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. 2023 Jan 14;11(1):214.
doi: 10.3390/biomedicines11010214.

TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes

Leonie Schumacher  1 , Rédouane Slimani  2   3 , Laimdota Zizmare  4 , Jakob Ehlers  1 , Felix Kleine Borgmann  2   3 , Julia C Fitzgerald  5 , Petra Fallier-Becker  6 , Anja Beckmann  7 , Alexander Grißmer  7 , Carola Meier  7 , Ali El-Ayoubi  1 , Kavi Devraj  8   9 , Michel Mittelbronn  2   3   10   11   12   13 , Christoph Trautwein  4 , Ulrike Naumann  1
Affiliations

TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes

Leonie Schumacher et al. Biomedicines. .

Abstract

The blood-brain barrier (BBB) is a selectively permeable boundary that separates the circulating blood from the extracellular fluid of the brain and is an essential component for brain homeostasis. In glioblastoma (GBM), the BBB of peritumoral vessels is often disrupted. Pericytes, being important to maintaining BBB integrity, can be functionally modified by GBM cells which induce proliferation and cell motility via the TGF-β-mediated induction of central epithelial to mesenchymal transition (EMT) factors. We demonstrate that pericytes strengthen the integrity of the BBB in primary endothelial cell/pericyte co-cultures as an in vitro BBB model, using TEER measurement of the barrier integrity. In contrast, this effect was abrogated by TGF-β or conditioned medium from TGF-β secreting GBM cells, leading to the disruption of a so far intact and tight BBB. TGF-β notably changed the metabolic behavior of pericytes, by shutting down the TCA cycle, driving energy generation from oxidative phosphorylation towards glycolysis, and by modulating pathways that are necessary for the biosynthesis of molecules used for proliferation and cell division. Combined metabolomic and transcriptomic analyses further underscored that the observed functional and metabolic changes of TGF-β-treated pericytes are closely connected with their role as important supporting cells during angiogenic processes.

Keywords: blood–brain barrier; glioblastoma; metabolomics; transforming growth factor beta.

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Conflict of interest statement

