Targeted and untargeted proteomics-based comparison of adenoviral infected hCMEC/D3 and hBMEC as a human blood-brain barrier model to study the OATP2B1 transporter

Targeted and untargeted proteomics-based comparison of adenoviral infected hCMEC/D3 and hBMEC as a human blood-brain barrier model to study the OATP2B1 transporter | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Targeted and untargeted proteomics-based comparison of adenoviral infected hCMEC/D3 and hBMEC as a human blood-brain barrier model to study the OATP2B1 transporter Valerio Taggi, Anima M. Schäfer, Jonny H. Kinzi, Danilo Ritz, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5388233/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Mar, 2025 Read the published version in Molecular Neurobiology → Version 1 posted 18 You are reading this latest preprint version Abstract The blood-brain barrier (BBB) is essential for central nervous system (CNS) homeostasis by regulating permeability between the bloodstream and brain. This study evaluates the immortalized human brain capillary endothelial cell lines hCMEC/D3 and hBMEC for their use as an in vitro BBB model to investigate the OATP2B1 transporter following adenoviral infection. We assessed the impact of adenoviral-mediated OATP2B1 expression on BBB marker proteins and transporters using targeted and untargeted mass spectrometry-based proteomics. Targeted proteomics identified measurable levels of endothelial markers PECAM1 and CDH5 in hCMEC/D3, whereas these markers were undetectable in hBMEC. Both cell lines exhibited similar Pgp levels, while BCRP was absent in hCMEC/D3. Although OATP2B1 levels did not significantly increase post-infection in targeted proteomics, untargeted proteomics confirmed enhanced OATP2B1 expression. Other BBB markers and transporters remained unaffected in both cell lines. Notably, hCMEC/D3 demonstrated a stronger BBB phenotype, indicated by higher expression of BBB markers and transporters, while adenoviral infection was more effective in hBMEC. The differences between targeted and untargeted proteomics underscore the need for diverse methods to verify protein expression levels. This comparative analysis provides insights into the strengths and limitations of hCMEC/D3 and hBMEC for BBB research, particularly regarding drug transport mechanisms. hCMEC/D3 hBMEC OATP2B1 Adenoviral infection BBB model Proteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The unique environment within the central nervous system (CNS) is crucial for ensuring normal neurological function. A structure that plays a major role in maintaining CNS homeostasis is the blood-brain barrier (BBB). Due to its highly selective permeability, the BBB governs the entrance of substances from the bloodstream into the brain (Masuda et al., 2019 ). The neurovascular unit composing the BBB consists of brain capillary endothelial cells (BCECs), pericytes, astrocytes, glial cells and neurons (Lok et al., 2007 ). While each of these cell types plays a role in the overall function of the brain's microvasculature, BCECs are assumed to be responsible for the selective BBB permeability (Urich et al., 2012 ). Human immortalised BCECs are often used as in vitro models to study the BBB and its properties (Naik & Cucullo, 2012 ; Sivandzade & Cucullo, 2018 ). These models offer advantages, such as ease of use, reproducibility, and the ability to conduct high-throughput screenings. However, they also present limitations, namely the potential for genetic drift, de-differentiation and consequently an incomplete representation of in vivo BCECs characteristics (Czupalla et al., 2014 ). Among the various cell lines employed, the human cell lines hCMEC/D3 and hBMEC have been used and characterised for their utility as in vitro BBB models. When looking into the literature, greater attention was given to hCMEC/D3, which have been studied for their expression of tight junction proteins, surface receptors and transporters (Masuda et al., 2019 ; Ohtsuki et al., 2013 ; Urich et al., 2012 ), revealing that they maintain the expression of various proteins normally found at the BBB. In addition, permeability of a range of test compounds varying in physicochemical properties and sizes were investigated in hCMEC/D3 (Poller et al., 2008 ). hBMEC have also been evaluated, although less extensively. In particular, a study by Eigenmann et al. proposed hBMEC as a model to distinguish between permeable and non-permeable molecules at the BBB (Eigenmann et al., 2016 ). The two cell lines have also been compared for their use as an in vitro BBB model, revealing a stronger BBB phenotype in hCMEC/D3 but higher resistance and lower permeability in hBMEC (Eigenmann et al., 2013 ; Taggi et al., 2024 ). An essential aspect of the BBB functionality is the presence of membrane transporters, which coordinate the flux of molecules across the BBB by controlling cellular efflux and uptake. More in detail, members of the ATP-binding cassette (ABC) family are responsible for the efflux of their substrates from the cells (Ebinger & Uhr, 2006 ), whereas the organic anion-transporting polypeptides (OATPs) are involved in the uptake of drugs and endogenous compounds (Grube et al., 2018 ; Schafer et al., 2021 ). Among OATPs, OATP2B1 is of particular interest, as it has been detected at the BBB in