza, 2001a). HIF-1α plays an important role in the pathogenesis of PAH (Semenza, 2001b). In animals, HIF-1α was associated with the development of PAH in rats (Jiang et al., 2007; Li and Dai, 2004), and may be related to the occurrence of AS in broilers (Zhang et al., 2013a). However, there are few research reports on the mechanism of HIF-1α in the occurrence of AS in broilers.

Here, we cultured chicken fibroblast cell line (DF-1) under hypoxia and analyzed gene expression profiles by using RNA-seq. Further, we constructed HIF-1α knockdown DF-1 cell line and analyzed the gene expression profiles of wild-type cells and HIF-1α knockdown cell line in hypoxic culture, as well as the changes in key signaling pathways. We aimed to analyze the transcriptional regulatory effect of HIF-1α on other genes under hypoxic conditions and screen the key genes regulated by HIF-1α.

MATERIALS AND METHODS

Cell Culture

DF-1 cell line were maintained in DMEM medium supplemented with 15% FBS, and 1% penicillin and streptomycin were cultured at 37℃ with 5% CO2. In this assay, we simulated cellular hypoxia by treating cells with CoCl2; the DF-1 cell line were treated with 100 μM CoCl2 (Sigma-Aldrich, C8661-100G) for 4, 24, 48 h to induce hypoxia (Figure 1A) (Lopez-Sanchez et al., 2014).

Figure 1.

Figure 1

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Hypoxic culture of DF-1 cell line for different time points. (A) Schematic of experimental design pattern. (B) Effect of hypoxia on DF-1 cell morphology. (C) Effect of hypoxia on mitochondrial membrane potential. (D) Quantitative analysis of mitochondrial membrane potential. (E-I) Quantify gene expression level by qPCR. Relative expression levels for various genes were obtained by the 2−^^CT method with β - actin as a reference gene. The experiment was performed in 3 biological replicates. All qPCR data were presented as mean ± SEM. The probability of significant differences was analyzed using Student's t test. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001 compared with the control.

Measurement of Mitochondrial Membrane Potential

To detect whether cells are hypoxic, mitochondrial membrane potential (MMP) was measured using a JC-1 mitochondrial membrane potential assay kit. Briefly, after treating the DF-1 cell line with CoCl2 at the indicated time, the culture medium should be discarded and the cells should be washed twice with 1 ml of PBS. Then, 1 mL of JC-1 solution should be added, mixed thoroughly, and incubated at 37℃ for 20 min. After incubation, the supernatant was removed, washed with JC-1 staining buffer twice, and 2 ml DMEM was added. The MMP was detected by using fluorescence microscope (Olympus, IX73, Shanghai, China) and enzyme-labeled instrument. In normal cells with high mitochondrial membrane potential, a large amount of JC-1 in the mitochondrial matrix produces red fluorescence. In contrast, in apoptotic cells, the reduced MMP promotes the transfer of JC-1 to the cytoplasm, where it appears green fluorescence. Therefore, changes in MMP can be judged by changes in the fluorescence of JC-1.

Construction of HIF-1α Knockdown Cell Line

Three shRNAs (shHIF-1α-1, shHIF-1α-2, and shHIF-1α-3) were designed to target HIF-1α by using the BLOCK-iT RNAi Designer (Invitrogen, Waltham, MA), the shRNA sequence was listed in the Table 1. The shHIF-1α was inserted into the lentiviral vector pLKO-eGFP-puro to construct the PLKO- shHIF-1α plasmid, and PLKO-NC -puro was used as a negative control. To generate lentiviruses expressing shHIF-1α, HEK293T cells were seeded into 6-well plates and co-transfected with 1 mg of pMD.2G, 2 mg of pSPAX, and 2 mg of pLKO-shTRAF3 per well, pLKO-NC-puro was used as a negative control. At 72 h post-transfection, virus-containing cell culture supernatants were collected and filtered through a 0.45 mm filter. Incubate overnight with virus concentrate, and centrifuge at 5,000 g for 30 min to pellet the virus. Resuspend the virus pellet, mix it with polystyrene, and incubate it with epithelial cells. After incubating for 24 h, the medium was replaced with a medium containing 10% FBS and 4 mg/mL puromycin (Selleck, S7417, Houston, TX) to purify cell line. The fluorescence microscopy (Olympus, IX73) was used to observe purification rates of knockdown cell line, and the HIF-1α knockdown efficiency was performed by qPCR.

