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. 2020 Feb 18;98(3):skaa062. doi: 10.1093/jas/skaa062

Role of peroxisome proliferator-activated receptor-α on the synthesis of monounsaturated fatty acids in goat mammary epithelial cells

Huibin Tian 1, Jun Luo 1,1,✉, Hengbo Shi 2, Xiaoying Chen 1, Jiao Wu 1, Yusheng Liang 3, Cong Li 1, Juan J Loor 3
PMCID: PMC7070155  PMID: 32067038

Abstract

A key member of the nuclear receptor superfamily is the peroxisome proliferator-activated receptor alpha (PPARA) isoform, which in nonruminants is closely associated with fatty acid oxidation. Whether PPARA plays a role in milk fatty acid synthesis in ruminants is unknown. The main objective of the present study was to use primary goat mammary epithelial cells (GMEC) to activate PPARA via the agonist WY-14643 (WY) or to silence it via transfection of small-interfering RNA (siRNA). Three copies of the peroxisome proliferator-activated receptor response element (PPRE) contained in a luciferase reporter vector were transfected into GMEC followed by incubation with WY at 0, 10, 20, 30, 50, or 100 µM. A dose of 50 µM WY was most effective at activating PPRE without influencing PPARA mRNA abundance. Transfecting siRNA targeting PPARA decreased its mRNA abundance to 20% and protein level to 50% of basal levels. Use of WY upregulated FASN, SCD1, ACSL1, DGAT1, FABP4, and CD36 (1.1-, 1.5-, 2-, 1.4-, 1.5-, and 5-fold, respectively), but downregulated DGAT2 and PGC1A (−20% and −40%, respectively) abundance. In contrast, triacylglycerol concentration decreased and the content and desaturation index of C16:1 and C18:1 increased. Thus, activation of PPARA via WY appeared to channel fatty acids away from esterification. Knockdown of PPARA via siRNA downregulated ACACA, SCD1, AGPAT6, CD36, HSL, and SREBF1 (−43%, −67%, −16%, −56%, −26%, and −29%, respectively), but upregulated ACSL1, DGAT2, FABP3, and PGC1A (2-, 1.4-, 1.3-, and 2.5-fold, respectively) mRNA abundance. A decrease in the content and desaturation index of C16:1 and C18:1 coupled with an increase in triacylglycerol content accompanied those effects at the mRNA level. Overall, data suggest that PPARA could promote the synthesis of MUFA in GMEC through its effects on mRNA abundance of genes related to fatty acid synthesis, oxidation, transport, and triacylglycerol synthesis.

Keywords: gene expression, lactation, milk fat, PPAR, ruminant

Introduction

Lactating mammary cells actively esterify fatty acids into triacylglycerol (TAG) that eventually lead to formation of milk fat (Clegg et al., 2001). In rodents and bovine, the complex networks of genes and pathways some under coordination of peroxisome proliferator-activated receptors (PPAR) coordinate fatty acid and TAG synthesis during lactation (Rudolph et al., 2007; Bionaz et al., 2013). In nonruminants, it is well established that PPAR play important roles in the regulation of adipocyte differentiation, lipid metabolism, and inflammation among others (Desvergne and Wahli, 1999; Feige et al., 2006; Michalik and Wahli, 2007; Huang et al., 2016). There are 3 PPAR isotypes: PPARA, PPARD, and PPARG (Sher et al., 1993). When the transcription factor is activated by specific ligands such as endogenous fatty acids, it forms a heterodimer with retinoid X receptors binding to the specific DNA region called PPAR response elements (PPRE; Heinäniemi et al., 2007) and triggering a transcriptional cascade (Varga et al., 2011).

In nonruminants, it is well established that PPARA governs transcription of genes involved in fatty acid β-oxidation, cholesterol metabolism, and gluconeogenesis (Volcik et al., 2008; Huang et al., 2016; Lakhia et al., 2018). In murine liver, the PPARA agonist fenofibrate increased not only genes involved in β-oxidation, but also genes related to de novo lipogenesis and fatty acid elongation (Oosterveer et al., 2009). The same study also indicated that the lipogenic activity of PPARA depends on a mature SREBP1c that is transcriptionally active (Oosterveer et al., 2009). A CHIP-chip analysis of PPARA in human hepatoma cells revealed that some genes containing a PPARA-binding region in their promoter are also targets of sterol regulatory element-binding protein (SREBP; Meer et al., 2010). Thus, at least in rodent cells, data suggest potential cross-talk between PPARA and SREBP in the regulation of fatty acid synthesis.