humans (Al-Majdoub et al., 2019 ; Gao et al., 2015 ) and it has been proposed to be involved in the uptake of statins and neurosteroids (Schafer et al., 2021 ). Despite that, its precise role remains unclear due to the lack of OATP2B1 expression at the BBB in animal models (Hoshi et al., 2013 ) and to limitations of human in vitro BBB models, which may not fully replicate the expression patterns of all BBB transporters (Czupalla et al., 2014 ; Taggi et al., 2024 ). To address these limitations, methods such as adenoviral infection have been employed to transiently express specific proteins in cell models (Meyer Zu Schwabedissen et al., 2014). This approach allows the investigation of a specific transporter's function in the respective cellular environment. Of note, a first investigation of hCMEC/D3 and hBMEC as an in vitro BBB model in which adenoviral expression of OATP2B1 was applied has already been performed in our research group (Taggi et al., 2024 ). In this study, Western blot analysis and transport experiments revealed higher expression and activity of OATP2B1 in hBMEC, respectively, while other features of the BBB cell model were not affected by adenoviral infection. An additional method to characterise the two cell lines and to evaluate the efficacy of the adenoviral infection in expressing protein of interest is the use of proteomics. More in detail, targeted proteomics provides absolute quantification of specific proteins, whereas untargeted proteomics offers a macroscopic analysis of the protein landscape, although in a more relative and less quantitative manner. As a consequence, combining the two approaches may enhance the information obtained and improve the reproducibility of the data (Hart-Smith, 2020 ; Sobsey et al., 2020 ). Accordingly, the aim of the herein described study was to further evaluate and characterise the Ad-OATP2B1 infected hCMEC/D3 and hBMEC for their use as a human in vitro BBB model to study the OATP2B1 transporter and to investigate the impact of the adenoviral infection on various proteins known to be of relevance in BCECs. MS-based targeted and untargeted proteomics were applied to hCMEC/D3 and hBMEC either infected with Ad-LacZ as control or Ad-OATP2B1 to test the transient expression of OATP2B1 and to assess the expression of key BBB markers, ABC and solute carrier (SLC) transporters in cells undergoing adenoviral infection. Materials and Methods Cell culture hBMEC (ScienCell Research Laboratories, Carlsbad, CA; catalogue number: #1000) (Stins et al., 2001) and hCMEC/D3 (RRID:CVCL_U985) were kindly provided by Prof. Matthias Hamburger, Prof. Robin Teufel (Pharmaceutical Biology), and Prof. Jörg Huwyler (Pharmaceutical Technology) from the Department of Pharmaceutical Sciences, University of Basel, respectively. Both hBMEC and hCMEC/D3 are not authenticated cell lines. Nevertheless, the International Cell Line Authentication Committee has not indicated hBMEC or hCMEC/D3 as a misidentified cell line. Following the previous work conducted by Eigenmann et al. (Eigenmann et al., 2013), hBMEC were cultured in EGM-2 basal medium supplemented with 20% Fetal Calf Serum (FCS, Thermo-Fisher Scientific, Karlsruhe, Germany), 1% penicillin-streptomycin (Thermo-Fisher Scientific) and SingleQuots kit composed of hydrocortisone, human basic fibroblast growth factor, vascular endothelial growth factor, human insulin like growth factor 1, ascorbic acid, epidermal growth factor, and heparin (CC-4176, Lonza, Basel, Switzerland). hCMEC/D3 were cultured in EGM-2 medium with the addition of 5% FCS, 1% penicillin-streptomycin, 1% HEPES (BioConcept AG, Allschwil, Switzerland), 1% Chemically Defined Lipid Concentrate™ (Thermo-Fisher Scientific), human basic fibroblast growth factor, ascorbic acid, and hydrocortisone (Sigma-Aldrich, Buchs, Switzerland) (Taggi et al., 2024). hBMEC used within the study were between passages 15 and 25, while hCMEC/D3 were between passages 28 and 35. Cells were grown at 37°C in a humidified atmosphere with 5% CO 2 , and were cultured in rat-tail collagen type I (R&D Systems, Minneapolis, Minnesota) coated cultureware. Adenoviral infection of hCMEC/D3 and hBMEC and membrane protein sample preparation To analyse protein expression using targeted and untargeted proteomics, hCMEC/D3 and hBMEC were seeded in pre-coated 10 cm culture dishes (Sarstedt, Nümbrecht, Germany) at a density of 1,8 * 10 6 cells/dish. 24 hours later cells were infected with 200 plaque forming units (pfu) of either Ad-OATP2B1 or Ad-LacZ. The Ad-OATP2B1 was previously produced and characterised (Knauer et al., 2010; Schafer et al., 2018), whereas the Ad-LacZ was provided by the ViraPower™ Adenoviral Expression System (Invitrogen, Carlsbad, CA, USA). 