β'-COP Mediated Loading of PPARγ into Trophoblast-Derived Extracellular Vesicles

doi:10.21203/rs.3.rs-4680400/v1

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Research Article

β'-COP Mediated Loading of PPARγ into Trophoblast-Derived Extracellular Vesicles

https://doi.org/10.21203/rs.3.rs-4680400/v1

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Fetal growth restriction is characterized by impaired fetal growth and dysregulated lipid metabolism. Extracellular vesicles have been proved playing a crucial role in transporting biomolecules from mother to fetus. However, the mechanisms underlying cargo sorting and loading into trophoblastic extracellular vesicles remain elusive. The focus of our study is to examine how the essential fatty acid regulator, peroxisome proliferator-activated receptor gamma (PPARγ), is sorted and loaded into extracellular vesicles originating from trophoblasts. In this study, proteomic analysis was performed on placenta derived extracellular vesicles from normal and fetal growth restriction pregnancies. Interactions between PPARγ and COPI subunit were evaluated using co-immunoprecipitation and bioinformatics simulation. Molecular dynamics simulations were conducted to identify critical binding sites between β'-COP, a subunit of COPI, and PPARγ. lentivirus-mediated knockout and overexpression were employed to elucidate the role of β'-COP in PPARγ loading into extracellular vesicles. We have demonstrated that PPARγ protein levels were significantly decreased in fetal growth restriction placental extracellular vesicles. β'-COP subunit directly interacted with PPARγ in trophoblasts, mediating its sorting into early endosomes and multivesicular bodies for extracellular vesicle incorporation. Knockout of β'-COP impaired PPARγ loading into extracellular vesicles. Molecular dynamics simulations identified critical binding sites for β'-COP and PPARγ. Mutation of these sites significantly weakened the β'-COP-PPARγ interaction and reduced PPARγ levels in trophoblastic extracellular vesicles. In conclusion, β'-COP mediates sorting and loading of PPARγ into trophoblastic extracellular vesicles. This study provide insights into regulating extracellular vesicles cargo loading and potential strategies for targeted cargo delivery from maternal to fetus.

Fetal growth restriction

Extracellular vesicles

PPARγ

COPI complex

β'-COP

Fetal growth restriction (FGR) is characterized by the failure of the fetus to achieve its full growth potential during intrauterine development. FGR affects up to 15% of pregnancies globally[1], while in China, the incidence of FGR stands at 8.7%[2]. FGR fetuses are at an elevated risk of health defects, such as developmental delay, impaired development of neurological and cardiovascular system and metabolic disorders such as diabetes mellitus[3, 4]. Therefore, it is crucial to carry out in-depth studies on the pathogenesis of FGR. An important distinguishing factor between fetuses affected by FGR and those developing normally is the impaired fatty acid metabolism and lipogenesis disorder[5, 6]. FGR fetuses exhibit these disorders in fat metabolism during gestation, highlighting the importance of timely intervention in pregnancy.

Extracellular vesicles (EVs) are formed through the fusion of late endosomal multivesicular bodies (MVBs) with the plasma membrane. This fusion enables the release of intraluminal vesicles (ILVs) contained within the MVBs into the extracellular space, which gives rise to EVs[7]. The concentrations of EVs in the plasma of pregnant women exceed those in non-pregnant women by over 50-fold[8, 9]. EVs play a crucial role as mediators between the mother and fetus, aiding in the regulation of cellular metabolic homeostasis[9, 10]. In addition, EVs are proved to be capable of directly transporting lipids and regulating lipid synthesis through the enrichment of enzymes associated with lipogenesis[11–13]. Alterations in both the circulating levels and content of placental EVs during pregnancy have been correlated with a range of pregnancy complications[14]. The abundance of EVs isolated from plasma in FGR cases are shown to be decreased compared to those from normal pregnancies[15–17].

In this study, EVs were isolated from placental tissues, and proteomic analysis was conducted to discern differences in EVs between fetuses with FGR and those with normal growth. There were notable discrepancies in protein content related to lipid metabolism, particularly in the expression of peroxisome proliferator-activated receptor gamma (PPARγ), which controls the expression of genes playing key roles in the storage and mobilization of lipids[18]. PPARγ has a crucial regulatory function in placental metabolism by modulating the transfer of maternal nutrients to the fetus, enabling adjustment of fetal development to maternal nutritional conditions[18–20]. It is possible that EVs released by placental trophoblast cells play a role in lipid regulation in cases of FGR. Understanding how trophoblastic EVs sort and load their contents is crucial for elucidating the pathogenesis of FGR. Intervening in this process may present a promising strategy for regulating metabolism during pregnancy, potentially alleviating the impact of FGR on both the mother and fetus.

1 Clinical characteristics and sample collection

This research recruited women with FGR and pregnancies matched to them from the Department of Obstetrics, the First Affiliated Hospital of Chongqing Medical University. FGR is evidenced by a body weight below the 10th percentile of the growth curve specific to the population[21]. The study obtained informed consent from each participant. The sample collection procedures in the experiment were in accordance with the guidelines of the National Institutes of Health and approved by the Institutional Ethics Committee of Chongqing Medical University (No: 2020 − 788). Human specimen sampling was conducted following a previously outlined procedure, with certain modifications made[22].