C.T. reports a research grant by Bruker BioSpin GmbH, Ettlingen, Germany. All other authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The in vitro blood–brain barrier (BBB) model. (A) Scheme of porcine brain microvascular endothelial cells (PBMVEC), human brain microvascular pericytes (HBVP) and SV40 large T-antigene immortalized astrocytic cells (SV-GA) or T98G glioblastoma cell line localization in the BBB co-culture model co-culture. (B) CD31, claudin 5 and ZO-1 staining of isolated PBMVEC (bars = 10 µm). (C) The cells were cultivated as described in the material and methods part either as monocultures (HBVP on the bottom insert membrane; PBMVEC on the top insert membrane) or as co-cultures. After 5 days, the membrane integrity was determined by TEER measurement (n = 3, each up to 10 replicates, SEM, t-test, **** p < 0.0001). (D) Co-culture of barrier-tight BBB-layers (containing PBMVEC and HBVP) with SV-GA or T98G cells. TEER was performed 24 h after co-culture (Co: no additional cells were seeded; n > 7, SEM, t-test, *** p < 0.005). Co-culture with astrocytic cells strengthens, with glioma cells weakens barrier integrity. (C,D) Circles, squares and triangles at the appropriate bars indicate data points of single experiments.
Figure 2
Figure 2
TGF-β negatively modulates the integrity of the BBB in vitro. (A) TGF-β negatively modulates the integrity of the barrier. Addition of TGF-β1 plus -β2 (each 5 ng/mL; grey bar) into the bottom chamber (HBVP cell membrane side) of barrier-tight BBB layers. Twenty-four hours later, the integrity of the barrier was determined by TEER (Co: sham treatment; black bar). n = 4, each 2 technical replicates, SEM, t-test, ** p < 0.01). (B) The cells were treated with TGF-β1 plus -β2 [each 3 ng/mL (3+3) or 6 ng/mL (6+6)] in the absence or presence of the pan-TGF-β neutralizing antibody 1D11 (1 µg/mL). Addition of 1D11 reverted the TGF-β mediated reduction in BBB integrity (Co: sham treatment, n = 2, each one replicate, SD). (C) Intact barrier-dense BBB layers were transferred into new wells containing either no cells (black bars) or T98G cells (green bars) as well as no antibody (-), 1D11 antibody, or an isotype control (IgG, each 1 µg/mL). Twenty-four hours later, TEER measurement was performed (n = 3, each 2 replicates, SEM, t-test). (A–C) Circles, triangles and squares at the appropriate bars present the data points of single experiments. (D) PBMVEC monoculture and PMBVEC/HBVP co-culture membranes were treated with TGF-β1 plus -β2 (each 5 ng/mL) 48 h after seeding. TEER measurement was performed as described in the Methods section (one out of three independent experiments is shown, light-colored lines indicate the SD of technical replicates). (E) Differences in the reduction in TEER in PBMVEC mono- and PBMVEC/HBVP co-cultures after addition of TGF-β1 plus -β2 (each 5 ng/mL; n = 3, SD, p = 0.18).
Figure 3
Figure 3
TGF-β treatment of HBVP reduced the amount and complexity of TJ in endothelial cells. (A) TGF-β1 plus -β2 (each 10 ng/mL) were added into the bottom chamber of wells containing intact BBB layers. Twenty-four hours later, PBMVEC growing on the upper part of the membrane were collected and prepared for freeze fraction electron microscopy. Red stars exemplarily indicate TJ branches. Exemplarily, one photograph is shown. (B) TJs were manually labeled in different visual fields (n = 2, in total 4 replicates). (C,D) Quantification of TJs density (C) and complexity (D). Black bar and circles present controls, grey bars and squares present the TGF-β treatment (n = 6 visual fields for controls, n = 8 for TGF-β; **** p < 0.0001).
Figure 4
Figure 4
Metabolomics analysis of HBVP upon TGF-β treatment. (A) Determination of mitochondrial to glycolytic ATP production at different TGF-β concentrations using respiratory analysis. HBVP were treated with TGF-β (2 or 10 ng/mL, dark grey bars; black triangles) for 16 h or were left untreated (0; light grey bar; black dots; n = 3, each 6 technical replicates, SEM). (B) Volcano plot visualizing most significant metabolite concentration increase (orange dots) and decrease (purple dots) upon TGF-β treatment compared to control for p < 0.05, false discovery rate (FDR)-corrected data with fold change (FC) threshold >1.2 (n = 4). (C) Bar plots with individual replicate points and SEM of the most significant metabolite changes upon TGF-β treatment (each 5 ng/mL of TGF-β1 plus β2; black triangles) (n = 4): aspartate, glycine, O-phosphocholine, glutamate, UDP-glucose, and citrate concentrations were decreased compared to control (black dots), while glucose and acetate concentrations increased upon TGF-β treatment, based on parametric, unpaired t-test, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Grey bar and circles present controls, dark grey bars and squares present the TGF-β treatment.
Figure 5
Figure 5
Pattern hunter analysis of glucose and citrate. (A) Glucose concentration pattern changes positively correlate (pink bars) with inosine, leucine, isoleucine, lysine, valine, acetate, pyroglutamate, phenylalanine, tyrosine, glutamine, N-methyl-D-aspartate, choline, glutathione, histidine, and alanine, while negatively correlate (blue bars) with aspartate, glycine, citrate, nicotinamide adenine dinucleotide (NAD)+, uridine diphosphate (UDP)-glucose, O-phosphocholine, glutamate, adenosine triphosphate (ATP) and 1-methylnicotinamide. (B) Citrate positively (pinks bars) correlates with O-phosphocholine, glycine, UDP-glucose, aspartate, UDP-galactose, UDP-glucuronate, oxidized glutathione (GSSG), NAD+, and 1-methylnicotinamide; and negatively (blue bars) correlates with inosine, acetate, glucose, pyroglutamate, alanine, formate, lactate, sucrose, lysine, tyrosine, leucine, N-methyl-D-aspartate, glutathione, and isoleucine concentration patterns, based on Pearson r distance measure (n = 4, two-group comparison).
Figure 6
Figure 6
Joint pathway analysis of 1H-NMR spectroscopy-based metabolomics and RNA-seq gene fold change data. Pathway analysis indicates the most significantly changed metabolic pathways (the node color scheme is based on p-values from yellow dots = low significance to red dots = high significance and the node radius is determined based on the pathway impact value). Glycerolipid metabolism, branched-chain amino acid (BCAA) metabolism of valine, leucine, and isoleucine biosynthesis, glycerophospholipid metabolism, amino sugar and nucleotide sugar metabolism, ascorbate metabolism, glycolysis or gluconeogenesis, and alanine, aspartate, and glutamate metabolism, were most affected based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. For joint-pathway analysis, data from metabolomics (n = 4) and RNA-seq (n = 4) were used.
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