72 hours after seeding, cells were harvested in ice-cold extraction buffer I (ProteoExtract® Native Membrane Protein Extraction Kit, Sigma-Aldrich) supplemented with 5 µl/ml of Protease Inhibitor Cocktail (PIC) (P8340, Sigma-Aldrich), transferred in LoBindTubes (Eppendorf, Hamburg, Germany) and incubated for 15 minutes at 4°C while shaking at 100 rpm. Then, samples were centrifuged for 15 minutes at 16000 g and 4°C. The supernatant containing the cytoplasm protein was transferred in separated LoBindTubes, whereas the pellet was resuspended in extraction buffer II (ProteoExtract® Native Membrane Protein Extraction Kit, Sigma-Aldrich) supplemented with 5 µl/ml of PIC and incubated for 60 minutes at 4°C while shaking at 100 rpm, followed by centrifugation for 15 minutes at 16000 g and 4°C. Subsequently, supernatant containing membrane proteins was collected in separated LoBindTubes, whereas the pellet was discarded. Finally, protein content was measured using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific) and the Microplate Reader Tecan Infinite M200 Pro (Tecan, Männedorf, Switzerland). Eight distinct cell culture preparations were made for targeted proteomics analysis, while three were prepared for untargeted proteomics analysis. LC-MS/MS based targeted proteomics of hBMEC and hCMEC/D3 Following the isolation of membrane proteins, if necessary membrane fractions were adjusted to a maximum protein concentration of 2 mg/ml using ammonium bicarbonate buffer (50 mM, pH 7.8, Sigma-Aldrich). Next, 50 µl of each membrane fraction were combined with 5 µl of dithiothreitol (200 mM, Sigma-Aldrich, Taufkirchen, Germany), 20 µl of ammonium bicarbonate buffer, and 5 µl of acetonitrile (Thermo Fisher Scientific) and incubated at 60°C for 20 minutes. Cooling down the samples at room temperature (RT) was followed by the addition of 5 µl iodoacetamide (400 mM, Sigma-Aldrich) and a further incubation at 37°C for 15 minutes. For protein digestion, 5 µl of trypsin (trypsin/protein ratio: 1/40, Promega, Mannheim, Germany) was added and the mixture was incubated at 37°C for 16 hours. Then, 10 µl of formic acid (10% v/v, Sigma-Aldrich) was added to stop the digestion process. The samples were subsequently centrifuged at 16000 g and 4°C for 15 minutes. From the resulting supernatant, 40 µl were taken and mixed with 40 µl of an isotope-labelled internal standard peptide mix (10 nM of each labelled peptide, Thermo Fisher Scientific). Analogous to a previously validated method (Groer et al., 2013), a 7500 QTRAP triple quadrupole mass spectrometer (AB Sciex, Darmstadt, Germany) coupled to an Agilent Technologies 1260 Infinity system (Agilent Technologies, Santa Clara, California) was used for protein quantification. Throughout the analysis, both precision (CV) and accuracy (error) were below 20%. The lower limit of quantification (LLOQ) was0.04 nmol/L and values below LLOQ were excluded from the analysis. Final protein abundance data were normalised to the total protein amount of the isolated membrane fraction, as measured by the BCA assay. Thus, the protein-normalised LLOQ was 0.02 pmol/mg. Protein amount of PECAM1 (UniProtKB-ID: P16284), CDH5 (P33151), OCLN (Q16625), TFRC (P02786), GLUT1 (P11166), ENT1 (Q99808), Pgp (P08183), BCRP (Q9UNQ0), MRP1 (P33527), MRP4 (O15439), MCT1 (P53985), OAT2 (Q9Y694), OAT3 (Q8TCC7), OAT7 (Q8IVM8), OATP1A2 (P46721), OATP2B1 (O94956), OCT1 (O15245), OCT3 (O75751) and Na + /K + -ATPase (P05023) was quantified. The proteomic data have been submitted to the ProteomeXchange Consortium (https://www.proteomexchange.org/) through the MassIVE partner repository, using the MassIVE dataset identifier MSV000096107 and the ProteomeXchange identifier PXD056849. The targeted proteomics analysis via LC-MS/MS was funded by the German Research Foundation (project number: 505943254). Selection of peptides and MRMs for protein analysis Using in silico predictions, peptides specific for the proteins mentioned above were identified applying a method discussed elsewhere(Oswald et al., 2013) . First, protein sequences taken from the UniProtKB/Swiss-Prot databasewere subjected to in silico trypsin digestion (www.expasy.org/tools) , leading to the generation of 7-25 amino acids peptide sequences, in which any missed cleavage site was excluded.Moreover, the following exclusion criteria were applied: peptides located in transmembrane regions, carrying N-terminal glutamic acid, methionine, cysteine or tryptophan, containing non-synonymousgenetic polymorphisms with a frequency higher than 1%,or subjected to experimentally proven post-translational modifications.The specificity of each observed peptide was validated using an NCBI protein BLAST search against the UniProtKB/Swiss-Prot database.To establish the best multiple reaction monitoring (MRM) methods for the top peptides, the most suitable mass transitions were identified and fine-tuned.This was achieved by manually infusing synthetic peptides along with their stable isotope-labelled versions into a 7500 QTRAP triple quadrupole mass spectrometer (AB Sciex). For each peptide, the four mass transitions with the highest signal intensity were selected. Table 1 provides a summary of all proteotypic selected peptides and their optimised mass transitions. Table 1. Overview of the selected proteotypic peptides and their corresponding mass transitions used in targeted proteomic analysis. The amino acid residue at the C-terminus (R or K) of the stable isotope-labelled peptide is marked with an asterisk. hBMEC and hCMEC/D3 membrane protein digest and untargeted proteomics analysis Subsequent to the isolation of membrane proteins, samples were resuspended in 5% SDS, 10 mM Tris(2-carboxyethyl)phosphine hydrochloride (TCEP, Sigma-Aldrich), 0.1 M Triethylammonium bicarbonate (TEAB) and incubated for 10 minutes at 95°C while shaking at 500 rpm. Proteins were alkylated in 20 mM iodoacetamide for 30 minutes at 25°C and digested