2 Cell culture

The protocol followed for isolating primary CTB from the placenta was described in a previous study[23]. Human derived extravillous trophoblast cell line HTR8/SVneo was purchased from the US ATCC cell bank. The cell culture incubator was set at a temperature of 37°C and 5% CO₂. The cell culture medium consisted of RPMI 1640 (GIBCO, USA) supplemented with 10% fetal bovine serum (FBS) (GIBCO, USA) and 1% penicillin-streptomycin-gentamicin solution (Beyotime, China).

3 EVs isolation and verification

EVs were isolated from CTBs and HTR8/SVneo cells following established protocols[24–26]. The morphology of EVs was examined using transmission electron microscopy (Thermo Fisher Scientific, Glacios 2 Cryo-TEM, USA) revealing a typical double-membrane structure. The density and size of EVs were analyzed through Nanoparticle Tracking Analysis (NS300 system, NanoSight technology, Malver, UK).

4 Proteomic analysis

Following reduction and alkylation, EVs underwent enzymatic digestion with trypsin (Beyotime, China) at a mass ratio of 1:50 for 20 hours at 37°C. The resulting enzymatic products were desalted, lyophilized, redissolved in a 0.1% formic acid solution (MedChemExpress, USA), and stored at -20°C until further use. Solution A contained 0.1% formic acid in water, while Solution B comprised 0.1% formic acid in acetonitrile (MedChemExpress, USA) at an 84% concentration, serving as the organic solvent for peptide elution during chromatography. Prior to sample loading onto the Trap column from an autosampler (Thermo Fisher Scientific, Teledyne CETAC, USA), the column was equilibrated with 95% Solution A. Gradient 1H, a gradient elution program, involved a gradual increase in the proportion of Solution B in the mobile phase over time, typically utilized for the separation of complex samples. Following each full scan (MS2 scan), 20 fragment profiles were collected for the analysis of peptide and fragment mass-to-charge ratios.

5 Binding Free Energy (BFE) Analysis

The study utilized AlphaFold2 for β’-COP structure prediction and retrieved the PPARγ structure from PDB (6oni). Protonation was performed under neutral conditions using H + + 3. Protein structure refinement was carried out using PyMOL. Molecular docking was conducted with HawDOCK, which integrates homology search, template-based modeling, and docking. The HawDOCK server utilizes a hybrid algorithm for interaction prediction, employing Fast Fourier Transform-based search for global docking, followed by scoring and MMGBSA calculation. Graphical analysis was done using PyMOL and ChimeraX, while 2D interaction analysis was performed with LigPlus+.

6 Molecular Dynamics (MD) Simulation

To investigate the protein-protein interaction between PPARγ and β’-COP, simulations were performed using Gromacs 2019.6. The Amber14SB force field and TIP3P solvent model were utilized, with the proteins contained in a periodic cubic box. The box had a boundary distance of 0.1 nm and was filled with n-decane, water molecules, and counter ions for neutralization. Energy minimization was carried out using the steep algorithm for 50,000 steps, with a force convergence criterion of 1000 kcal/mol/nm. Subsequent simulations were conducted under NVT and NPT conditions for 100 ps each, at a temperature of 298 K and pressure of 1 bar. MD simulations utilized the V-rescale thermostat for temperature maintenance and the Parrinello-Rahman barostat for pressure control. Post-simulation analyses included assessing secondary structure, RMSD, RMSF, Rg and SASA.

7 Mutation analysis

Bioinformatics was used to simulate the mutation of ASP258, LYS273, GLN281, GLY282, PHE285, and ARG286 in the PPARγ protein with alanine. A comprehensive conservation analysis and 3D modeling using AlphaFold DB were conducted to analyze the spatial arrangement of amino acid residues and visualize potential structural changes with PyMOL software.

8 Immunofluorescence

The samples were fixed with 4% PFA and permeabilized with 0.5% Triton X-100. Sections were blocked with 5% BSA, incubated with primary antibodies overnight at 4°C. Fluorescently labeled secondary antibodies were applied, followed by DAPI staining for nuclei. Slides were sealed with a fluorescence quencher before imaging with a fluorescence microscope to analyze protein localization and expression. The primary antibodies applied in immunofluorescence is available in supplement Table 1.

Table 1

PPARγ expression in human normal term placenta derived exosomes
Accession	Description	Score	Coverage	Proteins	Unique Peptides	Peptides	PSMs	AAs	MW [kDa]	calc.pl
D2KUA6	HUMAN Peroxisome proliferator-activated receptor gamma OS = Homo sapiens OX = 9606 GN = PPARG PE = 2 SV = 1	51.93	54.92	1	1	15	37	1906	305.4	8.27
Q53EW1	HUMAN Peroxisome proliferator-activated receptor gamma (Fragment) OS = Homo sapiens OX = 9606 GN = PPARG PE = 2 SV = 1	67.88	35.89	1	1	23	15	1530	226.5	6.91
E9PFV2	HUMAN Peroxisome proliferator-activated receptor gamma OS = Homo sapiens OX = 9606 GN = PPARG PE = 1 SV = 1	39.41	42.84	1	1	11	26	1282	309.0	6.29
A0A0S2Z 4K5	HUMAN Peroxisome proliferator-activated receptor gamma (Fragment) OS = Homo sapiens OX = 9606 GN = PPARG PE = 2 SV = 1	48.12	29.63	1	1	14	19	1711	257.2	7.21
Q86WD1	HUMAN Peroxisome proliferator-activated receptor gamma OS = Homo sapiens OX = 9606 GN = PPARG PE = 2 SV = 1	49.09	69.21	1	1	22	17	2074	397.7	8.47
P37231	HUMAN Peroxisome proliferator-activated receptor gamma OS = Homo sapiens OX = 9606 GN = PPARG PE = 1 SV = 3	30.51	30.31	1	1	16	25	2108	334.5	7.12
PSMs: Peptide-Spectrum Matches; AAs: Amino Acid Count; MW: Molecular weight; OS: Organism; OX: Organism taxonomy; GN: Gene name; PE: Protein existence; SV: Sequence version.