Table 1.

Nucleotide sequences used in this article.

Gene Accession no. Product length Sequence (5′-3′)
CX3CL1 AM398231 113 F: CAGATCCCGTTTGCACTGAG
R: TGATCTATGGCACGGTGGAA
IL8L2 NM_205498 125 F: ACGCTGGTAAAGATGGGGAA
R: TCAACATTCTTGCAGTGGGG
EDN1 XM_040664613 142 F: TTCCCTATGGTCTTGGAGGC
R: TTTCCCGTCTGGCAGAAGTT
VEGFA NM_205042 110 F: AGTCTACGAACGCAGCTTCT
R: TCATCAGAGGCACACAGGAT
NOS2 NM_204961 128 F: AACAGCCAGCTCATCCGATA
R: TCAAAGCGGCCATATTTCGG
HIF1A NM_204297 140 F: CTTCGTAGCAATGCCGATCC
R: TGGCTGGTACTTGCATCAGA
beta-actin L08165 116 F: CGGACTGTTACCAACACCCA
R: ATCCTGAGTCAAGCGCCAAA
shHIF1A-1 F: CCGGGCTCTTGATGGATTTGTTATGCGAA
CATAACAAATCCATCAAGAGCTTTTTTG
R: AATTCAAAAAAGCTCTTGATGGATTTGTTA
TGTTCGCATAACAAATCCATCAAGAGC
shHIF1A-2 F: CCGGGGGACTCACTCAGTTCGATTTCGAA
AAATCGAACTGAGTGAGTCCCTTTTTTG
R: AATTCAAAAAAGGGACTCACTCAGTTCGA
TTTTTCGAAATCGAACTGAGTGAGTCCC
shHIF1A-3 F: CCGGGCAGCTTCTTTCTCAGAATGACGAA
TCATTCTGAGAAAGAAGCTGCTTTTTTG
R: AATTCAAAAAAGCAGCTTCTTTCTCAGAA
TGATTCGTCATTCTGAGAAAGAAGCTGC
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qPCR Assay

Total RNA was extracted from tissue and cells using RNAiso Plus reagent (Cat. No. 9109, Takara, Japan). Then, 500 ng of RNA was reverse transcribed into cDNA using ReverTra Ace qPCR RT Master Mix and gDNA Removal Kit (Vazyme Biotech Co. Ltd, Nanjing, China) according to the manufacturer's instructions. The PCR reaction mix contains 10 μL SYBR Select Master Mix (TOYOBO, Japan), 2 μL cDNA, 0.8 μL forward primer, 0.8 μL reverse primer, and 6.4 μL ddH2O2. The PCR reactions were performed using the QuanStudio 6 real-time PCR system, and relative expression was calculated using the 2−ΔΔCt method (Livak and Schmittgen, 2001). β-actin was used as a control. The experiment was performed in 3 biological replicates. Primers are listed in the Table 1.

RNA-Seq Library Preparation and Sequencing

Total RNA was isolated from cells, and RNA-seq libraries were prepared using the VAHTS Stranded mRNA-Seq Library Prep Kit (Vazyme, Catalog code: NR602-02) according to the manufacturer's instructions. Briefly, mRNA was isolated from 1 µg of total RNA using mRNA Capture Beads and incubated at 94°C for 8 min to obtain mRNAs between 150 bp and 200 bp in length. Reverse transcription of mRNA to cDNA and conversion to double-stranded cDNA molecules. After end repair and tailing, paired-end sequencing adapters are ligated to the ends of cDNA fragments, followed by library amplification and purification. Purified libraries were verified and quantified using an Agilent 2,100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) and sequenced using the illumina platform.

NGS Data Process and Visualization

The RNA-seq data was processed using Pertea et al.'s method to obtain a reading count for each sample (Pertea et al., 2016), and then read count lists were merged into a matrix by an R package dpylr (https://CRAN.R-project.org/package=dplyr). The read count matrix was applied to DESeq2 (Love et al., 2014) for the differential analysis, genes with an absolute fold change greater than 2 and a p-value less than 0.05 were considered as significantly up/down-regulated genes, additionally, the normalized count was exported, and the heatmap was generated with the normalized counts by using the OmicShare tools at www.omicshare.com/tools. Gene ontology (GO) enrichment analysis of differentially expressed genes was implemented at the (http://www.geneontology.org) (Ashburner et al., 2000). Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource for understanding high-level functions and utilities of biological systems (Kanehisa and Goto, 2000; Kanehisa, 2019; Kanehisa et al., 2023). KOBAS software (KOBAS, Surrey, UK) was used to test for statistically significant enrichment of differentially expressed genes in KEGG pathways.