using S-Trap™ micro spin columns (Protifi, New York, USA) according to the manufacturer’s instructions. Shortly, 12% phosphoric acid was added to each sample (final concentration of phosphoric acid 1.2%) followed by the addition of S-trap buffer (90% methanol, 100 mM TEAB pH 7.1) at a ratio of 6:1. Samples were mixed by vortexing and loaded onto S-trap columns by centrifugation at 4000 g for 1 minute followed by three washes with S-trap buffer. Digestion buffer (50 mM TEAB pH 8.0) containing sequencing-grade modified trypsin (Promega) was added to the S-trap column and samples were incubated for 1 hour at 47 °C. Peptides were eluted by the consecutive addition and collection by centrifugation at 4000 g for 1 minute of 40 ul digestion buffer, 40 ul of 0.2% formic acid and finally 35 ul 50% acetonitrile, 0.2% formic acid. Samples were dried under vacuum and stored at -20 °C. The day of the analysis, dried peptides were resuspended in 0.1% aqueous formic acid, 0.02% DDM (n-Dodecyl-B-D-maltoside) and subjected to LC–MS/MS analysis using a timsTOF Ultra Mass Spectrometer (Bruker, Fällanden, Switzerland) equipped with a CaptiveSpray nano-electrospray ion source (Bruker) and fitted with a Vanquish Neo (Thermo Fisher Scientific). Peptides were resolved using a RP-HPLC column (100 µm × 30 cm) packed in-house with C18 resin (ReproSil Saphir 100 C18, 1.5 µm resin; Dr. Maisch GmbH, Ammerbuch, Germany) at a flow rate of 0.4 µl/min and column heater set to 60°C. The following gradient was used for peptide separation: from 2% B to 25% B over 25 minutes to 35% B over 5 minutes to 95% B over 1 minute followed by 5 minutes at 95% B to 2% B over 1 minute followed by 3 minutes at 2% B. Buffer A was 0.1% formic acid in water and buffer B was 80% acetonitrile, 0.1% formic acid in water. The mass spectrometer was operated in dia-PASEF mode with a cycle time estimate of 0.95 s. MS1 and MS2 scans were acquired over a mass range from 100 to 1700 m/z. A method with 8 dia-PASEF scans separated into 3 ion mobility windows per scan covering a 400-1000 m/z range with 25 Da windows and an ion mobility range from 0.64 to 1.37 Vs cm 2 was used. Accumulation and ramp time were set to 100 ms, capillary voltage was set to 1600V, dry gas was set to 3 l/min and dry temperature was set to 200 °C. The collision energy was ramped linearly as a function of ion mobility from 59 eV at 1/K0 = 1.6 V s cm -2 to 20 eV at 1/K0 = 0.6 V s cm -2 . The acquired files were searched using the Spectronaut (Biognosys v18.6, Schlieren, Switzerland) directDIA workflow against a Homo sapiens database (consisting of 20360 protein sequences downloaded from Uniprot on 20220222) and 393 commonly observed contaminants using default settings. Proteomic data have been deposited to the ProteomeXchange Consortium (https://www.proteomexchange.org/) via the MassIVE partner repository with MassIVE data set identifier MSV000096107 and ProteomeXchange identifier PXD056849. Statistical analysis The targeted proteomics data were analysed using GraphPad Prism version 10.2 (GraphPad Software, La Jolla, California). Statistical significance was set at a P-value of ≤ 0.05. One-way ANOVA followed by Tukey’s post-hoc test for multiple comparisons or unpaired t-test were conducted. The data were not checked for normality, and no outlier tests were performed. For untargeted proteomics analysis, quantitative fragment ion data (F.Area) was exported from Spectronaut and analysed using the MSstats R package v.4.9.9. (https://doi.org/10.1093/bioinformatics/btu305). Data was normalised using the default normalisation option “equalizedMedians”, imputed using “AFT model-based imputation” and P-values for pairwise comparisons were calculated as implemented in MSstats. To identify expression differences, we defined our selection criteria for differentially expressed proteins as a minimum 2-fold change with statistical significance (P-value < 0.05). Results Absolute quantification of BBB markers in adenoviral infected hCMEC/D3 and hBMEC An important feature of a BBB cell model is the presence of endothelial proteins, tight junctions and receptors (Naik & Cucullo, 2012). Following a previous study, where we reported on the expression and functionality of OATP2B1 in adenoviral infected hCMEC/D3 and hBMEC (Taggi et al., 2024), we conducted targeted proteomics in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1, and hBMEC Ad-OATP2B1, comparing the expression of BBB markers between the two cell lines and evaluating the effects of the adenoviral infection on their expression. As shown in Figure 1 , the endothelial markers PECAM1 and CDH5 were detected only in hCMEC/D3, in which the protein abundance was found to be comparable between Ad-LacZ and Ad-OATP2B1 infected cells. Conversely, although not affected by the adenoviral infection, the protein level of the tight junction OCLN was above the lower limit of quantification (LLOQ) only in hBMEC. Finally, protein expression of the BBB receptor TFRC was measurable and comparable in both hCMEC/D3 and hBMEC independently from the adenoviral infection. As control, Na + /K + -ATPase protein abundance was also evaluated. Table 2 reports the values below LLOQ measured in the two cell lines for the above mentioned BBB markers. Table 2. BBB markers under limit of quantification. Absolute protein abundance