Molecular regulation of differential lipid molecule accumulation in intramuscular fat and abdominal fat of chicken

doi:10.21203/rs.3.rs-2348705/v1

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Molecular regulation of differential lipid molecule accumulation in intramuscular fat and abdominal fat of chicken

https://doi.org/10.21203/rs.3.rs-2348705/v1

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Background

Reducing abdominal fat (AF) accumulation and increasing the level of intramuscular fat (IMF) simultaneously is a major breeding goal in poultry industry.

Results

To explore the different molecular mechanisms between AF and IMF, gene expression profiles in the breast muscle (BM) and AF among three chicken breeds were analyzed. A total of 4737 shared DEGs were identified between BM and AF, of which 2602 DEGs were upregulated and 2135 DEGs were downregulated in the BM groups compared with that in AF groups. DEGs involved in glycerophospholipid metabolism and glycerolipid metabolism were potential regulators resulting in the difference of lipid metabolite accumulation in IMF and AF. PPAR signaling pathway was the most important pathway involved in tissue-specific lipid deposition. Correlation analysis showed that most representative DEGs enriched in PPAR signaling pathway, such as FABP5, PPARG, ACOX1, GK2, were negatively correlated with PUFA-enriched glycerophospholipid molecules. Most DEGs related to glycerophospholipid metabolism, such as GPD2, GPD1, PEMT, CRLS1 and GBGT1, were positively correlated with glycerophospholipid molecules, especially DHA- and arachidonic acid (ARA)- containing glycerophospholipid molecules.

Conclusion

This study elucidated the molecular mechanism of tissue-specific lipid deposition and poultry meat quality.

Chicken

Abdominal fat

Intramuscular fat

Integration analysis

glycerophospholipid

Lipids are one of the major components of chicken body. The intensive selection for a rapid growth rate leads to excessive abdominal fat (AF) accumulation [1], which exerts a negative impact on consumer acceptance and health [2]. However, intramuscular fat (IMF) is the major substrate to produce meat flavor; and the presence of intramuscular fat improves meat tenderness and juiciness [3–5]. To meet consumers’ needs, reducing AF accumulation and increasing the levels of IMF simultaneously have become a major breeding goal in poultry industry.

A previous study verified that a desirable broiler line with higher IMF but lower AF could be obtained by genetic selection [6]. AF-derived preadipocytes have higher adipogenic differentiation ability than IMF-derived preadipocytes [7]. Large transcriptomic differences were identified between IMF- and AF-derived preadipocyte differentiation [7]. These results indicated that the deposition of AF and IMF are subject to different regulatory mechanisms in chicken. Although transcriptomics has been applied to reveal the molecular mechanisms of AF deposition or IMF deposition between different chicken breeds [8, 9], it is more effective to find a balanced selection between AF and IMF rather than selection for higher IMF alone or lower AF alone. Therefore, a systematic and deep research is needed to reveal the different molecular mechanisms between AF and IMF deposition.

Adipose tissues from different locations displayed unique biochemical characteristics [10]. The major lipid class in AF was triglyceride [1], whereas phospholipids were found in a considerable proportion in IMF [11]. The fatty acid composition of AF was also significantly different from that of IMF [11]. In our recently published study, the liquid chromatography (LC)–mass spectrometry (MS)-based lipidomics was used to characterize the comprehensive lipid composition of abdominal region and breast muscle. A total of 209 lipid molecules were commonly determined as the maker candidates for AF and IMF among three chicken breeds [12]. The significantly higher PUFA-enriched glycerophospholipids metabolites, such as PC (18:3e/19:2), PC (18:4e/21:2), PC (22:6e/17:2), PE (18:2e/22:6), and PE (18:2e/22:5), elucidated the underlying reasons why IMF could improve meat flavor and nutritional value [12]. However, further investigation is required to combine these lipid metabolites with underlying molecular mechanisms to regulate and optimize body fat distribution in chickens.

In the current study, RNA sequencing (RNA-seq) was used to explore regulatory genes and signaling pathways involved in AF and IMF deposition in three native chicken breeds (Guangyuan grey chicken, Jiuyuan black chicken and Tibetan chicken). The integrative analysis of transcriptome and previous lipidomics data would be helpful to identify the valuable gene markers for specific lipid metabolite accumulation in abdominal region and muscle.

Overall description of sequencing data

We obtained overall 118.9 Gb clean bases and 792,671,934 clean reads from RNA-seq data. These clean reads were uniquely mapped to the chicken genome (GRCg6a), and the mapping frequencies were found to vary from 86.91–91.27% (Table S1). Among the mapped reads, an average of 92.95% of the total mapped reads was mapped to exons, 3.30% was mapped to introns, and 3.75% was mapped to the intergenic regions.

Identification Of Degs Between Af And Bm

Based on the criterion of “P_adj < 0.05 and |log₂(Fold Change)| ≥1”, a total of 7360, 7545 and 6335 known DEGs were screened in breast muscle of Guangyuan grey chicken (GYBM) vs abdominal fat of Guangyuan grey chicken (GYAF), breast muscle of Jiuyuan black chickens (JYBM) vs abdominal fat of Jiuyuan black chickens (JYAF) and breast muscle of Tibetan chickens (TCBM) vs abdominal fat of Tibetan chickens (TCAF) comparisons, respectively (Fig. 1A,B,C). Among these DEGs, 4737 genes were identified as common DEGs between BM and AF groups in Guangyuan grey chicken, Jiuyuan black chicken and Tibetan chicken (Fig. 1D). Although the values of log₂ (Fold Change) existed difference among three chicken breeds, these 4737 shared DEGs showed a completely consistent trend between BM and AF. We found that 2602 shared DEGs were upregulated and 2135 shared DEGs were downregulated in the BM group compared with AF group.

As expected, we observed that large numbers of genes or transcription factors involved in lipid metabolism were differentially expressed. In the Table 1, we listed shared DEGs related to glycerolipid metabolism, glycerophospholipid metabolism, and sphingolipid metabolism. We found that 21 upregulated DEGs, including GPD2, ACHE, GPD1, PLA2G4B, PLA2G4EL2, PLA2G2E, PLA2G2A, PROCA1, PTDSS2, PEMT, CRLS1, PHOSPHO1, ETNPPL, GNPAT, ST6GALNAC4, GBGT1, A4GALT, LOC101750362, ST3GAL6, PCYT1A, and PCYT1B, were enriched in glycerophospholipid metabolism pathway. However, more DEGs related to glycerolipid metabolism were significantly higher expressed in AF groups, including PLPP5, PLPP4, AGPAT2, AGPAT4, LOC422609, LPIN2, DGKQ, PNPLA2, DGAT2, PNPLA3, AWAT1, GK2, LPL, ALDH3A2, LOC425137 and AKR1E2. Sphingolipid metabolism related DEGs, including NEU3, ARSA, SGPL1, SMPD2, CERS4, and ASAH1, were significantly higher in AF groups. These genes would be the key potential regulators resulting in the difference of lipid metabolite accumulation in abdominal region and muscle.

