RNA-Seq reveals transcriptome changes to heat stress in the breast muscle of adult female chickens

doi:10.21203/rs.3.rs-4368514/v1

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Research Article

RNA-Seq reveals transcriptome changes to heat stress in the breast muscle of adult female chickens

https://doi.org/10.21203/rs.3.rs-4368514/v1

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Background

Heat stress has caused significant impacts on the poultry industry globally. Tianjin-monkey Chicken (TM) is a local naked neck chicken genetic resource in China, characterized by its heat stress resistance due to a low feather coverage.

Results

We conducted heat stress stimulation tests on TM and a normal feathered chicken (Jingfen No. 6 Layer, JF), and the breast muscle tissues were collected for transcriptome sequencing. A total of 157 differentially expressed genes (DEGs) and 1435 DEGs were respectively obtained from the comparisons of JFN-vs-JFT and TMN-vs-TMT. GO enrichment analysis found that biological process (BP) terms including phospholipid homeostasis, regulation of aggrephagy, positive regulation of aggrephagy, and negative regulation of lipase activity may be closely related to heat stress resistance in JF chickens. While catabolism-related BP terms were mainly enriched for DEGs of TM, such as catabolic process, protein catabolic process and cellular catabolic process. KEGG pathway analysis showed that the MAPK signaling pathway was enriched both in TM and JF with high connectivity. In addition, some pathways with higher connectivity (Metabolic pathways, FoxO signaling pathway, TGF-beta signaling pathway and AMPK signaling pathway) may be closely associated with resistance to heat stress in JF. In Tianjin-monkey Chicken, we also identified several pathways potentially involved in heat stress regulation, including Ubiquitin mediated proteolysis, Autophagy-animal and Regulation of actin cytoskeleton. Protein-Protein Interaction Networks (PPI) for the 24 co-differentially expressed genes revealed four key genes (Klf9, Asb2, Tmem164 and Arrdc2) associated with heat stress both in JF and TM.

Conclusions

Our findings will enrich the research on heat stress resistance in chicken skeletal muscle, while also providing a theoretical basis for the genetic improvement of heat stress resistance in chickens.

chicken

heat stress

breast muscle

RNA-seq

Chicken meat is widely enjoyed by consumers around the world. Chicken breast meat, as a key component of the skeletal muscle system, constitutes a significant portion of the total meat yield in chickens [1, 2]. It is also highly valued for its nutritional attributes, being a rich source of high protein content, low-fat levels, essential vitamins and minerals, lower caloric content, and versatility in cooking [3]. Recently, as a result of global warming, the high ambient temperature especially during the summer season often proves disastrous for poultry farming and heat stress (HS) induced by extremely high temperatures compromises the yield and quality of poultry products and endangers the sustainability of the poultry industry [4].

Chicken is particularly vulnerable to heat stress due to their high metabolic rate and lack of sweat glands [5, 6]. Research has shown that acute heat stress led to a decrease in chicken muscle pH and denaturation of muscle proteins, potentially resulting in tougher meat texture [7]. It also influenced postmortem carbohydrate metabolism, potentially affecting glycogen and lactate metabolism, thus contributing to changes in meat quality. Zhang et al. [8] revealed alterations in the metabolic profiles of chicken breast muscle under extreme heat stress conditions, indicating changes in energy metabolism and biochemical pathways. Liang et al. [9] compared acute heat tolerance and meat quality among three different chicken breeds. They found that acute heat stress had discernible impacts on meat quality parameters such as pH, color, water-holding capacity, and tenderness in the different chicken breeds, suggesting that breed differences may influence how chickens respond to acute heat stress and the subsequent meat quality outcomes.

The naked neck chicken is a distinctive poultry breed characterized by the absence of feathers on its neck. The unique trait allows naked neck chickens to exhibit greater heat tolerance compared to other breeds, because the absence of feathers on the neck allows these chickens to increase heat dissipation, alleviating adverse heat effects, especially on productive performance [10]. Melesse [11] found that heat stress-induced reduction in feed consumption was generally higher in commercial hens than in naked neck and F₁ crosses. The average decline in feed consumption was highest in commercial hens NH (22.7%) followed by LW (18.3%) whereas the corresponding figure for Na, F1 crosses NaxNH and NaxLW was 17.8, 16.9 and 12.1%, respectively. Njenga [12] compared the growth of naked neck, frizzle, dwarf, and normal feathered birds from 1 day old to week 5 in Kenya, and found significantly higher (P < 0.05) body weights in naked neck birds than all the other chicken genotypes. Adomako et al. [13] observed that the body weight and weight gain of naked neck birds were significantly higher (P < 0.05) as compared to their normal feathered counterparts in tropical villages.