below lower limit of quantification (LLOQ) of PECAM1 and CDH5 in hBMEC Ad-LacZ and hBMEC Ad-OATP2B1 and of OCLN in hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1. Values are expressed as pmol/ml. Results are reported as mean ± SD of n=8. Targeted proteomics quantification of efflux transporters in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1 A key feature of a robust in vitro BBB cell model is the expression of efflux transporters, as these proteins play an important role in the selective permeability that characterises the BBB. Among them, Pgp, BCRP and multidrug resistance proteins (MRPs) are prominently expressed at the BBB (Begley, 2004). Accordingly, following the quantification of BBB endothelial, tight junction and receptor proteins, we wanted to compare the two cell lines for their protein level of efflux transporters and to verify that the adenoviral induced expression of OATP2B1 did not modify their expression levels. As displayed in Figure 2 , targeted proteomics analysis showed that the abundance of Pgp was comparable between hCMEC/D3 and hBMEC independently from the adenoviral infection. Conversely, BCRP expression was measurable in hBMEC Ad-LacZ and hBMEC Ad-OATP2B1 only, whereas hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1 levels were found to be below LLOQ ( Figure 2 and Table 3 ). Finally, the protein quantification of MRPs revealed that MRP1 and MRP4 protein abundance was comparable between hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1, and hBMEC Ad-OATP2B1 ( Figure 2 ). Table 3. BCRP protein expression in hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1. Below LLOQ absolute protein abundance of BCRP [pmol/ml] in hCMEC/D3 and hCMEC/D3 Ad-OATP2B1. Results are shown as mean ± SD of n=8. Targeted proteomics analysis of SLC transporters following adenoviral infection in hCMEC/D3 and hBMEC Apart from OATP2B1, other SLC transporters are essential at the BBB for the permeation of compounds that play a role in the physiological function of the brain, such as glucose, nucleosides, thyroid hormones and neurosteroids (Taggi et al., 2022; Vatine et al., 2017). Consequently, to broaden the comparison of the two cell lines for their applicability as a BBB cell model and to confirm that the Ad-OATP2B1 infection led to a selectively enhanced expression of OATP2B1, we also evaluated the protein level of other SLC transporters with a major role at the BBB. As shown in Figure 3 , the expression of GLUT1 and ENT1 was comparable among hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. Similar results were found for MCT1, whose protein abundance did not differ in the BBB models ( Figure 3 and Table 4 ). When focusing on OATs and OCTs, diverse results emerged. OAT7 abundance was similar between hCMEC/D3 and hBMEC, regardless of adenoviral infection ( Figure 3 ). In contrast, the protein levels of OAT2, OAT3, OCT1, and OCT3 were either below LLOQ or undetectable. Similarly, OATP1A2 was found to be below LLOQ or not detected in both cell lines, independent of infection status ( Table 4 ). As one of the main goals of this study was to compare the expression of the OATP2B1 transporter in hCMEC/D3 and hBMEC following Ad-OATP2B1 infection, two distinct peptides - SSPAVEQQLLVSGPGK and YYNNDLLR - were used for quantification. Despite previous confirmation of increased OATP2B1 expression in both cell lines after adenoviral infection via Western blot analysis (Taggi et al., 2024), OATP2B1 abundance for both peptides was found to be below LLOQ in hCMEC/D3 and hBMEC, regardless of adenoviral infection (Table 4) . Table 4. SLC transporters below LLOQ in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. Absolute protein abundance of OAT2, OAT3, OCT1, OCT3, OATP1A2, and OATP2B1 below LLOQ in hCMEC/D3 and hBMEC either infected with Ad-LacZ or Ad-OATP2B1. The data are expressed as mean ± SD in pmol/ml, with a total sample size of n=8. ND= not detected. Untargeted proteomics analysis of hCMEC/D3 and hBMEC infected with Ad-LacZ and Ad-OATP2B1 An effective way to describe and compare hCMEC/D3 and hBMEC is the use of untargeted proteomics analysis. While this approach does not quantify absolute protein levels, it provides a broad characterization of the two cell lines. At first, we wanted to verify the efficacy of the Ad-OATP2B1 infection in selectively increasing the expression of the transporter without modifying the level of other key BBB proteins. Consequently, we performed a comparison within the same cell line but between Ad-LacZ and Ad-OATP2B1 infected cells. As reported in Supplementary Table 1 , we identified 6690 proteins in hCMEC/D3 and 6860 proteins in hBMEC. A generally lower variation in protein level and no protein with a biologically meaningful different expression level was found in hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1, whereas a higher variation in expression could be observed when comparing hBMEC Ad-LacZ and hBMEC Ad-OATP2B1, mainly for proteins related to the cell cycle, the adenoviral infection and the subsequent DNA modification ( Figure 4A and 4B ). Specifically, the level of GEN1 (involved in Holliday junction resolution, DNA repair, meiosis and regulation of the cell cycle (Chan & West, 2015; Ip et al., 2008; Lee et al., 2015)) was increased in hBMEC Ad-OATP2B1, whereas in hBMEC Ad-LacZ enhanced protein expression was found for ITB3 (partaking in cell signalling (Mori et al., 2008; Saegusa et al., 2009)), LRRN4 (contributing to cell adhesion and signal transduction (Bando et al., 2013; Chen et al., 2015)), ABRAL (active in cell migration (Fan et al., 2021)), S100A6 (associated with cell proliferation and differentiation (Li et al., 2015)), and SPB5 (connected to cell signalling (Bonuccelli et al., 2009)). In addition, the analysis revealed that the Ad-OATP2B1 infection increased the expression of OATP2B1 in both hCMEC/D3 and hBMEC but did not modify the expression of BBB markers, ABC and SLC transporters ( Figure 4C and 4D ). Importantly, although OATP2B1 protein level was enhanced in both cell lines, the difference in OATP2B1 level between Ad-LacZ infected and Ad-OATP2B1 infected cells was statistically significant in hBMEC only. The list of selected BBB markers, ABC and SLC transporters is reported in Supplementary Table 2 . Comparison of Ad-OATP2B1 infected hCMEC/D3 and hBMEC by untargeted proteomics After confirming that Ad-OATP2B1 infection increased OATP2B1 expression without affecting the protein levels of key BBB markers, we proceeded with comparing hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. One major goal of this study is to evaluate these two cell lines as a human in vitro model for studying OATP2B1 at the BBB. Although the analysis revealed 6498 shared proteins ( Supplementary Table 1 ), the expression of several proteins differed between hBMEC Ad-OATP2B1 and hCMEC/D3 Ad-OATP2B1 ( Figure 5A ). More in detail, a thorough assessment of the two cell lines displayed that hCMEC/D3 Ad-OATP2B1 had a higher protein expression of the BBB markers B2M, PECAM1, CDH5, FCGRT, LDLR and VLDLR ( Figure 5B ). Furthermore, hCMEC/D3 Ad-OATP2B1 showed enhanced protein level of the ABC transporters Pgp and MRP1, whereas hBMEC Ad-OATP2B1 had an augmented level of ABC50 ( Figure 5C ). Finally, when observing the expression of SLC transporters, higher expression was observed for various SLC transporters in hCMEC/D3 Ad-OATP2B1, whereas only OATP4A1, S22AI and ZIP8 were elevated in hBMEC Ad-OATP2B1 ( Figure 5D ). Of note, although not statistically significant, OATP2B1 expression was higher in hBMEC Ad-OATP2B1 compared to hCMEC/D3 Ad-OATP2B1. Discussion In this study, we characterised and compared the applicability of Ad-OATP2B1 infected hCMEC/D3 and hBMEC as a human in vitro BBB cell model to study the OATP2B1 transporter. For this, we determined the change in OATP2B1 expression after adenoviral gene transfer in the two cell lines and focused on investigating the influence of the adenoviral infection on the expression of key BBB proteins. A robust BBB cell model should express a broad spectrum of proteins normally found at the BBB in vivo , such as endothelial markers, tight junction proteins, receptors, efflux and uptake transporters (Naik & Cucullo, 2012 ). Performing targeted proteomics, hCMEC/D3 and hBMEC were compared for their expression of 18 proteins among BBB markers, efflux, and uptake transporters. Independently from the adenoviral infection, hCMEC/D3 cells showed a higher expression of the endothelial markers PECAM1 and CDH5, whereas hBMEC levels of these endothelial markers were below LLOQ. However, the expression of TFRC was comparable between the two cell lines. These results are in line with a previous study performed in our research group, in which the expression of BBB markers was evaluated performing real-time quantitative PCR and Western blot analysis (Taggi et al., 2024 ), further pointing to the tendency of hCMEC/D3 cells to better preserve the BBB endothelial phenotype compared to hBMEC cells. Conversely, the expression of the tight junction protein OCLN was above LLOQ only in hBMEC. As OCLN is involved in cell permeability (Taggi et al., 2024 ), its higher expression may explain the previously observed greater tightness of hBMEC compared to hCMEC/D3 (Eigenmann et al., 2013 ; Taggi et al., 2024 ). Efflux transporters are critical for maintaining the selective permeability of the BBB, as they are involved in the protection of the brain against toxic compounds (Taggi et al., 2022 ). In contrast to what we showed in our previous study (Taggi et al., 2024 ), targeted proteomics revealed comparable levels of Pgp in hCMEC/D3 and hBMEC either infected with Ad-LacZ or Ad-OATP2B1. On the contrary, the BCRP level was found to be below LLOQ in hCMEC/D3 Ad-lacZ and hCMEC/D3 Ad-OATP2B1. These results are also in opposition to previous literature (Masuda et al., 2019 ; Ohtsuki et al., 2013 ; Taggi et al., 2024 ) in which BCRP expression was shown in hCMEC/D3 by RT-qPCR, Western blot and proteomic analyses. One potential explanation for the below LLOQ level of BCRP observed in this study could be the low protein concentrations obtained from the hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1 samples. When protein concentrations are low, the resulting samples may not provide sufficient material for accurate detection. Additionally, the subsequent dilution that occurs during the analysis by the LC-MS/MS device may further contribute to this issue. This dilution can further reduce the concentration of BCRP in the final sample, making it more difficult to quantify it accurately (Wilson et al., 2015 ). Thus, both the initial