Table 1

Common DEFs related to glycerolipid metabolism, glycerophospholipid metabolism and between BM and AF groups in three chicken breeds
Gene ID	Gene name	Putative function in KEGG pathway	Log2 (Fold Change)			Trend	P_adj
Gene ID	Gene name	Putative function in KEGG pathway	GYBM vs GYAF	JYBM vs JYAF	TCBM vs TCAF
427138	PLPP1	Glycerolipid metabolism	1.40	1.36	1.14	Up
421898	AGPAT5		2.86	3.05	2.85
421925	MBOAT2		6.58	5.34	4.81		< 0.01
418121	AGK		1.87	1.51	1.64
426846	LIPG		3.86	3.42	3.72
424321	GPD2	Glycerophospholipid metabolism	1.82	2.54	2.84	Up	< 0.01
396388	ACHE		5.09	5.51	4.65
426881	GPD1		9.02	10.24	8.94
101749229	PLA2G4B		4.00	3.03	2.62
771574	PLA2G4EL2		4.62	2.33	3.17
426747	PLA2G2E		3.20	3.37	3.05
426748	PLA2G2A		5.19	4.80	4.46
100858782	PROCA1		2.18	2.62	4.53
422997	PTDSS2		2.19	2.29	1.35
416508	PEMT		1.77	2.09	1.85
428559	CRLS1		1.62	1.74	1.91
395650	PHOSPHO1		4.10	3.92	3.65
428743	ETNPPL		3.46	2.85	5.20
421548	GNPAT		2.28	2.22	2.37
395182	ST6GALNAC4		1.16	1.21	1.56
417163	GBGT1		2.94	2.86	2.63
418223	A4GALT		2.62	2.30	2.18
101750362	LOC101750362		6.63	7.24	6.42
395138	ST3GAL6		2.10	2.27	1.43
424915	PCYT1A		1.50	1.55	1.65
418594	PCYT1B		3.68	6.62	3.41
428318	CERS4L	Sphingolipid metabolism	2.41	2.34	3.05	Up	< 0.01
422529	SGMS2	Sphingolipid metabolism	4.82	4.12	3.51	Up	< 0.01
770752	PLPP5	Glycerolipid metabolism	-4.89	-4.21	-3.02	Down	< 0.01
428987	PLPP4		-4.20	-3.91	-2.68
772114	AGPAT2		-4.42	-3.54	-3.48
421578	AGPAT4		-2.88	-2.03	-2.15
422609	LOC422609		-1.06	-1.21	-1.14
421059	LPIN2		-1.93	-1.56	-1.32
427381	DGKQ		-1.13	-1.30	-1.13
431066	PNPLA2		-6.44	-5.28	-4.78
421309	DGAT2		-5.06	-2.44	-3.23
418233	PNPLA3		-2.59	-3.61	-3.05
428693	AWAT1		-3.71	-2.91	-2.01
418589	GK2		-6.34	-5.37	-4.89
396219	LPL		-4.74	-4.73	-4.99
417615	ALDH3A2		-2.90	-3.11	-2.46
425137	LOC425137		-2.41	-2.45	-2.06
418171	AKR1E2		-2.13	-2.09	-1.79
424263	GPD1L2	Glycerophospholipid metabolism	-11.69	-8.63	-9.70	Down	< 0.01
396394	PLA2G4A		-2.28	-1.61	-1.42
107051573	PLA2G3		-5.74	-6.12	-5.52
424986	PLD1		-1.56	-1.68	-1.83
423112	CHKA		-1.49	-2.11	-1.22
422611	CDS1		-3.99	-4.12	-4.07
419517	CDS2		-4.31	-3.15	-2.78
430542	NEU3	Sphingolipid metabolism	-1.11	-1.69	-1.49	Down	< 0.01
426863	ARSA		-2.99	-2.33	-2.32
423714	SGPL1		-2.64	-2.00	-2.52
770663	SMPD2		-1.22	-1.25	-1.34
420050	CERS4		-1.89	-1.58	-1.10
422727	ASAH1		-3.15	-2.28	-2.26

Kegg Enrichment Analysis Of Degs Involve In Lipid Metabolism

To eliminate the effect of breed on lipid metabolism, we used 4737 common DEGs simultaneously identified in three chicken breeds for functional enrichment analysis. Based on 4737 shared DEGs, Fig. 2A, B, C showed the results of KEGG enrichment in Guangyuan grey chicken, Jiuyuan black chicken and Tibetan chicken, respectively. Interestingly, we found that these shared DEGs identified in GYBM vs GYAF, JYBM vs JYAF and TCBM vs TCAF comparisons were all enriched in 15 KEGG pathways, including carbon metabolism, biosynthesis of amino acids, citrate cycle (TCA cycle), pyruvate metabolism, glycine, serine and threonine metabolism, 2-Oxocarboxylic acid metabolism, glycolysis/gluconeogenesis, propanoate metabolism, calcium signaling pathway, pentose phosphate pathway, glyoxylate and dicarboxylate metabolism, fructose and mannose metabolism, apelin signaling pathway, PPAR signaling pathway, and vascular smooth muscle contraction (P < 0.05). 31 shared DEGs were significantly enriched for PPAR signaling pathway, which was the sole pathway involved in lipid metabolism (Fig. 2D). In Guangyuan grey chicken and Tibetan chicken, 28 DEGs were significantly enriched in adipocytokine signaling pathway (P < 0.05), while no significant difference was observed in Jiuyuan black chicken (P > 0.05).