Tianjin-monkey Chicken is a local naked neck chicken genetic resource in China, characterized by its neck being either completely or partially featherless. We conducted heat stress stimulation tests on Tianjin-monkey Chicken (TM) and a normal feathered chicken (Jingfen No. 6 Layer, JF), then collected breast muscle tissue for transcriptome sequencing, aiming to identify key genes involved in the heat stress response of breast muscle. The study would provide a theoretical basis for the genetic improvement of heat stress resistance in chickens.

Overview of sequencing data

We obtained a total of 844, 819, 320 raw reads, from which 842,223, 614 clean reads were got after filtering, accounting for 99.69% (Table S1). The proportion of clean reads in each sample was not less than 99.65% (JFT_1). The GC content of clean reads for each sample ranged from 51.18–53.31% (Table S1). More than 81.97% (TMT_2) clean reads removed rRNA were mapped to the reference genome (Table S2). The mapped clean reads were mainly located in exon, followed by intron and finally intergenic regions (Figure S1). All the above results collectively indicate that the data quality obtained from this sequencing is high.

Identification of differentially expressed genes

A total of 157 differentially expressed genes (DEGs) and 1435 DEGs were obtained with FDR < 0.05 and FC ≥ 2 in the comparisons of JFN-vs-JFT and TMN-vs-TMT (Fig. 1A). Compared with group JFN, 41 genes were up-regulated and 116 genes were down-regulated in the heat stress group JFT. For the Tianjin-monkey Chicken, 40 up-regulated genes and 1,395 down-regulated genes were found in the heat stress group TMT. And 24 co-differentially expressed genes were found between the two comparison groups (Fig. 1B). The heatmap illustrates the upregulation and downregulation relationship of differential genes across different groups, with heat stress resulting in an increase in down-regulated genes in the breast muscles (Fig. 1C and D).

GO enrichment analysis for DEGs

Gene Ontology (GO) is an international standardized gene functional classification system that offers a dynamic-updated controlled vocabulary and a strictly defined concept to comprehensively describe properties of genes and their products in any organism. GO has three ontologies: molecular function (MF), cellular component (CC) and biological process (BP). We conducted GO enrichment analysis for the DEGs and the top20 GO terms were shown in Fig. 2. In comparison group JFN-vs-JFT, the most significantly enriched GO term was phospholipid homeostasis (Fig. 2A). We also found that regulation of aggrephagy, positive regulation of aggrephagy, and negative regulation of lipase activity entries in the top20 may be closely related to heat stress resistance in JF chickens. Additionally, multiple BP terms related to skeletal muscle were also enriched in the top20 terms, including skeletal muscle cell differentiation, skeletal muscle tissue development, skeletal myofibril assembly and skeletal muscle organ development. In the top20 terms of TMN-vs-TMT, catabolism-related BP terms were mainly enriched such as catabolic process, protein catabolic process and cellular catabolic process (Fig. 2B). Heat stress may lead to changes in cellular metabolic activities thereby affecting these biological processes related to degradation metabolism.

KEGG pathway enrichment analysis for DEGs

Genes usually interact with each other to play roles in certain biological functions. Pathway-based analysis helps to further understand their biological functions. Pathway enrichment analysis identified significantly enriched metabolic pathways or signal transduction pathways in DEGs comparing with the whole genome background. The top20 KEGG pathways were shown in Fig. 3A and 3C. Afterward, we constructed interaction networks among all enriched pathways to assist in identifying the core pathway (Fig. 3B, D), and the degree represents connectivity, with higher connectivity indicated by a deeper shade of red. We found that the MAPK signaling pathway had the highest connectivity in the top20 KEGG pathways of group JFN-vs-JFT (Fig. 3A, B). In addition, pathways with higher connectivity such as Metabolic pathways, FoxO signaling pathway, TGF-beta signaling pathway, and AMPK signaling pathway may be closely associated with resistance to heat stress. In the comparison group TMN-vs-TMT, we also identified MAPK signaling pathway with high connectivity in the top20 pathways (Fig. 3C, D). Additionally, some pathways (Ubiquitin mediated proteolysis, Autophagy-animal and Regulation of actin cytoskeleton), which exhibit high connectivity, may also be involved in the regulation of heat stress resistance.

We further predicted the protein-protein interaction (PPI) relationships for the 24 co-differentially expressed genes. The results (Fig. 4) revealed that only 13 genes exhibited interactions, including three transcription factors KLF9, NFIL3 and NSD2. KLF9 and ASB2 have the highest node degree, followed by TMEM164 and ARRDC2.