Compared with nonruminants, the function of PPARA in ruminant fatty acid metabolism differs to some degree (Bionaz et al., 2012), for example, the bovine PPAR pathway seems more responsive to saturated than unsaturated fatty acids (Carlsson et al., 2001; Tai et al., 2005). Whether PPARA has any role in regulating milk fatty acid synthesis and composition is unknown. Thus, the aim of this study was to explore the effect of PPARA on fatty acid metabolism-related genes mRNA abundance by using the PPARA agonist WY-14643 (WY) and a specific small-interfering RNA (siRNA) for knocking down PPARA in primary goat mammary epithelial cells (GMEC).

Materials and Methods

Ethics statement

All experimental procedures were conducted under the approval of the Institutional Animal Care and Use Committee in the College of Animal Science and Technology, Northwest A&F University, Yang Ling, China (permit number: 15–516, date: 13 September 2015).

Cell culture and treatment

The GMEC were isolated from 5 Xinong Saanen dairy goats at peak lactation (60 d after parturition) using a tissue-block technique (Zhu et al., 2015). Tissue was cut into about 1 mm3 cubes, plated in a 60-mm culture dishes and cultured in 5% CO2 at 37 °C until mammary epithelial cells separated from the tissue block. The culture medium was changed every 2 d during tissue cultivation. Cells were then digested from the tissue block and purified using trypsin digestion for 4 or 5 passages to remove other cell types, especially fibroblasts (Zhu et al., 2015). Cells were cultured in a basal DMEM/F12 medium (11320-033, Invitrogen Corp., Waltham, MA) containing 10% fetal bovine serum (10099–141, Invitrogen), bovine insulin (5 μg/mL, 16634, Sigma, St. Louis, MO), hydrocortisone (5 mg/L; H0888, Sigma), penicillin/streptomycin (10 kU/L, 080092569, Harbin Pharmaceutical Group, Harbin, P. R. China), and epidermal growth factor (EGF; 10 ng/mL; PHG0311, Invitrogen). The GMEC were passaged every 48 h using 0.25% trypsin and were maintained in 5% CO2 at 37 °C. Twelve hours before treatment with WY-14643 (S8029, Selleck, Ltd., Shanghai, China), the medium was changed to serum-free DMEM/F12 medium containing hydrocortisone (5 mg/L), bovine insulin (5 μg/mL), penicillin/streptomycin (10 kU/L), EGF (10 ng/mL), sodium acetate (5 mmol/L), BSA (1 mg/mL, A1933, Sigma), and prolactin from sheep pituitary (2.5 μg/mL, L6520, Sigma) for 12 h to promote differentiation of GMEC. Cells were then treated with serum-free medium containing ovine prolactin for 12 h. After that, GMEC were treated with WY for 24 h in serum-free medium containing prolactin and cells cultured with dimethyl sulfoxide (DMSO) served as control. After treatment, GMEC were harvested for RNA extraction, TAG assay, and fatty acid extraction.

Luciferase assay

When cells were at about 80% confluence in 48-well plates, 3 copies of the PPRE sequence constructed into pGL3-Basic (E1751, Promega, Madison, WI) vector plasmid were transfected into GMEC along with a Renilla luciferase vector (E2231, pRL-TK, Promega) as an internal control using Lipofectamine 2000 (11668019, Invitrogen). In brief, the PPRE sequence was 5′-GTCGACAGGGGACCAGGACAAAGGTCACGTTCGGG-AGGTCAC-3′ for 3 copies and constructed to pGL3-Basic backbone. The total DNA of each well was 0.3 µg, and the mass ratio of pGL3-PPRE and pRL-TK was 25:1. The GMEC were treated with 0, 10, 20, 30, 50, and 100 μM WY for 24 h. Cells were then washed with PBS 3 times and lysed with Promega passive lysis (Promega) for 30 min at room temperature. Relative luciferase activity was tested using Dual-Luciferase Reporter Assay (E1910, Promega) with a Fluoroskan Ascent apparatus (Thermo Scientific, Waltham, MA), and the ratio of firefly luciferase activity compared with Renilla luciferase activity was analyzed.