Correlations Between Shared Degs And Differential Lipid Molecules

In our recently published research, the completely same samples were used for lipidomic analysis. We identified a large amount of shared glycerophospholipid lipid molecules significantly upregulated in IMF compare with that in AF. These glycerophospholipid lipid molecules included 11 cardiolipin (CL), 1 phosphatidic acid (PA), 33 phosphatidylcholines (PC), 19 phosphatidylethanolamines (PE), 4 phosphatidylinositols (PI), and 10 phosphatidylserines (PS). The difference between these individual glycerophospholipid compounds was the root cause of the higher content of phospholipids in intramuscular fat. In the present study, shared DEGs related to glycerophospholipid metabolism and PPAR signaling pathway were screened. It is reasonable to speculate that these DEGs was involved in the biosynthesis of glycerophospholipid molecules in BM. Therefore, integrated analysis of transcriptomics and lipidomics was conducted to evaluate the correlation between differential glycerophospholipid metabolites and DEGs related to glycerophospholipid metabolism and PPAR signaling pathway. 59 shared DEGs (31 related to PPAR signaling pathway and 28 related to glycerophospholipid metabolism) and 78 shared differential glycerophospholipid metabolites were used for correlation analysis in Guangyuan grey chicken, Jiuyuan black chicken and Tibetan chicken, respectively. The heatmap plots were presented in Fig. 3. Each row represents a glycerophospholipid molecule and each line represents a gene. A total of 1896, 3034 and 1496 significant correlations between DEGs and differential metabolites were identified in Guangyuan grey chicken, Jiuyuan black chicken and Tibetan chicken, respectively. In order to dig out the candidate genes for specific lipid molecule accumulation more accurately, shared significantly correlations among three chicken breeds were screened. A total of 777 significantly shared correlations were detected. Considering the important role of polyunsaturated fatty acid (PUFA) in meat flavor and nutritional value, we concentrated on those PUFA-enriched glycerophospholipid molecules (Fig. 4). We found most representative DEGs enriched in PPAR signaling pathways were negatively correlated with PUFA-enriched glycerophospholipid molecules (P < 0.01). For example, the mRNA expressions of FABP5, PPARG, ACOX1, GK2 were simultaneously negatively correlated with PC (18:3e/19:2), PE (18:2e/22:5), PC (18:0/20:4), PE (18:0/20:4), PE (18:1e/20:4) (P < 0.01). Considering the important role of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in human, we found that these four genes also significantly inhibited the biosynthesis of glycerophospholipid molecules carrying DHA and EPA, such as PC (16:0/22:6), PE (18:1e/22:6), PE (16:1e/22:6), PS (16:0/22:6), PC (21:0/22:5), PE (16:1e/22:5), and PE (18:2e/22:5) (P < 0.01). However, most DEGs related to glycerophospholipid metabolism were positively correlated with glycerophospholipid molecules, especially DHA- and arachidonic acid (ARA)- containing glycerophospholipid molecules. For example, GPD2, GPD1, PEMT, CRLS1 and GBGT1 were collectively involved in positive regulation of ARA-enriched metabolites, including PC (18:0/20:4), PC (14:0/20:4), PE (18:0/20:4), PE (18:1e/20:4), PI (18:0/20:4), PS (18:0/20:4) (P < 0.01). Furthermore, GPD1 and GDP2 were positively related to PC (16:0/22:6), PE (16:1e/22:6) and PS (16:0/22:6) (P < 0.01).

In our previous study, we identified two triacylglycerols metabolites (TAG (16:1–18:1–18:1) and TG (16:1(9Z)/16:1(9Z)/18:1(9Z))[iso3])) significantly higher in AF tissue, which were the possible reason for the higher triglyceride content verified in AF. To decrease AF content, we also conducted the correlation analysis between two triacylglycerols molecules and shared DEGs involved in glycerolipid metabolism and PPAR signaling pathway. Unfortunately, no shared correlations were identified among three chicken breeds (P > 0.01).

In poultry, excessive AF accumulation causes low slaughter efficiency and exerts a negative impact on consumer acceptance and health [2]. Increased IMF content contributes to better meat quality, including tenderness, flavor and nutritional value [4]. Lowering AF content and increasing IMF content can effectively increase the economic value of broilers, which have become a major breeding goal in poultry industry. Most previous studies concentrated on exploring the potential gene markers regulating AF or IMF deposition unilaterally based on the comparison of two types of breeds of chicken whose AF deposition or IMF deposition are exceptionally varied. For example, Cui et al., performed the gene expression profiles of breast muscles between Beijing-You and AA chickens to identify candidate genes related to IMF deposition [8]. Huang et al., used extreme high- and low-abdominal fat chicken groups to investigate crucial genes related to AF deposition [13]. Although genetic mechanisms underlying chicken fat deposition were widely studied, few were conducted to determine the mechanism that leads to tissue-specific lipid molecule accumulation.