Statistical Analysis

All data were presented as mean ± SEM. Numerical data from 3 independent experiments were analyzed using GraphPad Prism 8.0 software (La Jolla, CA). The probability of significant differences was analyzed using Student's t test. P < 0.05 was considered significant.

RESULTS

Construction of Hypoxic Culture Model of DF-1 Cell Line

To study the effect of hypoxia on gene expression profiles of DF-1 cell line, we constructed a hypoxic culture model for DF-1 cell line (Figure 1A). There are obvious differences in the morphology of the DF-1 cell line cultured under normoxic and hypoxic conditions. Under an optical microscope, the cells in the normoxia group grew in a long spindle shape and radially. The cells in the hypoxia group were larger in size, some cells changed from spindle shape to round or triangular shape, and their growth rate was affected, more significantly after 48 h of hypoxia (Figure 1B). The MMP (ΔΨm) change in DF-1 cell line was detected by using JC-1 staining. The results showed that the hypoxia induced the JC-1 fluorescent color change from red to green, suggesting that CoCl2 induced the decrease in ΔΨm, and the green light signal increased gradually while the red-light signal diminishes (Figures 1C and 1D). After 4 h of hypoxia, the expression of HIF-1α exhibited a decreasing trend, however this change was non-significant, which subsequently increased after 24 h. Notably, the expression at 48 h showed a significant increase compared to the control group (Figure 1E). After hypoxia, the CX3CL1 showed a similar expression trend to the HIF-1α, but the expression of the CX3CL1 increased significantly after 24 h, and the difference was significant compared with the control group, and there was no significant difference between 24 h and 48 h (Figure 1F). The expression patterns of VEGFα, CCR2, and NOS2 were similar to HIF-1α, and their expression levels significantly increased compared to the control group after 48 h (Figures 1G–1I). The above data show that the hypoxic culture model we constructed is successful.

Analysis of Gene Expression Profiling of DF-1 Cell Line Under Different Hypoxia

To further understand the effect of hypoxic culture on the transcriptome of chicken fibroblasts, we analyzed the gene expression profile of the DF-1 cell line at different hypoxic times by using RNA-Seq. The data showed that 4 h under hypoxic conditions differentially regulated 236 gene expression level compared with the control group (0 h), of which 144 were up-regulated and 92 were down-regulated (Figure 2A). There were 89 differential expression genes in 24 h compared with 4 h under hypoxia; among them, 56 increased and 33 decreased (Figure 2B). We found that the compared with 24 h, there were 2,851 differentially expressed genes after continuing the hypoxia for 48 h, of which 1,491 were increased and 1,360 were decreased (Figure 2C). Wayne's results showed that the expression levels of 7 genes changed significantly during all hypoxia time points, including WDR1, WISP2, WDR17, CFAP20, WDR13L, KRT7, and DIXDC1 (Figure 2D). During 3 times of hypoxia, the expression of gene WDR17 continued to increase, while the expression of WDR1 and KRT17 were down-regulated (Figure 2E). The Figure 2F presents the top 50 differentially expressed genes after 48 h of hypoxia, the results indicate a significant change in the expression levels of these genes compared to 24 h of hypoxia. To comprehend the potential function of differentially expressed genes regulated by hypoxia, we conducted GO and KEGG pathway analysis. The GO analysis showed that differentially expressed genes were mainly enriched in signaling pathways related to cellular response to stress, DNA metabolic process, cellular response to DNA, damage stimulus, DNA repair, and response to stress. (Figure 2G). KEGG enrichment analysis showed that differentially expressed genes were mainly enriched in the apoptosis pathway and DNA replication pathway (Figure 2H). Pathway enrichment analysis results indicate that hypoxia influences the expression of genes associated with cell proliferation and apoptosis. Additionally, cells exhibit differential expression of stress response-related genes to adapt to the hypoxic environment.

Figure 2.