9 Western Blot

Western Blot analysis was conducted following established protocols (Abcam, UK). Protein isolation was performed through electrophoresis, running at 100V for 30 minutes followed by 150V for 1 hour. The proteins were transferred onto a polyvinylidene fluoride membrane at 4°C and a constant current of 200mA for 2 hours. Finally, the membrane was imaged using a ChemiDoc XRS + Gel Imaging System and analyzed with Image Lab 5.2 software. The information about antibodies utilized in Western Blot is available in the supplement Table 2.

Table 2

PPARγ expression in human FGR term placenta derived exosomes
Accession	Description	Score	Coverage	Proteins	Unique Peptides	Peptides	PSMs	AAs	MW [kDa]	calc.pl
D2KUA6	HUMAN Peroxisome proliferator-activated receptor gamma OS = Homo sapiens OX = 9606 GN = PPARG PE = 2 SV = 1	60.83	40.53	1	1	23	35	1816	335.1	5.22
Q53EW1	HUMAN Peroxisome proliferator-activated receptor gamma (Fragment) OS = Homo sapiens OX = 9606 GN = PPARG PE = 2 SV = 1	48.43	59.32	1	1	14	25	2560	204.5	3.41
E9PFV2	HUMAN Peroxisome proliferator-activated receptor gamma OS = Homo sapiens OX = 9606 GN = PPARG PE = 1 SV = 1	48.32	48.34	1	1	21	27	3282	314.0	5.21
A0A0S2Z 4K5	HUMAN Peroxisome proliferator-activated receptor gamma (Fragment) OS = Homo sapiens OX = 9606 GN = PPARG PE = 2 SV = 1	50.75	57.78	1	1	17	29	2711	239.2	5.22
Q86WD1	HUMAN Peroxisome proliferator-activated receptor gamma OS = Homo sapiens OX = 9606 GN = PPARG PE = 2 SV = 1	69.22	49.59	1	1	16	27	2944	339.1	6.77
P37231	HUMAN Peroxisome proliferator-activated receptor gamma OS = Homo sapiens OX = 9606 GN = PPARG PE = 1 SV = 3	58.85	53.39	1	1	26	22	1908	349.1	8.22
PSMs: Peptide-Spectrum Matches; AAs: Amino Acid Count; MW: Molecular weight; OS: Organism; OX: Organism taxonomy; GN: Gene name; PE: Protein existence; SV: Sequence version.

10 RT-qPCR

RNA extraction form HTR8/SVneo cells was performed followed RNA extraction protocol (Thermo Fisher Scientific, USA). RT-qPCR was performed followed the instructions of RT-qPCR kit (Vazyme, China). The primers applied in this study is listed in the supplement Table 3.

Table 3

COPI complex subunits expression in human normal term placenta derived exosomes
Accession	Description	Score	Coverage	Proteins	Unique Peptides	Peptides	PSMs	AAs	MW [kDa]	calc.pl
P35606	Coatomer subunit beta'OS = Homo sapiens GN = COPB2 PE = 1 SV = 2	41.33	29.14	1	5	20	27	906	102.4	5.27
P48444	Coatomer subunit delta OS = Homo sapiens GN = ARCN1 PE = 1 SV = 1	14.92	24.85	1	2	9	10	511	57.2	6.21
Q9Y678	Coatomer subunit gamma-1 OS = Homo sapiens GN = COPG1 PE = 1 SV = 1	9.79	12.59	1	1	7	7	874	97.7	5.47
O14579	Coatomer subunit epsilon OS = Homo sapiens GN = COPE PE = 1 SV = 3	7.57	20.78	1	2	4	5	308	34.5	5.12
PSMs: Peptide-Spectrum Matches; AAs: Amino Acid Count; MW: Molecular weight; OS: Organism; OX: Organism taxonomy; GN: Gene name; PE: Protein existence; SV: Sequence version.

11 Co-immunoprecipitation (Co-IP) assay

The Co-IP of PPARγ and subunits of COPI was conducted utilizing a Pierce Crosslink Magnetic IP/Co-IP Kit (USA) following the manufacturer's instructions. The information about antibodies utilized in Co-IP is available in the supplement Table 4.