Unlike the marbling distribution of IMF in domestic animals, the IMF of chickens cannot be obtained directly from anatomy. To elucidate the regulatory mechanism of tissue-specific lipid metabolism, DEGs were identified between AF and BM. Those genes related to glycerolipid metabolism, glycerophospholipid metabolism, and sphingolipid metabolism were displayed. To eliminate the effects of breed, three chicken breeds (Guangyuan grey chicken, Jiuyuan black chicken and Tibetan chicken) were selected in this study. Only shared DEGs identified in GYBM vs GYAF, JYBM vs JYAF and TCBM vs TCAF simultaneously were used for subsequent analysis. A total of 4737 shared DEGs showed a completely consistent trend between BM and AF, of which 2602 DEGs were upregulated and 2135 DEGs were downregulated in the BM group compared with AF group. We found that more shared DEGs related to glycerophospholipid metabolism were upregulated in BM groups, while the expressions of more DEGs related to glycerolipid metabolism were higher in AF groups. Interestingly, previous study indicated that the phospholipids were found in a considerable proportion in BM [11], while the triglyceride content in AF was significantly higher than that in BM [14]. Thus, these DEGs were potential regulators causing the difference of triglyceride and phospholipid content between AF and BM. It is well known that the AF content is far greater than IMF content in chickens. At the cellular level, Zhang et al., indicated that the lipogenesis of preadipocytes derived from AF was significantly increased compared with that of preadipocytes derived from BM [15]. Consistent with previous results in vitro, the downregulated DEGs related to glycerolipid metabolism were responsible for the decrease of total lipids in BM.

Based on 4737 shared DEGs, the results of KEGG signaling pathway analysis showed DEGs between BM and AF were jointly enriched in 15 pathways. 31 shared DEGs were significantly enriched in PPAR signaling pathway with the classic mediation of lipid metabolism. Among 31 DEGs, the RNA-seq data showed that PLIN1, FABP4, SCD, PPARG, GK2, CD36, APOC3, FABP7, LPL, RXRG, SLC27A6, FABP3, ACSL1, DBI, ACOX1, PLTP, APOA1, ANGPTL4, FABP5, ACSL4, ILK, ACAA1, PCK2, and SLC27A2 genes were downregulated, while only 7 genes (CYP27A1, HMGCS1, ME1, LOC121106914, RXRA, ACOX2 and FABP1) were upregulated in the BM group. Many studies have confirmed the role of these genes in lipid metabolism [16–18]. For example, PLIN1, which has been reported to protect lipid droplets from the hydrolytic activity of hormone sensitive lipase [18], had significantly higher expression levels in AF. PPARG, the most adipocyte-specific member of the PPAR family, is mainly expressed in adipose tissue [17]. Fatty acid-binding proteins (FABPs), also known as intracellular lipid chaperones, are a family that regulate lipid trafficking and responses in cells [16]. Fatty acids cross the cell membrane via a protein-mediated mechanism and FABPs have been responsible for fatty acid uptake [19]. Different isoforms of the FABP family are uniquely expressed in tissues involved in lipid metabolism [20]. The mRNA expressions of heart-FABP (H-FABP/FABP3), adipocyte-FABP (A-FABP/FABP4), epidermal-FABP (E-FABP/FABP5), brain-FABP (B-FABP/FABP7) were significantly higher in AF, while the expression of liver-FABP (L-FABP/FABP1) was significantly higher in IMF. This indicated that FABPs may provide tissue-specific control of lipid metabolism. Consistent with previous results in vitro [21], it was considered that tissue-specific lipid deposition mostly depended on the changed expression of related genes through the PPAR pathway.