RNA interference experiment

PPARA-specific siRNA was designed using the goat PPARA coding sequence (GenBank: HM600811.1) and synthesized by Invitrogen (Shanghai, China). The siRNA sequences (siPPARA) were as follows: sense 5′–3′: CCCAAGUUCGACUUUGCAATT and antisense 5′–3′: UUGCAAAGUCGAACUUGGGTT; the scrambled small interference RNA (siSCR) sequence are as follows: sense 5′–3′: UUCUCCGAACGAACGUGUCACGUTT and antisense: 5′–3′ ACGUGACACGUUCGGAGAATT. Small-interfering RNA was transfected into GMEC using Lipofectamine RNAiMAX (13778-150, Invitrogen Corp.) at 100 µM according to the manufacturer’s instructions. Cells were cultured in basal medium containing 2.5 μg/mL prolactin. Cells were harvested after 48-h treatment and used for RNA extraction, TAG assay, and fatty acid analysis. At the same time, the scrambled small interference RNA was transfected as control.

RNA extraction and real-time quantitative PCR

Total RNA was extracted using the RNAiso Plus kit (9109, Takara Bio Inc., Otsu, Japan). The first-strand cDNA was synthesized using PrimeScript RT Reagent Kit with gDNA Eraser (RR047A, Perfect Real Time, Takara) and 500 ng RNA in a 20-µL reaction system. Quantitative Real-Time PCR was performed using SYBR Premix Ex Taq II (RR820A, Perfect Real Time, Takara) on a CFX96 Real-Time system (Bio-Rad, Hercules, CA) with the following conditions: 95 °C for 30 s, followed by 40 cycles at 95 °C for 5 s and 60 °C for 30 s; a dissociation curve was performed at 95 °C for 10 s and then from 65 °C to 95 °C with a 0.5 °C increase. Relative mRNA abundance was normalized using ribosomal protein S9 (RPS9), ubiquitously expressed transcript (UXT), and mitochondrial ribosomal protein L39 (MRPL39) according to a previous report (Bionaz and Loor, 2007; Bonnet et al., 2013). The specific primers related to the experiment are listed in Table 1.

Table 1.

Primers used for real-time quantitative PCR

NCBI accession no. Gene1 Primer sequences Size, bp Efficiency2
siRNA WY
JN236219.1 ACACA F: CTCCAACCTCAACCACTACGG 171 2.08 1.95
R: GGGGAATCACAGAAGCAGCC
XM_018063771 ACOX1 F: CGAGTTCATTCTCAACAGTCCT 245 1.91 2.04
R: GCATCTTCAAGTAGCCATTATCC
BC119914 ACSL1 F: GTGGGCTCCTTTGAAGAACTGT 120 1.98 2.00
R: ATAGATGCCTTTGACCTGTTCAAAT
NM_00108366.1 AGPAT6 F: AAGCAAGTTGCCCATCCTCA 101 1.97 1.96
R: AAACTGTGGCTCCAATTTCGA
X91503 CD36 F: GTACAGATGCAGCCTCATTTCC 81 1.95 1.89
R: TGGACCTGCAAATATCAGAGGA
DQ380249.1 DGAT1 F: CCACTGGGACCTGAGGTGTC 101 1.91 2.00
R: GCATCACCACACACCAATTCA
BT030532.1 DGAT2 F: CATGTACACATTCTGCACCGATT 100 2.08 2.07
R: TGACCTCCTGCCACCTTTCT
NM_001009350 FABP3 F: GATGAGACCACGGCAGATG 120 2.06 2.02
R: GTCAACTATTTCCCGCACAAG
DV778074 FABP4 F: TGGTGCTGGAATGTGTCATGA 101 2.04 2.11
R: TGGAGTTCGATGCAAACGTC
DQ915966.3 FASN F: GGGCTCCACCACCGTGTTCCA 226 1.96 2.11
R: GCTCTGCTGGGCCTGCAGCTG
XM_018062484 HSL F: GGGAGCACTACAAACGCAACG 118 2.00 2.06
R: TGAATGATCCGCTCAAACTCG
NM017446 MRPL39 F: AGGTTCTCTTTTGTTGGCATCC 101 2.04 2.07
R: TTGGTCAGAGCCCCAGAAGT
XM_018049155 PGC1A F: GTACCAGCACGAAAGGCTCAA 120 2.02 2.00
R: ATCACACGGCGCTCTTCAA
HM600811.1 PPARA F: TACTCTCGGCAGACTTCCTAC 359 1.92 1.94
R: CCTCCTCACATCTGTCATACAC
DT860044 RPS9 F: CCTCGACCAAGAGCTGAAG 64 2.13 2.05
R: CCTCCAGACCTCACGTTTGTTC
GU947654 SCD1 F: CCATCGCCTGTGGAGTCAC 257 2.00 2.09
R: GTCGGATAAATCTAGCGTAGCA
HM443643.1 SREBF1 F: CTGCTGACCGACATAGAAGACAT 81 1.91 2.08
R: GTAGGGCGGGTCAAACAGG
NM_001037471 UXT F: CAGCTGGCCAAATACCTTCAA 125 2.00 2.04
R: GTGTCTGGGACCACTGTGTCAA
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1 ACACA, acetyl-coenzyme A carboxylase alpha; ACOX1, acyl-CoA oxidase 1; AGPAT6, 1-acylglycerol-3-phosphate O-acyltransferase 6; CD36, thrombospondin receptor; DGAT1, diacylglycerol acyl transferase 1; DGAT2, diacylglycerol acyl transferase 2; FABP3, fatty acid binding protein 3; FABP4, fatty acid binding protein 4; FASN, fatty acid synthase; HSL, hormone-sensitive lipase; MRPL39, mitochondrial ribosomal protein L39; PGC1A, peroxisome proliferator-activated receptor gamma coactivator 1 alpha; PPARA, peroxisome proliferator-activated receptor alpha; RPS9, ribosomal protein S9; SCD1, stearoyl-CoA desaturase 1; SREBF1, sterol regulatory element-binding transcription factor 1; UXT, ubiquitously expressed transcript.