protein concentration and the dilution occurring during the analysis likely play a role in the observed low LLOQ for BCRP in this study. In the next step, we evaluated uptake transporters, for which targeted proteomics-based characterization revealed comparable levels of GLUT1, ENT1, MCT1, and OAT7 among hCMEC/D3 and hBMEC independently of the adenoviral infection. The expression of OAT2, OAT3, OCT1, OCT3, and OATP1A2, however, was found to be below LLOQ in both cell lines regardless of the adenoviral infection. Importantly, applying this proteomics analysis we were unable to quantify OATP2B1 in hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. This finding contrasts with the results observed performing untargeted proteomics, which showed a significant increase in OATP2B1 expression in hBMEC following Ad-OATP2B1 infection, while no significant change was observed in hCMEC/D3. Such results are in line with our previous study, in which performing Western blot analysis and transport assay enhanced OATP2B1 expression and functionality could be observed only in hBMEC following Ad-OATP2B1 infection (Taggi et al., 2024 ). The lack of OATP2B1 detection in targeted proteomics can be attributed to several factors. Despite applying exclusion criteria during the selection of proteotypic peptides, our inability to detect OATP2B1 may still be due to post-translational modifications or artifactual chemical changes during sample processing (Lange et al., 2008 ). In the targeted proteomics the peptides YYNNDLLR and SSPAVEQQLLVSGPGK were used for the measurement of OATP2B1 protein amount. However, performing untargeted proteomics only YYNNDLLR was detected within the OATP2B1 identifying peptides. These findings suggest post-translational modification of the SSPAVEQQLLVGSPGK peptide. In fact, using the NetPhos predictor (Blom et al., 1999 ), we identified a phosphorylation probability exceeding 99% for this peptide. In addition, the inability to detect YYNNDLLR during targeted proteomics may be attributed to variations in sample preparation methods (Masuda et al., 2019 ; Wegler et al., 2017 ; Wilson et al., 2015 ). Specifically, only in the preparation of untargeted proteomics the samples were dried and resuspended, thereby increasing their concentration. The elements discussed above to explain the differences in OATP2B1 measurement among the two employed methods can be also applied to understand the further discrepancies found between targeted and untargeted proteomics comparison of hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. More in detail, untargeted proteomics comparison showed enhanced expression of BBB markers in hCMEC/D3 Ad-OATP2B1 when compared to hBMEC Ad-OATP2B1, in line with what was observed in the targeted proteomics analysis. On the contrary, evaluation by untargeted proteomics displayed higher levels of the ABC transporters Pgp and MRP1 and of several SLC transporters in hCMEC/D3 Ad-OATP2B1, thus further emphasising the stronger BBB phenotype of this cell line. Of note, despite not being statistically significant, OATP2B1 expression was higher in hBMEC Ad-OATP2B1 when compared to hCMEC/D3 Ad-OATP2B1, which is in line with our previous comparison study (Taggi et al., 2024 ). Moreover, the broader characterization of the two cell lines provided by the untargeted proteomics analysis revealed a higher variation in the expression of proteins related to the cell cycle and viral infection in hBMEC, thus suggesting a more pronounced cellular response to adenoviral infection. Despite that, the expression levels of other BBB markers, ABC, and SLC transporters remained unchanged in both cell lines, confirming the specificity of the adenoviral system for OATP2B1 without broadly altering the BBB phenotype. This project intended to continue the characterization and comparison of hCMEC/D3 and hBMEC for their applicability as a cell model to study transport across the BBB (Taggi et al., 2024 ). Taken together, our results indicate that hCMEC/D3 have a more solid BBB phenotype, making them usable as a BBB model to study the function of transporters endogenously expressed in this cell line. In fact, hCMEC/D3 have been previously employed to investigate Pgp and BCRP regulation at the BBB (Poller et al., 2010 ). Considering the low efficacy of Ad-OATP2B1 infection in increasing OATP2B1 expression and functionality (Taggi et al., 2024 ), a potential alternative method to drive protein expression in hCMEC/D3 might be the use of lentiviral infection, as previously performed by Gericke et al. for the expression of claudin-5 (Gericke et al., 2020 ). However, our current and previous findings (Taggi et al., 2024 ), along with studies from other groups (Balzer et al., 2022 ; Eigenmann et al., 2013 ), suggest that hCMEC/D3 cells exhibit leakiness, posing a significant challenge given the need for a tight cell monolayer in cellular permeability studies. On the contrary, hBMEC showed higher tightness (Eigenmann et al., 2013 ; Taggi et al., 2024 ) and have been previously used as a model to predict the permeability of compounds at the BBB (Eigenmann et al., 2016 ). In addition, their stronger response to adenoviral-induced modification suggests that hBMEC protein expression can be more easily modified when compared to hCMEC/D3. Nevertheless, hBMEC showed a lower level