Figure 2

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Detection and analysis of DEGs in the wild-type DF-1 cell line after hypoxia treatment at different times. (A) Volcano map of DEGs after 4 h of hypoxia. (B) Volcano map of DEGs after 24 h of hypoxia. (C) Volcano map of DEGs after 48 h of hypoxia. The X-axis is log2 FC; the Y-axis is -log10 (adjusted P-value). Significantly upregulated genes are shown in red, downregulated genes in blue, and nondifferentially expressed genes in gray. (D) Overlap DEGs of different hypoxic times. (E) Gene expression levels (Log2 FC) of overlap 7 DEGs assessed by RNA-Seq. (F) The heat map represents the top 50 differential DEGs after 48 h of hypoxia, the 24 h-1, 24 h-2, 48 h-1, and 48 h represents of RNA-seq replicates at 24 h and 48 h of hypoxia. (G) GO pathway enrichment analysis of DEGs after 48 h hypoxic. (H) KEGG pathway enrichment analysis of DEGs after 48 h under hypoxic conditions.

Analysis of Gene Expression Profiling of HIF-1α Knockdown DF-1 Cell Line Under Normoxia Condition

To analyze the role of the HIF-1α in the DF-1 cells under normoxic and hypoxic conditions, we constructed HIF-1α knockdown DF-1 cell line (Figure 3A). Firstly, we constructed HIF-1α DF-1 knockdown cell line by using the RNAi method. The purification rate of the knockdown cell line was observed using a fluorescence microscope (Olympus, IX73), the gene knockdown cells will emit a green, fluorescent signal, while wild-type cells will not emit a green fluorescent signal (Figure 3B). The results from qPCR analysis revealed that among the 3 types of shHIF1α, shHIF1α-1 exhibited the highest knockdown efficiency, reaching approximately 80%, which utilized the shHIF1α-1 DF-1 cell line for conducting subsequent experiments (Figure 3C). Subsequently, we analyzed the transcriptome level of the HIF-1α knockdown DF-1 cell line under physiological conditions by using RNA-seq. The volcano diagram showed that compared with the WT cell line, 150 differentially expressed genes appeared in the HIF-1α DF-1 knockdown cell line, of which 79 genes were up-regulated and 71 genes were down-regulated under normoxia (Figure 3D). To analyze the gene information regulated by HIF-1α under normoxia, we displayed the top 50 differentially expressed genes (Figure 3E). GO analysis showed that differentially expressed genes were mainly enriched in the negative regulation of cell population proliferation, which including ITGA1, FGFRL1, IL8L2, P3H2, NFIB, and PDCD10 (Figure 3F). Earlier studies reported that HIF-1 regulate cell proliferation (Hubbi and Semenza, 2015), and our results are consistent with this. The expression of IL8L2 was verified by using qPCR, and the results indicated a significant increase in the expression of IL8L2 after HIF-1α knockdown, which was consistent with the RNA-seq results (Figure 3G). Then, we predicted the PPI interaction network by string (Szklarczyk et al., 2021), and the results showed that there are interactions between gene HIF-1α and MIF, PPARP, VEGFα, MMP2, EDN1, EGFR, PPARG, and NOS2 (Figure 3H). Interestingly, the expression levels of MIF and MMP2 decreased significantly after HIF-1α knockdown (Figure 3I). These results suggested that the HIF-1α may negatively regulate the expression of MIF and MMP2 under normoxia, and MIF and MMP2 also exist in the top 50 differentially expressed genes. We further analyze the mRNA expression of NOS2, VEGFα, EDN1, and CX3CL1 by qPCR, which is related to hypoxia. The qPCR results indicated that there were no significant changes in the expression levels of NOS2, VEGFα, EDN1, and CX3CL1 under normoxia after HIF-1α knockdown (Figure 3J). This finding aligns with the RNA-seq data, suggesting that the expression of NOS2, VEGFα, EDN1, and CX3CL1 may be specifically regulated by HIF-1α under only hypoxic conditions.

Figure 3.

Figure 3

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Detection and analysis of DEGs in the HIF-1α knockdown DF-1 cell line under normoxia. (A) Schematic diagram of gene knockdown cell line construction. (B) 4 mg/ml puromycin were utilized for the purification of the knockdown cell line, knockdown cells will emit a green, fluorescent signal, while wild-type cells will not emit a green, fluorescent signal. (C) Detect gene knockdown efficiency by qPCR. (D) Volcano map of DEGs after HIF-1α knockdown of normoxia. (E) The heat map represents the top 50 differential DEGs, the NC-1, NC-2, shHIF1a-1, and shHIF1a-2 represents of RNA-seq replicates of normoxia. (F) GO pathway enrichment analysis of DEGs under normoxia. (G) RNA-Seq and qPCR were used to determine the expression levels of candidate genes identified by GO analysis. (H) Predicted protein-protein interaction network. (I) The expression levels of candidate genes were determined by RNA-Seq. (J) The expression level of candidate genes was verified by qPCR. Relative expression levels for various genes were obtained by the 2−^^CT method with β - actin as a reference gene. The experiment was performed in 3 biological replicates. All qPCR data were presented as mean ± SEM. The probability of significant differences was analyzed using Student's t test. **P ≤ 0.01 compared with the control.