Table 4

COPI complex subunits expression in human FGR term placenta derived exosomes
Accession	Description	Score	Coverage	Proteins	Unique Peptides	Peptides	PSMs	AAs	MW [kDa]	calc.pl
P35606	Coatomer subunit beta'OS = Homo sapiens GN = COPB2 PE = 1 SV = 2	59.72	39.29	1	12	26	37	906	102.4	5.27
P53621	Coatomer subunit alpha OS = Homo sapiens GN = COPA PE = 1 SV = 2	8.00	8.33	1	1	8	9	1224	138.3	7.66
P48444	Coatomer subunit delta OS = Homo sapiens GN = ARCN1 PE = 1 SV = 1	5.38	12.72	1	1	6	6	511	57.2	6.21
O14579	Coatomer subunit epsilon OS = Homo sapiens GN = COPE PE = 1 SV = 3	3.51	4.55	1	1	1	1	308	34.5	5.12
PSMs: Peptide-Spectrum Matches; AAs: Amino Acid Count; MW: Molecular weight; OS: Organism; OX: Organism taxonomy; GN: Gene name; PE: Protein existence; SV: Sequence version.

12 Virus transfection

Virus transfection was conducted according to the instructions of plasmid virus (OBiO, China). The β’-COP/α-COP/δ-COP-knockout plasmid or PPARγ overexpression plasmid viruses were introduced to the cells at MOI = 30 or MOI = 10. Following a 24-hour infection period, the virus-containing medium was replaced with fresh complete medium. Subsequently, the cells were treated with puromycin (Beyotime, China) at a concentration of 2 µg/ml for 48 hours to establish stable transfected cells.

13 Statistical analyses

The unpaired two-tailed Student’s t-test was employed for comparing two groups, while the nonparametric one-way ANOVA was utilized for analyzing differences among multiple groups using GraphPad Prism 9.0. The mean ± SD is used to present all results. The number of independent experiments carried out for each analysis is indicated in the figure legends. P < 0.05 was used to define significant differences. Each experiment was conducted at least twice.

PPARγ protein expression in FGR placental derived EVs is impaired.

To explore the difference in human placental derived EVs between normal and FGR pregnancy, the EVs were firstly isolated from normal and FGR term placenta via sucrose gradient centrifugation. Electron microscopy showed that exosomes have a typical double-membrane structure (Fig. S1A). Western blot showed that EVs highly express their biomarkers, such as CD9, CD81, CD63 and HSP90β, while barely express their negative marker Tubulin (Fig. S1B). Moreover, we ascertained that the size of the extracted EVs from normal and FGR term placenta was primarily within the 80 to 130 nm range, as measured by laser scattering using the NanoSight tracking system (NTA, Fig. S1C). To identify the proteins enriched in human normal and FGR term placental derived EVs, we conducted proteomic analysis of EVs derived from human normal and FGR term placenta using GC-MS. Wayne's analysis revealed that 634 proteins were commonly expressed in both normal and FGR placental derived EVs (Fig. 1A). The GO-molecular function analysis of the commonly expressed proteins in normal and FGR term placental derived EVs indicated a predominant focus on protein binding and fatty acid metabolism (Fig. 1B). In our analysis of fatty acid metabolism-related proteins, we identified the PPARγ protein, which demonstrates a high expression abundance and plays a crucial role in adipogenesis (Table 1–2).

To confirm the expression of PPARγ in the human placenta, primary cytotrophoblasts (CTBs) were isolated from normal and FGR placenta tissues in late pregnancy using percoll gradient centrifugation. The purity of the extracted CTBs was validated through an immunofluorescence assay, demonstrating high levels of epithelial cell markers CK7 and E-cadherin (98.09 ± 0.56% and 97.89 ± 0.65% of the cell population, respectively), with minimal expression of the mesenchymal cell marker vimentin (1.85 ± 0.61%) (Fig. 1C). Subsequently, EVs were isolated from primary CTBs (Fig. S1D-F, supplement Table 5). Western blot results showed that the expression level of PPARγ protein in EVs of FGR derived CTBs and FGR CTBs was both significantly lower than that in the normal group (Fig. 1D and E), which is consistent with the lipogenesis disorder in FGR fetuses reported in previous study[5, 6]. Furthermore, a notable decrease in PPARγ protein expression was observed in EVs derived from FGR placental tissues compared to the normal group (Fig. 1F).

Table 5

Analysis of the free energy of binding between PPARγ and β’-COP
Energy component	Average	SD (Prop.)	SD	SEM (Prop.)	SEM
ΔBOND	0.00	18.51	0.00	0.19	0.00
ΔANGLE	0.00	26.80	0.00	0.27	0.00
ΔDIHED	0.00	14.86	0.00	0.15	0.00
ΔVDWAALS	-31.60	24.20	5.52	0.24	0.06
ΔEEL	25.92	8.14	7.59	0.08	0.08
Δ1–4 VDW	0.00	10.03	0.00	0.10	0.00
Δ1–4 EEL	0.00	3.58	0.00	0.04	0.00
ΔEGB	-26.99	6.99	7.00	0.07	0.07
ΔESURF	-5.88	1.47	0.84	0.01	0.01
ΔGGAS	-5.69	25.53	10.10	0.26	0.10
ΔGSOLV	-32.87	7.15	6.53	0.07	0.07
ΔTOTAL	-38.55	26.51	5.77	0.27	0.06
SD: Standard Deviation; SEM: Standard Error of Mean; Prop.: Property; BOND: Bond Stretch Energy; ANGLE: Angle Bending Energy; DIHED: Dihedral Angle Energy; VDWAALS: van der Waals (vdW) Non-bonded Energy; EEL: Electrostatic (Coulombic) Energy between Charged Groups; VDW: van der Waals; EGB: Generalized Born Model for Solvation Energy; ESURF: Surface Area Term in Solvation Energy Calculation; GGAS: Gas Phase Energy Contribution to the Total Energy; GSOLV: Solvent Accessible Surface Area Energy; TOTAL: Total Energy Calculation.