Glycerophospholipids are dominant in cell membranes and play vital role in cellular functions such as signal transduction, protein function and regulation of the transport process [22]. In our recently published research, lipidomic analysis showed that the significantly higher abundance of 11 CL, 1 PA, 33 PC, 19 PE, 4 PI, and 10 PS was observed in IMF compared with that in AF. PUFAs are predominantly deposited in the glycerophospholipid of muscle, rather than free fatty acids [23, 24]. The higher abundance of PUFA-containing glycerophospholipids in IMF could elucidate its beneficial roles in imparting a characteristic flavor and nutritional value to meat [12]. Therefore, exploring the important candidate gene markers for specific PUFA-containing glycerophospholipid molecules would contribute to a more thorough understanding of IMF deposition and poultry meat quality.

The limited amounts of n-3 PUFAs present in meats became nutritionally important [25]. It is worthwhile to mention the beneficial function of n-3 PUFAs, notably EPA and DHA. They play a vital role in the prevention and treatment of certain diseases, such as Alzheimer's disease, cardiovascular disease (CVD), type 2 diabetes, hypertension, psychiatric diseases and several cancers [26, 27]. Besides, DHA is the core nutrient for human brain function, neuronal development and visual acuity [28]. In the present study, significantly negative correlations were observed between 4 genes (FABP5, PPARG, ACOX1, GK2) and 7 glycerophospholipid molecules carrying DHA and EPA (PC (16:0/22:6), PE (18:1e/22:6), PE (16:1e/22:6), PS (16:0/22:6), PC (21:0/22:5), PE (16:1e/22:5), and PE (18:2e/22:5)). However, previous studies have shown that DHA and EPA could be natural PPAR agonist to activate PPARG signaling [29, 30]. In golden pompano hepatocytes, the overexpression of PPARG increased the DHA content, whereas suppression of PPARG expression diminished this effect, suggesting that PPARG play an active role in regulating DHA content [31]. Here, we conducted the correlation analysis between PPARG and individual DHA- and EPA-containing glycerophospholipid rather than total DHA or EPA content. The molecular mechanism of PPARG regulating these 7 glycerophospholipid molecules carrying DHA and EPA need to be verified in chicken intramuscular preadipocyte. Similar with DHA, ARA also has significant physiological function during development and growth especially of the central nervous system and retina [32]. In this study, GPD2, GPD1, PEMT, CRLS1 and GBGT1 were collectively involved in positive regulation of ARA-enriched metabolites. Whether these DEGs positively regulated ARA content or these ARA-enriched molecules also need to be verified at cellular level.

In this study, the analysis of transcriptome data identified 4737 shared differential expressed genes between IMF and AF deposition among three chicken breeds. Differential expressed genes involved in glycerophospholipid metabolism and glycerolipid metabolism were potential regulators resulting in the difference of lipid metabolite accumulation in IMF and AF. PPAR signaling pathway was the most important pathway involved in tissue-specific lipid deposition. Most representative DEGs enriched in PPAR signaling pathways, such as FABP5, PPARG, ACOX1, GK2 were negatively correlated with PUFA-enriched glycerophospholipid molecules. Most DEGs related to glycerophospholipid metabolism, such as GPD2, GPD1, PEMT, CRLS1 and GBGT1 were positively correlated with glycerophospholipid molecules, especially DHA- and ARA-containing glycerophospholipid molecules. These results contributed a more thorough understanding of lipid deposition and poultry meat quality.

Animals and Sample collection

300-day-old Guangyuan grey chickens, Jiuyuan black chickens and Tibetan chickens were obtained from Sichuan Tianguan Ecological Agriculture and Animal Husbandry Co., Ltd (Guangyuan City, Sichuan Province), Wanyuan Hengkang Agricultural Development Co., Ltd (Wanyuan City, Sichuan Province) and Maoxian Jiuding Ecological Poultry Breeding Co. Ltd (Wenchuan City, Sichuan Province), respectively. All chicken were kept under similar environmental and feeding conditions. The diet for all chicken according to the feeding standard《NY/T33-2004》was shown in Table S2. 9 female chickens with normal and similar body weights from each breed were selected for sample collection. After a 12h fast and breathing anesthesia until unconsciousness, the chickens were sacrificed by cutting carotid arteries. AF and breast muscle (BM) were collected and immediately stored at − 80°C until RNA isolation was performed.