Analysis of Gene Expression Pattern of HIF-1α Knockdown DF-1 Cell Line Under Hypoxia Condition

To study the effect of HIF-1α on the transcriptome of chicken fibroblast cells under hypoxic conditions, we analyzed the transcriptome changes of HIF-1α knockdown cell line by using RNA-seq at different hypoxic times. The results showed that the 396 differentially expressed genes appeared in the HIF-1α knockdown DF-1 cell line compared with wild-type DF-1 cells after 4 h of hypoxia, in which 297 genes were up-regulated and 99 genes were down-regulated (Figure 4A). After 24 h of hypoxia, HIF-1α knockdown DF-1 cell line also showed a small number of differentially expressed genes compared with wild type DF-1 cells, of which 51 genes were up-regulated and 68 genes were down-regulated (Figure 4B). Compared with wild type DF-1 cells, the number of differential genes in HIF-1α knockdown DF-1 cell line increased significantly after 48 h of hypoxia, of which 1,879 genes were up-regulated and 2461 genes were down-regulated (Figure 4C). We performed Wayne analysis on the differentially expressed genes under different hypoxic times, and the results showed that the expression of 17 genes changed during the 3 hypoxic times (Figure 4D). The heatmap displays the changes in expression of the above 17 genes. The results show that after HIF-1α knockdown, the expression of most of the above 17 genes is down-regulated under hypoxic conditions, especially after 48 h of hypoxia (Figure 4E). The interaction between the above 17 genes and HIF-1α was predicted by string. The results showed that there may be interactions between HIF-1α and EGLN3, SLC16A3, and KLF4, and the expression of the EGLN3, SLC16A3, and KLF4 was significantly decreased under hypoxic conditions after HIF-1α knockdown (Figures 4E and 4F). This result suggests that the expression of the EGLN3, SLC16A3, and KLF4 may be regulated by HIF-1α under hypoxia. We further conducted GO and KEGG analysis on the differential genes after 48 h of hypoxia. The analysis of GO revealed that the differentially expressed genes were predominantly enriched in pathways associated with ribosome, biosynthesis of amino acids, oxidative phosphorylation, mitophagy, and cellular senescence signaling pathway (Figure 4G). KEGG enrichment analysis showed that the differentially expressed genes were mainly enriched ribosome, oxidative phosphorylation, Apoptosis, Cellular senescence, and mitophagy (Figure 4H). The results of the pathway enrichment analysis indicated that HIF-1α plays a role in regulating the expression of genes associated with the mentioned pathways, and the regulation allows cells to adapt to a hypoxic environment. Finally, the expression levels of hypoxia-related genes NOS2, EDN1, VEGFα, and CX3CL1 were detected by qPCR (Figure 4I). The results showed that After HIF-1α knockdown, the expression levels of NOS2 and VEGFα were significantly down-regulated under hypoxic conditions, whereas the expression levels of EDN1 and CX3CL1 remained unchanged. The expression of NOS2, EDN1, VEGFα, and CX3CL1 in wild-type cells was up-regulated, but after HIF-1α knockdown, their expression levels were down-regulated or unchanged under hypoxic conditions. This result shows that the expression of NOS2, EDN1, VEGFα, and CX3CL1 may also be regulated by HIF-1α under hypoxic conditions.

Figure 4.