COPI coat complex subunit proteins were enriched in both normal and FGR trophoblast derived EVs.

As PPARγ is found within EVs, we investigated the mechanism by which PPARγ protein enters EVs in trophoblasts. Through proteomic analysis of EVs from normal and FGR term placenta, we identified proteins associated with intracellular protein transport, including COPB2 (β’-COP), ARCN1 (δ-COP), COPG1 (γ-COP), COPA (α-COP) and COPE (ε-COP), which are part of the COPI coat complex (Table 3–4). Western Blot analysis showed that both normal and FGR term placenta and placental-derived EVs express all COPI complex subunits (Fig. 1G-H). There was no significant difference in their expression levels between the two groups, suggesting that the transportation of PPARγ into EVs may be linked to the COPI coat complex.

β’-COP interacts directly with PPARγ in trophoblast starting from the early stage of EVs formation.

To investigate the role of the COPI complex in sorting PPARγ into EVs, a Co-immunoprecipitation (Co-IP) assay was conducted to confirm the interaction between COPI complex subunits and PPARγ. The interaction of each COPI complex subunit with PPARγ was individually examined in primary CTBs from both normal and FGR placenta. The results revealed a direct interaction between PPARγ and the β’-COP subunit in primary CTBs, with no involvement of other subunits (Fig. 2A-G). This association was further supported by reciprocal immunoprecipitation of COPI complex subunits with the PPARγ antibody (Fig. 2H). Immunofluorescence staining demonstrated co-localization of β’-COP and PPARγ in human placenta tissues and primary CTBs from both normal and FGR groups (Fig. 2I), providing spatial evidence of their binding. These findings indicate a specific interaction between β’-COP and PPARγ in human trophoblast cells.

Studies have shown that EV formation begins with the transition from early endosomes to late endosomes and multivesicular endosomes (LE/MVEs), culminating in EV production[27]. To investigate the stage at which β’-COP and PPARγ interact during EV formation, we performed an immunofluorescence assay to assess their co-localization with the early endosome marker EEA1 and the LE/MVE marker CD63 in HTR8/SVneo trophoblast cells. The assay confirmed the co-localization of PPARγ and β’-COP with EEA1 and CD63 (Fig. 2J-K). Furthermore, a Co-IP assay demonstrated a direct interaction between PPARγ and β’-COP in trophoblast cells (Fig. 2L). These results suggest that the interaction between PPARγ and β’-COP occurs early in EV formation. However, no co-localization was observed with LAMP1, a lysosomal marker, or LC3 puncta, an indicator of autophagosome, indicating that PPARγ and β’-COP specifically localize to early endosomes and MVEs rather than lysosomes or autophagosomes (Fig. 2M-N). In conclusion, β’-COP directly binds to PPARγ in trophoblast cells, influencing PPARγ localization to early endosomes and MVEs during EV formation.

β’-COP mediates ab initio loading of PPARγ into t-Exos.

Subsequently, we investigated the role of β’-COP in loading PPARγ into EVs. Through lentiviral transfection, we successfully generated a β’-COP knockout (β’-COP-KO) HTR8/SVneo trophoblast cell line (Fig. 3A-C). The EVs derived from HTR8/SVneo were also characterized (Fig. S1G-I, supplement Table 5). However, immunofluorescence staining revealed the lack of co-localization between PPARγ with EEA1and CD63 following β’-COP knockout (Fig. 3D-E). Western Blot analysis demonstrated a significant reduction in PPARγ protein expression in trophoblast EVs in the β’-COP-KO group compared to the control group (Fig. 3F). These findings suggest that β’-COP facilitates the transport of PPARγ into EVs during the early stages of EV formation.

β’-COP mediated transportation of PPARγ into EVs relies on intact COPI complex.

To investigate the relationship between β’-COP mediated loading of PPARγ into EVs and the COPI complex, we constructed δ-COP knockout (δ-COP-KO) and ε-COP knockout (ε-COP-KO) HTR8/SVneo trophoblast cell lines by lentiviral transfection (Fig. 3G-L). Co-IP assays indicated that the knockout of δ-COP or ε-COP could significantly interfere with the interaction of PPARγ to β’-COP (Fig. 3M-N). Importantly, the deficiency of δ-COP or ε-COP can also result in an inhibition in the EVs transportation of PPARγ in trophoblast which manifested as declined co-localization of PPARγ with EEA1 and CD63, as well as reduced PPARγ protein expression in δ-COP-KO and ε-COP-KO HTR8/SVneo trophoblast (Fig. 3O-S). These findings demonstrated the essential role of intact COPI complex in the loading of PPARγ proteins into EVs.

The binding of PPARγ to β’-COP depends on solvation free energy and van der Waals forces.