Heat stress is one of the most important environmental stressors for the poultry industry in the world. The effects of heat stress on meat quality have been extensively studied and they are multifaceted and involve changes in pH, color, drip loss, cooking loss, shear force, and other parameters [7, 14, 15]. At the molecular level, some genes related to heat stress were also identified. Hsp70 has been found to effectively improve the meat quality of transported broilers by improving the energy status, inhibiting glycolytic influx, and restoring redox homeostasis [16]. Yao et al. [17] found that heat stress caused mitochondrial injury in the chicken primary myocardial cells, leading to oxidative stress and calcium overload. However, Hsp90 activation was found to prevent these effects, thus protecting the mitochondria and reducing cellular damage. Another research found that the expression of Hsp90b1 mRNA significantly increased during heat stress and decreased during recovery, with higher expression observed in Huainan chickens [18].

Currently, research on heat stress-related genes in chickens remains limited, primarily focusing on genes encoding heat shock proteins (HSPs) proteins [19]. This study subjected chickens of breeds JF and TM to acute heat stress stimulation, followed by the collection of breast muscle samples for transcriptome sequencing, aiming to identify heat stress-resistant genes and their functional pathways in the breast muscles.

GO results showed that the most significantly enriched GO term of DEGs in JFN-vs-JFT was phospholipid homeostasis. When cells were exposed to heat stress, the composition and organization of phospholipids in the plasma membrane can be disrupted, affecting the overall integrity and function of the membrane. This can lead to changes in the fluidity, permeability, and signaling properties of the membrane, impacting the cell's ability to maintain homeostasis [20, 21]. Aggrephagy was a selective autophagy process that targets the removal of protein aggregates, which were often formed under conditions of cellular stress, such as heat stress [22]. Additionally, some GO terms related to skeletal muscle development were also enriched in the JFN-vs-JFT, indicating that heat stress indeed affects skeletal muscle development. In the top20 terms of TMN-vs-TMT, catabolism-related BP terms were mainly enriched. Heat stress can accelerate cellular metabolism, leading to increased catabolic processes to generate the energy needed for cellular repair, adaptation, and survival under elevated temperatures. Therefore, catabolism is closely related to the cellular response to heat stress, as it provides the necessary energy for cells to maintain homeostasis and function effectively in challenging thermal environments [23, 24].

MAPK signaling pathway with high connectivity in the top20 pathways was found both in JFN-vs-JFT and TMN-vs-TMT. The MAPK (Mitogen-Activated Protein Kinase) signaling pathway plays a crucial role in the response of cells to external stimuli, including heat stress [25]. Research has shown that heat stress can activate the MAPK pathway, leading to the phosphorylation and activation of MAPK proteins such as Hog1 in yeast or ERK, JNK, and p38 in mammalian cells. This activation triggers a series of downstream events that help cells cope with the stress induced by high temperatures [25]. The study found that Hsp70 modulates stress-activated signaling and acts protectively via the MAPK pathway. Inhibition of Hsp70 by quercetin exacerbated intestinal injury and affected the activation of MAPKs and caspase-3 [26].

Additionally, KEGG pathways (Metabolic pathways, FoxO signaling pathway, TGF-beta signaling pathway, AMPK signaling pathway) with higher connectivity in group JFN-vs-JFT may all be associated with heat stress resistance. When organisms are exposed to high temperatures, their metabolic processes undergo significant alterations to cope with the stress and these changes often involve shifts in energy production, allocation of resources, and synthesis of protective molecules [27, 28]. The FoxO signaling pathway would be activated in response to heat stress and a study showed that heat stress treatment led to a significant increase in FoxO3a phosphorylation (specifically at Ser253) in rat skeletal muscle of heat-stressed legs after 24 hours [29]. The TGF-beta signaling pathway is implicated in the regulation of cortisol synthesis and secretion, neuroactive ligand-receptor interactions, and the JAK/STAT signaling pathway in response to temperature changes [30]. The AMPK signaling pathway plays a crucial role in regulating cellular energy homeostasis and lipid metabolism [31]. Research has shown that heat stress (HS) inhibits the AMPK signaling pathway, leading to the promotion of lipid accumulation in 3T3-L1 preadipocytes [32].