2Efficiency of amplification was calculated by LinRegPCR software (www.linregpcr.nl). siRNA represented the amplification efficiency in siSCR and siPPARA groups; WY represented the amplification efficiency in CTR and WY groups.

Data of RT-qPCR was analyzed using LinRegPCR software (www.linregpcr.nl) for quantification analysis (Ramakers et al., 2003). The amplification efficiency of each target gene is shown in Table 1. The normalization factor (NF) was the geometrical mean of data exported from LinRegPCR analysis of MRPL39, UXT, and RPS9. The stability of 3 reference genes was evaluated using geNorm (Vandesompele et al., 2002). An expression stability value (M) less than 1.5 and the pair-wise variation value (V) below 0.15 are considered stable for reference genes and indicate a reliable NF. The M-value in the siRNA group was <0.21, and M-value in the WY treatment group was <0.20. The V-values in the siRNA and WY treatment groups were 0.065 and 0.069, respectively. Thus, all 3 reference genes were stable, and the NF was deemed reliable.

Protein isolation and western blot

The GMEC were digested using trypsin, washed with PBS buffer 3 times, and resuspended in RIPA lysis buffer (R0010, Solarbio, Beijing, China) containing protease inhibitor cocktail (04693132001, Roche Diagnostics Ltd, Mannheim, Germany). Protein concentration was analyzed with the BCA assay kit (23227, Thermo Fisher Scientific, Rockford, IL). Thirty micrograms of total cell protein from every sample was separated by SDS-PAGE. The gel was then transferred onto 0.45-μm nitrocellulose membranes (HATF00010, Millipore, Massachusetts), and membranes were blocked for 1.5 h using 5% skim milk (232100, BD, Franklin Lakes, NJ). Membranes were incubated with primary antibody for PPARA (1:1,000, Cat#ab24509, Abcam, Cambridge, MA) and a monoclonal antibody for β-actin (1:2,000, CW0096, CW Biotech, Beijing, China) overnight at 4 °C. Membranes were washed with TBST buffer 3 times, and goat anti-mouse (1:2,000, CW0102, CW Biotech) and goat anti-rabbit (1:2,000, CW0103, CW Biotech) HRP-conjugated IgG were used as secondary antibodies. Signals were detected using the enhanced chemiluminescent (ECL) Western blot system (1705061, Bio-Rad). The intensity of bands was quantified using ImageJ software and relative protein abundance was normalized to β-actin.