To investigate the binding modes between PPARγ and β’-COP, we conducted molecular dynamics simulations using Gromacs 2019.6 software. The root mean square deviation (RMSD) analysis revealed that equilibrium in the β’-COP-PPARγ binding was reached after 20 ns of simulation (Fig. 4A), with the RMSD value stabilizing around 1.0 nm. Further assessment of the stability of the complex was performed through radius of gyration (Rg) and solvent accessible surface area (SASA) analysis (Fig. 4B-C). The Rg fluctuated within 4.7 nm post 20 ns of simulation, indicating sustained stability of the β’-COP-PPARγ binding complex (Fig. 4B). Additionally, the SASA of the complex remained constant throughout the simulation (after 20 ns) (Fig. 4C), suggesting persistent binding of PPARγ to the β’-COP surface (Fig. 4D-E).

To investigate the binding force between β’-COP and PPARγ, we conducted simulations to analyze hydrogen bond distribution during the binding process (Fig. 4F). The results show that β’-COP-PPARγ typically forms 7–8 hydrogen bonds over the simulation period of 0-100ns, indicating stable binding of PPARγ to the β’-COP protein surface (Fig. 4F-H). Upon further analysis, the binding free energy (BFE) between PPARγ and β’-COP was determined to be -38.55 kcal/mol, primarily driven by solvation free energy (ΔGSOLV, -32.87 kcal/mol) and Vander Waals forces (ΔVDWAALS, -31.60 kcal/mol) (Table 5). The Coulomb force was found to be the main repulsive force (ΔEEL, 25.92 kcal/mol) (Fig. 4I, Table 5). Decomposition of the BFE of the β’-COP-PPARγ complex into residues highlighted key amino acids crucial for their interaction (Fig. 4J-K). Nine of these amino acids were identified as hot spots with a binding energy less than − 1 kcal/mol, namely GLU175, HIS178, ASP203, ARG204, ASP207, ASN238, and ARG244 for β’-COP, and LYS273, GYN281, and PHE285 for PPARγ (Fig. 4J-K). This analysis and subsequent bioinformatics investigation identified the binding domains of PPARγ and β’-COP to be located between amino acid residues 258–286 on PPARγ.

ASP258, LYS273, GLN281, GLY282, PHE285 and ARG286 of PPARγ protein were the key binding sites of β’-COP-PPARγ complex.

In order to identify the essential amino acid binding sites for the PPARγ-β’-COP complex within the 258–286 binding domains, an analysis was conducted to assess the binding energies contributed by amino acid sites within this domain. Through mutation of all amino acids in the binding domain to alanine, we observed a significant impact on the BFE at positions ASP258, ILE279, PHE280, GLY282, PHE285, and ARG286 (Table 6). Additionally, we investigated the effect of amino acid residue mutation on protein stability and BFE using ΔΔG, revealing that mutations at ASP258, LYS273, GLN281, GLY282, GLN284, PHE285, and ARG286 significantly influence the binding stability of the β’-COP-PPARγ complex (Table 7). Overall, our findings highlight the critical role of ASP258, LYS273, GLN281, GLY282, PHE285, and ARG286 in facilitating the transport of PPARγ into EVs mediated by β’-COP.

Table 6

Analysis of the BFE between PPARγ and β’-COP after amino acid sites mutation
Amino acid site	Amino acid	BFE before mutation	BFE after mutation
258	ASP	-0.752	2.796
273	LYS	-3.436	-1.673
274	GLU	-0.390	-0.334
275	VAL	-0.288	0.0822
276	ALA	-0.031	0
277	ILE	-0.360	0.663
278	ARG	-0.438	-0.470
279	ILE	-0.038	2.434
280	PHE	-0.063	3.369
281	CYN	-1.000	-0.293
282	GLY	-0.542	4.934
283	GYS	-0.140	-1.288
284	GLN	-0.860	-1.406
285	PHE	-5.400	1.964
286	ARG	-0.949	2.284
BFE: Binding free energy.

Table 7

ΔΔG analysis of the BFE between PPARγ and β’-COP after amino acid sites mutation
Amino acid site	Amino acid	ΔΔG(kcal/mol)
258	ASP	1.79
273	LYS	1
281	CYN	1.03
282	GLY	1.06–1.31
284	GLN	1.04
285	PHE	1.23–3.31
286	ARG	1.0-1.1
BFE: Binding free energy

The mutation of ASP258, LYS273, GLN281, GLY282, PHE285 and ARG286 of PPARγ weaken the binding between PPARγ and β’-COP.

To elucidate the functions of the six amino acids in the β’-COP-PPARγ interaction, we employed bioinformatics to simulate the mutation of these six amino acids in the PPARγ protein to alanine. The stability of the system was assessed using RMSD, revealing initial stability from 0 to 30 ns, followed by significant fluctuations after 35 ns (Fig. 5A). These fluctuations were attributed to the impact of mutations on system stability, leading to partial dissociation and substantial RMSD fluctuations. Similar trends were observed for Rg and SASA, with Rg increasing significantly after 35 ns and SASA showing an increasing trend after 30 ns, indicating system instability post-mutation (Fig. 5B-C). Analysis of hydrogen bond distribution revealed a gradual decrease in the number of hydrogen bonds from 8–10 at the start of the simulation to approximately 1–2 by the end (Fig. 5D-H), aligning with the dissociation process observed in RMSD, Rg, and SASA.