In comparison group TMN-vs-TMT, we also have identified high connectivity pathways Ubiquitin mediated proteolysis, Autophagy-animal and Regulation of actin cytoskeleton. Ubiquitin-mediated proteolysis plays a significant role in cellular stress responses, including heat stress. When cells are exposed to heat stress, proteins like HuR can be targeted for degradation through ubiquitin-mediated proteolysis [33]. Autophagy is a cellular process that involves the degradation and recycling of cellular components, and it has been linked to increased tolerance to a range of abiotic stressors, including heat stress, in various ectothermic organisms. Flies exposed to heat stress exhibit protein aggregation, triggering an autophagy-related response, while rapamycin feeding induces local autophagy in the gut and enhances heat-stress tolerance [34]. sHsps (Small heat shock proteins) belong to the HSPs family, with a molecular weight of 15–40 kDa. They are known to interact with actin and play a significant role in protecting the actin cytoskeleton from disruption induced by stressful conditions. Some sHsps act as capping proteins, inhibiting actin polymerization, while others protect the actin cytoskeleton by coating microfilaments with small oligomers of phosphorylated sHsps [35].

The GO and KEGG pathways discussed above, along with their enriched genes, may be involved in skeletal muscle heat stress resistance. Most terms and their genes were enriched independently in either group JFN-vs-JFT or TMN-vs-TMT, with only one pathway, the MAPK signaling pathway, enriched universally. However, analysis revealed that the enriched DEGs within it were distinct. Therefore, we further analyzed the 24 co-differentially expressed genes and constructed a PPI network with them. The results revealed only 13 genes exhibiting interaction, with KLF9 and ASB2 having the highest degree, followed closely by TMEM164 and ARRDC2.

Krüppel-like factor 9 (Klf9) is a transcription factor that plays a crucial role in regulating various environmental and stress response pathways. It plays a critical role in mediating the cellular responses to stress, environmental cues, and developmental processes through its interactions with various signaling pathways [36]. Study showed that Klf9 demonstrates bimodal, reciprocal activity by activating the Fgfr1 promoter in proliferating myoblasts and repressing the same promoter via the same DNA-binding site in differentiated myotubes [37]. Asb2 encodes a protein engaged in cellular processes via its ankyrin repeat and SOCS box domains, regulating signaling pathways and protein degradation. Li et al. [38] found that the expression of the Asb2 gene in the breast muscle of Silkie fowl exposed to heat stress significantly increased by 1.74 times, and it might participate in the regulation of heat stress through the ubiquitin-proteasome pathways. The gene Tmem164, also known as Transmembrane Protein 164, has been identified as a new determinant of autophagy-dependent ferroptosis, a type of iron-dependent regulated cell death [39]. The resistance to heat stress is closely related to autophagy, and we have discussed this in the preceding text. Arrdc2 is a protein-coding gene that belongs to the arrestin family of proteins. Studies have found that stimuli suppressing growth such as testosterone depletion or acute aerobic exercise would increase Arrdc3 expression in skeletal muscle [40]. Another study found that Arrdc2 overexpression was sufficient to lower myotube diameter in C2C12 cells in part by altering the transcriptome favoring muscle atrophy [41].

In this study, we collected breast muscle samples from Tianjin-monkey Chickens (TM) and Jingfen No. 6 Layers (JF) stimulated by heat stress for transcriptome sequencing. We identified multiple GO terms and KEGG pathways associated with heat stress in TM and JF chickens respectively. We also found several important skeletal muscle heat stress-resistant genes both in TM and JF chickens including Klf9, Asb2, Tmem164 and Arrdc2. These results would play a guiding role in the research on skeletal muscle function and also would lay a theoretical foundation for clarifying the regulatory mechanism of skeletal muscle growth and development in chicken. The above results will establish the theoretical foundation for uncovering the mechanism of heat stress resistance in chicken skeletal muscle.

Animals

In this study, 10 healthy Tianjin-monkey Chickens (TM) and 10 normal feathered chickens (Jingfen No. 6 Layer, JF) at the age of 600 days were selected. Subsequently, 10 individuals of each breed were randomly divided into two groups, with each group consisting of 5 individuals, and subjected to normal temperature rearing (20°C for 3h) and heat stress stimulation (42°C for 3h) respectively. The four experimental groups were Tianjin-monkey Chicken (TMN) and Jingfen No. 6 Layer (JFN) groups raised at normal temperature (20°C), Tianjin-monkey Chicken (TMT) and Jingfen No. 6 Layer (JFT) treated with high temperature (42°C). Finally, the chickens were rapidly euthanized by severing the carotid artery, and breast muscles were collected and preserved in liquid nitrogen.