Transcriptome Profiling

RNA was extracted as described previously[33]. RNA purity was checked using NanoPhotometer spectrophotometer (Thermo Scientific). RNA integrity was assessed using Agilent 2100 Bioanalyzer (Agilent Technologies) and agarose gel electrophoresis. Only qualified RNA samples were used for RNA-seq. Qualified RNA from 3 BM or 3 AF samples collected from each chicken breed pooled into one mixture. Adapter sequences and other low-quality data were removed by Trimmomatic v0.32. Sequencing reads were then aligned to the chicken reference genome of GRCg6a using the HISAT2 v2.0.5

Differential Expression Analysis And Kegg Enrichment Analysis

Differential expression of transcripts was performed using DESeq2 R package (v 1.20.2). The resulting P values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate (FDR). Genes with an adjusted P (P_adj) value < 0.05 and |log₂(Fold Change)| ≥1 were considered as differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the KEGG online website (http://www.genome.jp/kegg/).

Integrated Analysis Of Transcriptomics And Lipidomics

In our recently published study, we used the same samples for lipidomic analysis. A total of 209 lipid molecules were commonly determined as the maker candidates for AF and IMF among three chicken breeds. The pair-wise Pearson’s correlation coefficients were generated to evaluate the correlation between 209 differential metabolites and DEGs between AF and BM in Guangyuan grey chicken, Jiuyuan black chicken and Tibetan chicken, respectively. Correlation coefficients were visualized by heatmap plots using the R package “complex_heat_map”.

abdominal fat

IMF

intramuscular fat

breast muscle

DEG

differentially expressed gene

LC-MS

liquid chromatography–mass spectrometry

cardiolipin

CVD

cardiovascular disease

phosphatidic acids

phosphatidylcholines

phosphatidylethanolamines

phosphatidylserines

phosphatidylinositols

PUFA

polyunsaturated fatty acids

EPA

eicosapentaenoic acid

DHA

docosahexaenoic acid

ARA

arachidonic acid

RNA-seq

RNA sequencing

FDR

false discovery rate

KEGG

Kyoto Encyclopedia of Genes and Genomes

GYBM

breast muscle of Guangyuan grey chicken

GYAF

abdominal fat of Guangyuan grey chicken

JYBM

breast muscle of Jiuyuan black chickens

JYAF

abdominal fat of Jiuyuan black chickens

TCBM

breast muscle of Tibetan chickens

TCAF

abdominal fat of Tibetan chickens

GPD2

glycerol-3-phosphate dehydrogenase 2

GPD1

glycerol-3-phosphate dehydrogenase 1

PEMT

phosphatidylethanolamine N-methyltransferase

CRLS1

cardiolipin synthase 1

GBGT1

globoside alpha-1,3-N-acetylgalactosaminyltransferase 1

FABP

fatty acid binding protein

PPARG

peroxisome proliferator activated receptor gamma

ACOX1

acyl-CoA oxidase 1

GK2

glycerol kinase 2

Ethics approval and consent to participate

The animal study is approved by Institutional Animal Care and Use Committee of Southwest University of Science and Technology (Permit No. SLSE- 2021-0032). The study is in accordance with the ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments). All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Availability of data and materials

The RNA-seq raw data supporting the conclusions of this article have been deposited into Sequence Read Archive under accession number PRJNA902098.

Competing interests

The authors declare that they have no conflict of interests.

Funding

This research was funded by the Natural Science Foundation of Southwest University of Science and Technology (Grant No. 22zx7151), the Open Fund of Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province (Grant No. CNDK-2020- 01), and the Sichuan Science and Technology Program.

Author’s contributions

J.Li and Q.Huang designed the study and draft the manuscript; J.Li, R.Li, and J.Wu performed the experiments and analyzed the data. P.Ren and M.Chen revised the manuscript. All authors read and approved the final manuscript.

Acknowledgments

Not applicable.

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Molecular regulation of differential lipid molecule accumulation in intramuscular fat and abdominal fat of chicken

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Abstract

Background

Results

Conclusion

Figures

Introduction

Results

Overall description of sequencing data

Identification Of Degs Between Af And Bm

Kegg Enrichment Analysis Of Degs Involve In Lipid Metabolism

Correlations Between Shared Degs And Differential Lipid Molecules

Discussion

Conclusion

Materials And Methods

Animals and Sample collection

Transcriptome Profiling

Differential Expression Analysis And Kegg Enrichment Analysis

Integrated Analysis Of Transcriptomics And Lipidomics

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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