We investigated the impact of a PPARγ mutation on BFE of PPARγ and β’-COP. The BFE was − 31.11 kcal/mol, primarily influenced by solvation free energy (ΔGSOLV, -42.63 kcal/mol) and van der Waals force (ΔVDWAALS, -26.42 kcal/mol), with Coulomb force (ΔEEL, 37.94 kcal/mol) as the main repulsive force (Table 8, Fig. 5I). The BFE initially increased within the first 20ns, followed by a subsequent decrease to a minimum of approximately − 8 kcal/mol at 42 ns. It then rose again, stabilizing at around − 32 kcal/mol (Fig. 5J), weaker than the BFE of the unmutated β’-COP-PPARγ complex (-38.55 kJ/mol). Key amino acids responsible for the bonding between the β’-COP-PPARγ complex were also altered and reduced (Fig. 5K-L). The mutation led to a significant decrease in the BFE between PPARγ and β’-COP, indicating a weakening of their interaction compared to the pre-mutation state.

Table 8

Analysis of the free energy of binding between PPARγ and β’-COP after mutation
Energy component	Average	SD	SEM
VDWAALS	-26.42	10.2	0.14
EEL	37.94	5.56	0.08
EGB	-38.14	5.01	0.07
ESURF	-4.49	1.82	0.03
GGAS	11.52	13.43	0.19
GSOLV	-42.63	4.24	0.06
TOTAL	-31.11	11.78	0.17
SD: Standard Deviation; SEM: Standard Error of Mean; VDWAALS: van der Waals (vdW) Non-bonded Energy; EEL: Electrostatic (Coulombic) Energy between Charged Groups; EGB: Generalized Born Model for Solvation Energy; ESURF: Surface Area Term in Solvation Energy Calculation; GGAS: Gas Phase Energy Contribution to the Total Energy; GSOLV: Solvent Accessible Surface Area Energy; TOTAL: Total Energy Calculation.

The mutation of ASP258, LYS273, GLN281, GLY282, PHE285 and ARG286 of PPARγ resulted in decreased expression of PPARγ in EVs.

To verify the participation of the six amino acids in β’-COP-mediated EVs transportation, we utilized overexpression lentivirus to introduce mutated PPARγ with these residues substituted with alanine to HTR8/SVneo trophoblast cell line. Concurrently, the fluorescent protein mCherry was incorporated upstream of the PPARγ protein, and a Histag label protein was attached downstream, resulting in a labeled PPARγ ASP258, LYS273, GLN281, GLY282, PHE285 and ARG286 mutated HTR8/SVneo trophoblast cell line (PPARγ^M-OE HTR8/SVneo) (Fig. 6A). The mCherry-PPARγ-histag overexpressed HTR8/SVneo (PPARγ-OE HTR8/SVneo) were used as a control group (Fig. 6A).

Confocal microscopy reveals the high transfection efficiency of the overexpressed letivirus in HTR8/SVneo trophoblast cells (Fig. 6B). Additionally, Western Blot and RT-qPCR analysis revealed a significantly overexpression of mCherry, PPARγ and histag both in PPARγ^M-OE and PPARγ-OE HTR8/SVneo cells (Fig. 6C-D). After mutation, the immunofluorescence assay revealed a significantly reduction in the co-localization of mCherry fluorescent protein with the EEA1 and CD63 in the PPARγ^M-OE HTR8/SVneo group (Fig. 6E-F). In contrast, the PPARγ-OE HTR8/SVneo group exhibited colocalization of mCherry protein with EEA1 and CD63 (Fig. 6E-F). Western Blot analysis also revealed a significantly reduction of PPARγ expression level in the EVs of PPARγ^M-OE HTR8/SVneo group compared to the PPARγ-OE HTR8/SVneo group (Fig. 6G). Taken together, these findings indicate that ASP258, LYS273, GLN281, GLY282, PHE285 and ARG286 of PPARγ are crucial amino acid sites for the β’-COP-mediated transportation of PPARγ into EVs.

In this study, we have elucidated the sorting and loading mechanism of PPARγ which mediated by β’-COP subunit of COPI complex in trophoblastic EVs. In addition, we have proved that the expression of PPARγ was significantly diminished in FGR placental derived EVs compared to the control group. By employing bioinformatics prediction techniques and Co-IP assay for protein interaction exploration, we have evidenced a direct interaction between the β’-COP and PPARγ in human placenta and trophoblasts, which facilitates the recruitment of PPARγ by trophoblastic EVs. Additionally, amino acid mutations of PPARγ demonstrated that the interaction between PPARγ protein and β'-COP predominantly hinges on ASP258, LYS273, GLN281, GLY282, PHE285 and ARG286 amino acid residues.

ILV formation and cargo sorting in placental trophoblast cells involve the recruitment of endosomal sorting complex required for transport (ESCRT) subcomplexes[28, 29]. Recent studies suggest the importance of ESCRT-independent mechanisms, such as lipid raft[30], RAB31[31], LAMP2A[32], and ceramides[33]. The COPI vesicular coat plays a crucial role in retrograde trafficking from the cis-Golgi to the endoplasmic reticulum and intra-Golgi transport[34]. Cargo incorporation into COPI vesicles is mediated by ADP-ribosylation factor hydrolysis of GTP[35]. Additionally, COPI binds to cargo through recognition of arginine-based signaling motifs with the RXR sequence by β’-COP and δ-COP subunits[36], as confirmed in our finding. However, the mechanism of COPI in EV sorting and loading, as well as its interaction with PPARγ, remains understudied. Our research highlights the involvement of COPI in sorting and loading PPARγ, a critical protein in fatty acid metabolism, into EVs of placental trophoblast cells.