Measurement of cellular TAG

The GMEC in 60-mm culture dishes were treated with siRNA or WY for TAG analysis. Total cellular TAG was extracted and measured using the TAG assay kit (E1013, Applypen Technologies Inc., Beijing, China). Data were collected using a Biotek microplate reader. The protein concentration was measured with the BCA assay kit (Thermo Fisher Scientific). The concentration of cellular TAG was normalized by total protein and reported as µg/mg protein.

Fatty acid extraction and analysis

The GMEC were cultured in 60-mm dishes for total fatty acid extraction. After treatment with siRNA and WY, cells were washed with PBS buffer 3 times. Two-microliter aliquots of 2.5% (vol/vol) sulfuric acid:methanol were added to culture dishes, and cells were then transferred to glass tubes for methylation as previously described (Xu et al., 2016). Fatty acid samples were analyzed by gas chromatography (Agilent 7890A; Agilent Technologies Inc., Santa Clara, CA) using an HP-5 column (Agilent Technologies Inc.) as previously described (Wang et al., 2012). The content of fatty acids was calculated as percentage of total peak area that could be measured. The desaturation index for C16:1 and C18:1 was calculated as the ratio of unsaturated fatty acid to the sum of unsaturated and saturated fatty acids (Morales et al., 2000).

Statistical analysis

All data were analyzed by SPSS 19.0 (SPSS Inc., Chicago, IL). Treatments were done for 3 independent experiments in triplicates, and results are presented as mean ± SEM. Significant differences between groups were evaluated using Student’s t-test (unpaired and two-tailed) and were considered statistically significant at P < 0.05. Significances of luciferase activity were determined by one-way ANOVA, and different letters (a to d) denote significant differences (P < 0.05) in different groups.

Results

The PPARA agonist WY-14643 enhanced activity of PPRE

Compared with other incubations, the activity of PPRE increased significantly (P < 0.05) when GMEC were treated with WY at 50 μM. Thus, this concentration of WY was selected for subsequent experiments (Fig. 1).

Figure 1.

Figure 1.

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The peroxisome proliferator-activated receptor alfa (PPARA)-specific ligand WY-14643 (WY) activated the PPARA response element (PPRE) in goat mammary epithelial cells (GMEC). GMEC were transfected with pGL3-PPRE×3 and pRL-TK vectors. After transfected with the vectors, cells were treated with WY at 0, 10, 20, 30, 50, and 100 μM for 24 h. The GMEC were lysed to measure luciferase activity, and the relative luciferase activity was calculated as the ratio of firefly compared with Renilla luciferase activity. Values are mean ± SEM. The different letters (a to d) mean significant differences (P < 0.05) in different groups.

Activation of PPARA affected the abundance of key genes involved in fatty acid metabolism

Although PPARA abundance level did not change when WY was added to culture medium (Fig. 2A), activation of PPARA increased (P < 0.05) abundance of the fatty acid synthesis-related genes FASN, SCD1 and ACSL1 by 1.1-fold, 1.5-fold and 2-fold, TAG synthesis-related genes DGAT1 by 1.4-fold, and fatty acid uptake and transport-related genes FABP4 and CD36 by 1.5-fold and 5-fold. Compared with the control, after treatment with WY abundance of DGAT2 and PGC1A were downregulated (P < 0.05) by 20% and 40%, respectively (Fig. 2B–F).

Figure 2.

Figure 2.

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mRNA level of PPARA and genes related to fatty acid metabolism after PPARA activation with WY at 50 μM for 24 h. (A) PPARA expression level after WY treatment. Expression of genes related to (B) fatty acid synthesis (FASN, ACACA, SCD1, ACSL1), (C) triacylglycerol synthesis (DGAT1, DGAT2, AGPAT6), (D) fatty acid uptake and transport (FABP3, FABP4, CD36), (E) fatty acid oxidation (HSL, ACOX1, PGC1A), and (F) transcription factor (SREBF1). Values are presented as mean ± SEM. *P < 0.05 vs. control.