Figure 4

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Detection and analysis of DEGs in the HIF-1α knockdown DF-1 cell line after hypoxia treatment at different times. (A) Volcano map of DEGs in the HIF-1α knockdown DF-1 cell line after 4 h of hypoxia. (B) Volcano map of DEGs in the HIF-1α knockdown DF-1 cell line after 24 h of hypoxia. (C) Volcano map of DEGs in the HIF-1α knockdown DF-1 cell line after 48 h of hypoxia. The X-axis is log2 FC; the Y-axis is -log10 (adjusted P-value). Significantly upregulated genes are shown in red, downregulated genes in blue, and non-differentially expressed genes in gray. (D) Overlap DEGs of different hypoxic times. (E) The heat map represents gene expression levels of overlap 17 DEGs, the NC-1, NC-2, shHIF1a-1, and shHIF1a-2 represents of RNA-seq replicates after 4 h, 24 h, and 48 h of hypoxia (F) Predicted protein-protein interaction network. (G) GO pathway enrichment analysis of DEGs in the HIF-1α knockdown DF-1 cell line after 48 h under hypoxic conditions. (H) KEGG pathway enrichment analysis of DEGs in the HIF-1α knockdown DF-1 cell line after 48 h under hypoxic conditions. (I) The expression level of candidate genes was verified by qPCR. Relative expression levels for various genes were obtained by the 2−^^CT method with β - actin as a reference gene. The experiment was performed in 3 biological replicates. All qPCR data were presented as mean ± SEM. The probability of significant differences was analyzed using Student's t test. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 compared with the control.

DISCUSSION

Broiler AS is a metabolic disease caused by hypoxia during the growth of broiler chickens. Hypoxia transcriptionally induces many genes controlled by HIF, as well as many other transcription factors, including nuclear factor kappa B (Taylor et al., 2016). However, the vast majority of oxygen-sensitive genes are actually direct targets of HIF (Semenza, 2012). These genes help adapt to decreasing oxygen levels at the cellular and organismal levels. Hypoxia-inducible factor 1 is a ubiquitously expressed heterodimeric transcription factor that mediates cellular adaptive responses to hypoxia (Zhang et al., 2013b). Pulmonary artery remodeling is a key step in the development of AS, and the development of hypoxic pulmonary hypertension is primarily attributed to pulmonary vasoconstriction and remodeling (Fuchs et al., 2010). This involves a significant increase in fibrous tissue growth within the adventitia of the pulmonary artery, with pulmonary artery fibroblasts playing a crucial role (Frid et al., 2006; Luo et al., 2015). Therefore, exploring the hypoxic adaptation and molecular mechanisms of lung fibroblasts is of certain significance in revealing pulmonary artery remodeling during AS. In this study, we used RNA-Seq to analyze the effect of hypoxia on the transcriptome of the DF-1 cell line.

Our results show that HIF-1α, NOS2, CCR2, VEGFα, and CX3CL1 are highly expressed in wild-type DF-1 cells after 48 h of hypoxia. HIF-1α is a transcriptional activator that is regulated by oxygen levels. HIF-1α is degraded quickly in cells when there is sufficient oxygen, but its expression is elevated in hypoxic conditions (Lequeux et al., 2021). Many studies have shown that HIF-1α, VEGFα, CX3CL1, CCR2, and NOS2 are related to cellular responses to hypoxia, and hypoxia can induce the expression of these genes (Xi et al., 2002; Zhang et al., 2012; Florentin et al., 2022; Fang et al., 2023). Transcriptome data showed that a large number of differentially expressed genes appeared after 48 h of hypoxia. As a transcriptional regulator, HIF-1α binds to the promoter regions of numerous genes during continuous hypoxia, resulting in alterations in gene expression levels (Lee et al., 2020). Therefore, we speculate that continuous hypoxia causes the up-regulated expression of HIF-1α, and HIF-1α regulates the expression levels of downstream genes. In addition, the expression of some genes may be directly regulated by the hypoxic environment. Wayne analysis showed that 7 genes were differentially expressed during the 3 hypoxia times, such as WDR1, WISP2, WDR17, CFAP20, WDR13L, KRT7, and DIXDC1. WDR17 showed an up-regulation trend during the 3 hypoxia times, while WDR1 and KRT7 showed a down-regulation trend. WDR17 is a protein that encodes protein 17 with WD repeat sequences. It is highly expressed in the retina and testis, it is a potential candidate gene for retinal diseases (Stohr et al., 2002). However, there is currently a lack of literature reports on the association between WDR17 and hypoxia. In this study, we observed a significant up-regulation trend of WDR17 in both short-term and long-term hypoxia. This finding suggests that WDR17 may be more sensitive to hypoxic conditions. WDR1 is a central regulator of actin turnover during the formation of the B-cell and T-cell immunologic synapses (Pfajfer et al., 2018a). KRT7 is a member of the keratin gene family, KRT7 is abnormally expressed in various cancers and promotes malignant tumor progression (Pfajfer et al., 2018b). It is suggested that genes related to immunity and tumor microenvironment may respond to hypoxic environments more quickly.