The utilization of nanoparticle drugs shows promise for targeted drug therapy aimed at the placenta[37], but limitations exist in their drug delivery potential and the range of materials they can encapsulate. EVs play a crucial role in intercellular communication, offering a mechanism for delivering biological cargo without immune rejection[27]. EVs, with their complex lipid and surface membrane protein composition, effectively target specific tissues, reducing potential drug-related adverse effects[27, 38, 39]. The size distribution variability among EV subpopulations provides options for drug delivery. Challenges remain in standardizing EV production processes and scaling up production[40], as well as addressing biological variability among different sources and types of EVs[41]. Hence, investigating the mechanisms of content sorting and loading by EVs can lay the foundation for regulating EV cargo sorting and advancing extracellular protein encapsulation technologies.

While our study shows promising results on protein sorting and loading into trophoblastic EVs, it is important to note its limitations. Our focus has been on FGR lipid metabolism, but further research on COPI transport for other proteins is needed. Moreover, this study only examined amino acid sites on PPARγ, while crucial binding sites on β'-COP were not investigated. Investigating the key binding site of β'-COP is essential for understanding how molecules are transported into EVs through the COPI complex.

To summarize, this study delves into the molecular mechanism of PPARγ transportation into EVs via β'-COP, offering potential strategies for regulating EVs cargo loading and innovative approaches for drug delivery to the placenta.

Acknowledgements:

We feel grateful for the patients’ understanding and cooperation in donating the clinical samples for research purposes. We would like to extend our gratitude to the General Program of National Natural Science Foundation of China (82171662, 82001581, 82301934), China Postdoctoral Science Foundation (2022M710560), Chongqing Science and Technology Commission (cstc2021jcyj-msxmX0754), Natural Science Foundation of Chongqing, China (CSTB2022NSCQ-MSX0122), Joint Medical Research Program of Chongqing Municipal Health Commission and Chongqing Science and Technology Bureau (2022QNXM057), Program for Youth Innovation in Future Medicine, Chongqing Medical University (W0130). The first affiliated hospital of Chongqing medical university (CYYY-BSHPYXM-202212, PYJJ2021-09).

Statements & Declarations

Funding

This work was supported by General Program of National Natural Science Foundation of China (82171662, 82001581, 82301934), China Postdoctoral Science Foundation (2022M710560), Chongqing Science and Technology Commission (cstc2021jcyj-msxmX0754), Natural Science Foundation of Chongqing, China (CSTB2022NSCQ-MSX0122), Joint Medical Research Program of Chongqing Municipal Health Commission and Chongqing Science and Technology Bureau (2022QNXM057), Program for Youth Innovation in Future Medicine, Chongqing Medical University (W0130). The first affiliated hospital of Chongqing medical university (CYYY-BSHPYXM-202212, PYJJ2021-09).

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

The experiments were designed and performed by Xiaofang Luo. Funding support was provided by Hao Wang and Hongbo Qi. The research and data collection were participated in by Xiaofang Luo, Biyang Yin, and Hao Wang. The data analysis was conducted by Biyang Yin, Biao Huang, and Jinfeng Cao. The sample collection was assisted by Biyang Yin and Jinfeng Cao. Technical support, study design, and paper editing were provided by Hao Wang, Biao Huang, and Jinfeng Cao. The overall study design was supervised by Hongbo Qi. The original draft writing was written by Xiaofang Luo. The manuscript was revised with the help of Hongbo Qi and Hao Wang. All authors read and approved the final manuscript.

Data Availability

No new data were created or analyzed in this study.

Ethics approval

The sample collection procedures in the experiment were in accordance with the guidelines of the National Institutes of Health. Approval was granted by the Institutional Ethics Committee of Chongqing Medical University (No: 2020-788).

Consent to participate

Informed consent was obtained from all individual participants included in the study.

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Abbreviations: FGR: Fetal Growth Restriction; EVs: Extracellular Vesicles; MVBs: Multivesicular Bodies; ILVs: Intraluminal Vesicles; PPARγ: Peroxisome Proliferator-Activated Receptor Gamma; CTB: Cytotrophoblast; FBS: Fetal Bovine Serum; HTR8/SVneo: Human derived extravillous trophoblast cell line; MD: Molecular Dynamics; Rg: Radius of Gyration; RMSD: Root Mean Square Deviation; RMSF: Root Mean Square Fluctuation; SASA: Solvent Accessible Surface Area; WT: Wild-Type; CK7: Cytokeratin 7; HSP90β: Heat Shock Protein 90 Beta; GC-MS: Gas Chromatography-Mass Spectrometry; GO: Gene Ontology; Co-IP: Co-immunoprecipitation; LE/MVEs: Late Endosomes/Multivesicular Endosomes; BFE: Binding Free Energy; ESCRT: Endosomal Sorting Complex Required for Transport; RAB31: Ras-related protein Rab-31; LAMP2A: Lysosome-associated membrane protein 2A; GTP: Guanosine Triphosphate; RXR: Retinoid X Receptor

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β'-COP Mediated Loading of PPARγ into Trophoblast-Derived Extracellular Vesicles

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