Knockdown of PPARA affected abundance of key genes related to fatty acid metabolism

Compared with the control (siSCR), knockdown of PPARA downregulated both its mRNA and protein abundance (P < 0.05, Fig. 3A and B). Silencing of PPARA decreased (P < 0.05) the mRNA abundance of fatty acid synthesis-related genes ACACA and SCD1 by 43% and 67%, TAG synthesis gene AGPAT6 by 16%, fatty acid uptake and transport-related genes CD36 by 56%, lipolysis gene HSL by 26%, and the transcription factor SREBF1 by 29%. Knockdown of PPARA upregulated (P < 0.05) the mRNA abundance of ACSL1, PGC1A, FABP3, and DGAT2 by 2-, 2.5-, 1.3-, and 1.4-fold, respectively (Fig. 3C–F).

Figure 3.

Figure 3.

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Knockdown of PPARA by siRNA changed mRNA expression levels of genes related to fatty acid metabolism in goat mammary epithelial cells (GMEC). (A) PPARA mRNA abundance after its inhibition. (B) PPARA protein level in GMEC transfected with siSCR or PPARA siRNA. Expression of genes related to (C) fatty acid synthesis (FASN, ACACA, SCD1, ACSL1), (D) triacylglycerol synthesis (DGAT1, DGAT2, AGPAT6), (E) fatty acid uptake and transport (FABP3, FABP4, CD36), (F) fatty acid oxidation (HSL, ACOX1, PGC1A), and (G) transcription factor (SREBF1). The intensity of bands was quantified using ImageJ software, and relative protein abundance was normalized to β-actin. Values are presented as mean ± SEM. *P < 0.05 vs. control.

Activation of PPARA decreased TAG content and increased the C16:1 and C18:1 desaturation index

Compared with control group treated with DMSO, the addition of WY resulted in a decrease of cellular TAG content (P < 0.05; Fig. 4A). However, WY treatment increased (P < 0.05) the percentage of C16:1 and C18:1 (P < 0.05; Fig. 5C and D), whereas it decreased content of C18:0 (Fig. 5B). Compared with the control group, the desaturation index of C16:1 and C18:1 increased (P < 0.05; Fig. 5E and F).

Figure 4.

Figure 4.

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Activation (A) or knockdown (B) of PPARA affected the content of cellular triacylglycerol (TAG) in goat mammary epithelial cells (GMEC). Values are presented as mean ± SEM. *P < 0.05 vs. control.

Figure 5.

Figure 5.

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Activation of PPARA altered the fatty acid content in goat mammary epithelial cells (GMEC). The percentage of C16:0 (A), C18:0 (B), C16:1 (C), C18:1 (D) in GMEC treated with PPARA agonist WY were shown as a proportion of the total fatty acids. Desaturation index of C16:1 (E) and C18:1 (F). Data were presented as mean ± SEM. *P < 0.05 vs. control.

Knockdown of PPARA increased TAG content and decreased C16:1 and C18:1 desaturation index

Cellular TAG content increased after PPARA knockdown (P < 0.05; Fig. 4B). Compared with the control group, the content of C16:0, C16:1, and C18:1 decreased (P < 0.05; Fig. 6A, C, and D) when PPARA was knocked down. The C16:1 and C18:1 desaturation index decreased (P < 0.05) after knockdown of PPARA (Fig. 6E and F).

Figure 6.

Figure 6.

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Knockdown of PPARA influenced the percentage and desaturation index of fatty acids in goat mammary epithelial cells (GMEC). C16:0 (A), C18:0 (B), C16:1 (C), C18:1 (D) content in GMEC transfected with PPARA siRNA and control siRNA. The desaturation index of C16:1 (E) and C18:1 (F). Values are presented as mean ± SEM. *P < 0.05 vs. control.

Discussion

Previous studies in nonruminants illustrated that PPARA plays an important role in hepatic fatty acid oxidation (Hashimoto et al., 2000; Kersten and Stienstra, 2017), but its function in fatty acid synthesis, desaturation, and esterification in lactating mammary gland has not been studied. A role for this PPAR isotype could be biologically relevant due to the well-established activation of lipid metabolism at the onset of lactation (Loor et al., 2013). Because of the large degree of flux between fatty acids taken up by mammary gland and those synthesized de novo, activity of PPARA as a result of its interaction with fatty acids could exert a role in terms of maintaining a threshold of free fatty acids that is not detrimental to cellular activity (Loor, 2010). The greater abundance of PPARA relative to the gamma and delta isoforms in bovine mammary tissue suggested a potential biological role in lipid metabolism (Bionaz et al., 2013).