Go and KEGG enrichment analysis of differentially expressed genes after 48 h of hypoxia. We found that differentially expressed genes were mainly enriched in pathways related to stress response, DNA, and apoptosis biological processes. This result is consistent with earlier research reports, which previously reported that hypoxia causes genome instability and mutation frequency by affecting DNA repair pathways, and induces apoptosis in human pluripotent stem cells (Isaja et al., 2020; Kaplan and Glazer, 2020). To better analyze the transcriptional regulatory role of HIF-1α during hypoxia, we constructed an HIF-1α knockdown cell line. Transcriptome analysis revealed that the expression of a few genes was altered in the knockdown cell line compared to the wild-type cells under normoxic conditions, and the differentially expressed genes were enriched in negative regulation of cell population proliferation. HIF-1α affects cell proliferation through multiple pathways under hypoxic conditions, thereby enabling cells to better adapt to the hypoxic environment (Hubbi and Semenza, 2015). Interestingly, the expression of NOS2, EDN1, VEGFα, and CX3CL1 did not change significantly after HIF-1α knockdown. VEGFα is closely related to angiogenesis and the pathology of respiratory injury, and its expression is regulated by the transcription factor HIF-1α under hypoxic conditions (Voelkel et al., 2006). HIF-1α regulates NOS2 expression by specifically binding to the promoter region of NOS2 under hypoxia, but this regulatory effect disappears under normoxia (Palmer et al., 1998). Studies have shown that hypoxia can induce the expression of EDN1 and CX3CL1, and HIF-1α is involved in the transcriptional regulation of EDN1 and CX3CL1 (Minchenko et al., 2019; Singh et al., 2019). The results suggested that the expression levels of the mentioned genes may remain relatively stable under normoxia. Additionally, HIF-1α may not be able to exert its usual transcriptional regulatory role under normoxia.

The gene expression profile of the HIF-1α knockdown cell line was further analyzed after hypoxia. The results revealed that after 48 h of hypoxia, the HIF-1α knockdown DF-1 cell line exhibited a significant number of differentially expressed genes compared to wild-type cells. However, a limited number of differentially expressed genes were observed at 24 h and 4 h of hypoxia. This finding is in line with the previous result, where a significant number of genes were affected after 48 h of hypoxia. However, the only distinction is that a greater number of differentially expressed genes were observed after HIF-1α knockdown. The results revealed that upon HIF-1α knockdown, the expression of 17 genes displayed a consistent downward trend at various hypoxic time points (4 h, 24 h, 48 h), which of EGLN3, KLF4, and SLC16A3 interacted with HIF-1α. Research shows that HIF-1α induced the expression of EGLN3 under hypoxic conditions (Pescador et al., 2005). HIF-1α upregulates KLF4 to promote migration of human vascular smooth muscle cells under hypoxic conditions (Shan et al., 2020). SLC16A3 plays a critical role in hypoxic cancer cell growth and proliferation, and hypoxia increases SLC16A3 transcription through the NF-κB signaling pathway (Choi et al., 2019). The above research reports are basically consistent with the results of this study, knockdown of HIF-1α affects the expression of EGLN3, KLF4, and SLC16A3 under hypoxia. Pathway analysis results showed that differentially expressed genes were mainly enriched in ribosomes, biosynthesis of amino acids, oxidative phosphorylation, mitophagy, cellular senescence, and apoptosis pathways. This result indicates that HIF-1α may be involved in biological processes related to cell life cycle activities such as cell proliferation, apoptosis, and oxidative phosphorylation under hypoxic conditions.

CONCLUSIONS

Collectively, we analyzed the gene expression profiles of wild-type DF-1 cell line and HIF-1α knockdown DF-1 cell line under hypoxic conditions. Our data indicate that there are certain differences in the gene expression profiles of the 2 cell lines under hypoxic conditions. This result further proves that HIF-1α plays an important transcriptional regulatory role under hypoxic conditions and provide valuable data for future investigations into the underlying mechanism of HIF-1α action in hypoxic conditions.

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China [grant number 32060785]; College student innovation and entrepreneurship training program project [grant number 202301].

Availability of Data and Materials: The data sets used in this study are available in the following databases: Gene Expression Omnibus GSE244260.

DISCLOSURES

The authors declare no conflicts of interest.

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