RNA Extraction, library construction and sequencing

Total RNA was extracted using Trizol reagent kit (Invitrogen, CA, USA) according to the manufacturer’s protocol. RNA quality was checked using RNase-free agarose gel and assessed on Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA), Nanodrop 2000 (Eppendorf AG, Hamburg, Germany) and Qubit2.0 Fluorometer (Thermo Fisher Scientific Inc., MA, USA). After total RNA was extracted, mRNA was enriched by Oligo (dT) beads. Then the enriched mRNA was fragmented into short fragments using fragmentation buffer and reversely transcribed into cDNA by using NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs, MA, USA). The purified double-stranded cDNA fragments were end-repaired, and A base was added and ligated to Illumina sequencing adapters. The ligation reaction was purified with the AMPure XP Beads (1.0X). And polymerase chain reaction (PCR) amplified. The resulting cDNA libraries were sequenced using Illumina Novaseq6000 by Gene Denovo Biotechnology Co. (Guangzhou, China).

Bioinformatics analysis

Raw reads were obtained from the sequencing machines and further filtered by fastp [42] (version 0.18.0) to get high-quality clean reads. Bowtie2 [43] (version 2.2.8) was used for mapping clean reads to the ribosome RNA (rRNA) database, and the rRNA mapped reads then will be removed. The remaining clean reads were mapped to the reference genome (bGalGal1.mat.broiler.GRCg7b) using HISAT2. 2.4 [44]. The mapped reads of each sample were assembled by using StringTie v1.3.1 [45, 46]. For each transcription region, a TPM (Transcripts Per Kilobase of exon model per Million mapped reads) value was calculated to quantify its expression abundance and variations, using RSEM [47] software.

RNA differential expression analysis was performed by DESeq2 [48] software between two different groups. The differentially expressed genes (DEGs) were identified with the parameter of false discovery rate (FDR) < 0.05 and fold change (FC) ≥ 2. Finally, the Gene Ontology (GO) and KEGG Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was implemented based on the GO (https://geneontology.org/) and KEGG (https://www.kegg.jp/) databases respectively.

Protein-Protein Interaction Networks (PPI) are of significant importance for revealing protein functionality. The PPI network of target genes would help to understand the interactions between genes, their average abundance, and connectivity. This study constructed the PPI network of target genes based on the STRING database (http://string-db.org).

Heat stress (HS); Differentially expressed genes (DEGs); Gene Ontology (GO); Kyoto Encyclopedia of Genes and Genomes (KEGG); Molecular function (MF); Cellular component (CC); Biological process (BP); Tianjin-monkey Chicken (TM); Jingfen No. 6 Layer (JF); Tianjin-monkey Chicken raised at normal temperature (TMN); Jingfen No. 6 Layer raised at normal temperature (JFN); Tianjin-monkey Chicken treated with high temperature (TMT); Jingfen No. 6 Layer treated with high temperature (JFT); False discovery rate (FDR); Fold change (FC); Protein-Protein Interaction Networks (PPI).

Ethics approval and consent to participate

The animal experiments performed in the study were all evaluated and approved by the Animal Ethics Committee of the Tianjin Academy of Agriculture Sciences. We confirm that all methods were performed in accordance with the relevant guidelines and regulations. We also confirm that the study is reported in accordance with ARRIVE guidelines.

Consent for publication

Not applicable

Availability of data and materials

Sequence data that support the findings of this study have been deposited in the National Center for Biotechnology Information (NCBI) with the primary accession code PRJNA1103706.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The study was jointly supported by the Seed Industry Innovation Project of the Tianjin Academy of Agricultural Sciences (2023ZYCX011).

Authors' contributions

PFW performed the data analysis and drafted the manuscript. SLX, HTY and XHZ contributed to tissue sampling and laboratory experiments. GXZ and KW revised the manuscript. SLX and PFW designed the study and also revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We are grateful to Guangzhou Gene Denovo Biotechnology Co., Ltd for assisting in sequencing and bioinformatics analysis.

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No competing interests reported.

FigureS1.tif
Figure S1 Mapped region statistics of exon, intron and intergenic.
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RNA-Seq reveals transcriptome changes to heat stress in the breast muscle of adult female chickens

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Version 1

Abstract

Background

Results

Conclusions

Figures

Background

Results

Overview of sequencing data

Identification of differentially expressed genes

GO enrichment analysis for DEGs

KEGG pathway enrichment analysis for DEGs

Discussion

Conclusions

Materials and methods

Animals

RNA Extraction, library construction and sequencing

Bioinformatics analysis

Abbreviations

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References

Additional Declarations

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Version 1