Fatty acid uptake during lactation is extremely active and supplies the lipid synthesizing machinery with substrates. CD36 is a high-affinity cell surface receptor for long-chain fatty acids (LCFA) and is essential for their transport (Ibrahimi et al., 1996). Relative to the prepartum period, the mRNA abundance of CD36 in mammary tissue increased remarkably during lactation in dairy cows (Bionaz and Loor, 2008). Two PPRE exist in the murine CD36 promoter (Sato et al., 2002; Tan et al., 2002). The marked upregulation of CD36 upon activation of PPARA along with its downregulation after knockdown suggested it is a target gene in the goat as reported in bovine (Bionaz et al., 2012). These changes also agree with those for C16:0 and C16:1 concentration after knockdown of PPARA, suggesting that C16:0 uptake in GMEC is correlated with CD36 protein abundance as demonstrated in vitro in nonruminant cells (Bonen et al., 2004). The lack of effect on C16:0 in response to WY could have prevented a buildup of the fatty acid that would be harmful for cell survival.

Treatment of bovine kidney cells with WY downregulated FABP3 expression and upregulated FABP4 (Bionaz et al., 2012). Our data for FABP3 and FABP4 agreed with that previous study. Because FABP4 has a large affinity for C18:1 (Bionaz and Loor, 2008), it could be possible that the increase of C18:1 after PPARA activation was due to the upregulation of FABP4.

At least in mouse liver, there is evidence that PPARA controls genes related to not only fatty acid oxidation but also fatty acid synthesis including FASN, ACACA, and SCD1 (Rakhshandehroo et al., 2010). In addition, in nonruminants, PPARA can enhance the protein level of mature SREBP-1 without altering its mRNA abundance (Knight et al., 2005). Another report also indicated that despite upregulation of the SREBP-1c targets FASN, ACACA, and SCD1 by agonists of PPARA, mRNA expression of SREBP-1c was not altered (Oosterveer et al., 2009). Further research indicated that PPARA could regulate fatty acid synthesis through enhancing SREBP-1 proteolytic cleavage (Meer et al., 2010). Together, results from the present study support those previous findings. Binding sites for SREBP-1 and PPARG have been identified on the goat SCD1 gene promoter (Yao et al., 2017), underscoring the existence of cross-talk between PPARA and SREBP-1 and a potential coordination of target gene expression. As such, activation of PPARA could conceivably have some control over de novo fatty acid synthesis in GMEC.

Alterations in the content of C16:1 and C18:1 would be the result of changes in SCD1 expression, which catalyzes the desaturation of MUFA. A decrease of C16:0 in the PPARA siRNA group may have been due to the decrease of de novo fatty acid synthesis regulated by FASN and ACACA. A study in pancreatic beta cells indicated that susceptibility to saturated fatty acids was regulated by LXR/PPARA-dependent SCD expression (Hellemans et al., 2009), which our results on SCD support. Another study also showed increased expression of SCD1 in liver after PPARA agonist treatment (Rogue et al., 2014). These results indicated that changes in MUFA synthesis are caused by activity of PPARA through regulation of SCD1.

The expression of ACSL1, a known target gene of PPARA in nonruminants, was upregulated in MDBK cells upon treatment with WY (Bionaz et al., 2012), which agrees with our data for this gene in the WY group. While unexpected, the ACSL1 upregulation in the PPARA knockdown group could be explained in part by an effect of endogenously synthesized fatty acids on ACSL1. As a result, the activated fatty acids would be channeled to FABP3, a response supported by the simultaneous upregulation of ACSL1 and FABP3 in mammary tissue during lactation (Bionaz and Loor, 2008) and in the present study when PPARA was knocked down. ACSL1 is a target gene of PPARG in adipose and a PPARA target gene in liver of mice (Phillips et al., 2010). Thus, different regulatory mechanisms underscore potentially different functions of ACSL1 depending on tissue type. The relationship between ACSL1 and PPARA in mammary gland merits further study.

There is a close connection between content of cellular TAG and the expression of DGAT1 and DGAT2. A previous study in fish showed that a low amount of exogenous WY (5 mg WY/kg weight of fish) increased the mRNA expression of DGAT1 in fish liver (Urbatzka et al., 2015), which agrees with our results. However, the work with MDBK cells challenged with WY did not alter DGAT1 expression (Bionaz et al., 2012). Reasons for these discrepancies might be related to cell type. The decrease of TAG content with WY treatment in the present study may have been due to the downregulation of DGAT2, which is an essential enzyme catalyzing the last step of TAG synthesis (Man et al., 2006). The increase of TAG in the PPARA knockdown group was also in accordance with a previous study performed in GMEC (Chen et al., 2017).

Acyl-CoA oxidase (ACOX1), a key enzyme in LCFA peroxisomal β-oxidation, contains a PPAR response element in its promoter and can be activated by PPARA in nonruminants (Anderson, 1992). In the present study, siRNA and PPARA agonist treatment did not have significant influence on ACOX1 expression. A difference in sequence and activity of PPRE in the ACOX1 gene promoter across different species resulted in a lack of response when WY was used (Lambe et al., 1999). This result is also in accordance with a previous study in bovine (Bionaz et al., 2012). Thus, the data suggest a different regulatory mechanism for ACOX1 in the context of LCFA oxidation in ruminants.

The PPAR coactivator PGC1A is important for mitochondrial biogenesis and function in tissues such as liver, kidney, and brain in nonruminants (Mastropasqua et al., 2018). In brown fat, PGC1A “cooperates” with PPARA to regulate fatty acid catabolism genes (Villarroya et al., 2007). The abundance of these two genes in liver tissue around parturition in dairy cows follows the same expression trend (Khan et al., 2015). The contrasting responses for these genes in GMEC may be due to WY-14643 also interacting with PPARA, resulting in a decrease binding of PGC1A to PPARA. At the same time, the decrease of PGC1A mRNA abundance after activation of PPARA may help keep the balance between fatty acid synthesis and oxidation in GMEC.

At least in nonruminants, it is known that PPARA and PPARG both participate in lipid and glucose metabolism with some overlap in terms of their target genes, including the fatty acid translocase CD36 (Teboul et al., 2001; Zhou et al., 2008). Knockdown of PPARG could decrease the expression of ACACA and SCD1 (Shi et al., 2013), which is similar to what we observed when silencing PPARA. PPARD regulates the expression of the lipid droplet formation-related gene PLIN2 by binding to the PPRE site in its promoter (Shi et al., 2018). Thus, the present data are in accordance with previous reports regarding cross-talk among PPAR isoforms in the context of regulation of aspects of lipid metabolism (Xu et al., 2018). The PUFA are the natural agonists of PPARs in nonruminants (Bensinger and Tontonoz, 2008), but the activation of PPARs by PUFA requires relatively high concentrations, approximately 100 μM (Grygiel-Górniak, 2014). Further experiments will have to be conducted to verify whether under PPARA siRNA treatment, the PUFA could activate PPARG and PPARD considering its concentration. The compensatory role of PPARG and PPARD after knockdown of PPARA needs to be further confirmed. Despite that limitation, the present study suggests an important role of PPARA in lactating goat mammary gland.

In conclusion, activation of PPARA upregulates genes related to fatty acid synthesis, transport, cellular TAG synthesis, and the amounts of C16:1 and C18:1 synthesized. Thus, the results provide strong support for PPARA as a key transcription factor not only in fatty acid oxidation, but also in unsaturated fatty acid synthesis, underscoring a unique role in goat mammary epithelial cells, compared with other tissues where fatty acid oxidation is most predominant (e.g., liver). Furthermore, the effect of PPARA on fatty acid metabolism-related genes provides support for milk fat synthesis regulation networks in goat.

Glossary

Abbreviations

DMSO

dimethyl sulfoxide

GMEC

goat mammary epithelial cells

NF

normalization factor

PPAR

peroxisome proliferator-activated receptors

PPRE

PPAR response elements

siRNA

small-interfering RNA

SREBP

sterol regulatory element-binding protein

TAG

triacylglycerol

WY

WY-14643

Funding

This research was supported by the Transgenic New Species Breeding Program of China (Beijing, China; 2018ZX0800802B), the National Natural Science Foundation of China (Beijing, China; 31772575) and the Key Research and Development Plan of Shaanxi Province, China (2018ZDCXL-NY-01-05).

Conflict of interest

The authors declare no conflict of interest.

Data availability

The authors confirm that all data underlying the findings are fully available without restriction.

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Data Availability Statement

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