of endothelial markers and transporters and consequently have a weaker BBB phenotype than hCMEC/D3. In summary, the discrepancies found between targeted and untargeted proteomics highlight the importance of employing orthogonal methods to validate protein expression levels. Despite that, we can conclude that the adenoviral infection can be used to selectively express transporters of interest without modifying the expression of BBB markers, ABC and SLC transporters in hCMEC/D3 and hBMEC, although the efficacy of this technique is higher in hBMEC. Therefore, it is crucial to carefully assess the efficacy of the selected transfection method to ensure it effectively manipulates cells and meets the specific requirements of the study. Based on these considerations, although with their limitations, we still suggest that both hCMEC/D3 and hBMEC can be used as a human cell model to study transport at the BBB. However, it is essential to select the model that best aligns with the specific requirements of the research question at hand. Doing so will enhance the validity of the findings and ensure that the results accurately reflect the phenomena being investigated. Abbreviations ABC: ATP-binding cassette BBB: Blood-brain barrier BCECs: Brain capillary endothelial cells CNS: Central nervous system DDM: n-dodecyl-B-D-maltoside FCS: Fetal calf serum LLOQ: Lower limit of quantification MRM: Multiple reaction monitoring MRPs: Multidrug resistance protein OATPs: Organic anion-transporting polypeptides PIC: Protease inhibitor cocktail RT: Room temperature SLC: Solute carrier TEAB: Triethylammonium bicarbonate TCEP: Tris(2-carboxyethyl)phosphine hydrochloride Declarations Funding This project was funded by the Biopharmacy at the University of Basel, by the Swiss 3R Competence Center, and by the German Research Foundation (project number: 505943254). Competing interests The authors have no competing interests with the contents of this manuscript. This manuscript will be part of the PhD thesis of Valerio Taggi. Author Contributions Valerio Taggi: contributed to the conception and design of the work, acquisition, analysis and interpretation of data, draft and revision of the work. Anima Magdalena Schäfer: contributed to the conception and design of the work, acquisition, analysis and interpretation of data, draft and revision of the work. Jonny Hanna Kinzi: contributed to the analysis and interpretation of data, draft and revision of the work. Danilo Ritz: contributed to the acquisition, analysis and interpretation of data, revision of the work. Isabell Seibert: contributed to the acquisition, analysis and interpretation of data. Stefan Oswald: contributed to the acquisition, analysis and interpretation of data, revision of the work. Henriette E. Meyer zu Schwabedissen: contributed to the conception and design of the work, interpretation of data, draft and revision of the work. Data availability The proteomic data have been submitted to the ProteomeXchange Consortium (https://www.proteomexchange.org/) through the MassIVE partner repository, using the MassIVE dataset identifier MSV000096107 and the ProteomeXchange identifier PXD056849. Additionally, the data supporting the findings of this study are available upon reasonable request from the corresponding authors. References Al-Majdoub, Z. M., Al Feteisi, H., Achour, B., Warwood, S., Neuhoff, S., Rostami-Hodjegan, A., & Barber, J. (2019). Proteomic Quantification of Human Blood-Brain Barrier SLC and ABC Transporters in Healthy Individuals and Dementia Patients. Mol Pharm , 16 (3), 1220-1233. https://doi.org/10.1021/acs.molpharmaceut.8b01189 Balzer, V., Poc, P., Puris, E., Martin, S., Aliasgari, M., Auriola, S., & Fricker, G. (2022). Re-evaluation of the hCMEC/D3 based in vitro BBB model for ABC transporter studies. Eur J Pharm Biopharm , 173 , 12-21. https://doi.org/10.1016/j.ejpb.2022.02.017 Bando, T., Morikawa, Y., Hisaoka, T., Komori, T., Miyajima, A., & Senba, E. (2013). Dynamic expression pattern of leucine-rich repeat neuronal protein 4 in the mouse dorsal root ganglia during development. Neurosci Lett , 548 , 73-78. https://doi.org/10.1016/j.neulet.2013.05.024 Begley, D. J. (2004). ABC transporters and the blood-brain barrier. Curr Pharm Des , 10 (12), 1295-1312. https://doi.org/10.2174/1381612043384844 Blom, N., Gammeltoft, S., & Brunak, S. (1999). Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol , 294 (5), 1351-1362. https://doi.org/10.1006/jmbi.1999.3310 Bonuccelli, G., Casimiro, M. C., Sotgia, F., Wang, C., Liu, M., Katiyar, S., Zhou, J., Dew, E., Capozza, F., Daumer, K. M., Minetti, C., Milliman, J. N., Alpy, F., Rio, M. C., Tomasetto, C., Mercier, I., Flomenberg, N., Frank, P. G., Pestell, R. G., & Lisanti, M. P. (2009). Caveolin-1 (P132L), a common breast cancer mutation, confers mammary cell invasiveness and defines a novel stem cell/metastasis-associated gene signature. Am J Pathol , 174 (5), 1650-1662. https://doi.org/10.2353/ajpath.2009.080648 Chan, Y. W., & West, S. (2015). GEN1 promotes Holliday junction resolution by a coordinated nick and counter-nick mechanism. Nucleic Acids Res , 43 (22), 10882-10892. https://doi.org/10.1093/nar/gkv1207 Chen, C., Bartenhagen, C., Gombert, M., Okpanyi, V., Binder, V., Rottgers, S., Bradtke, J., Teigler-Schlegel, A., Harbott, J., Ginzel, S., Thiele, R., Husemann, P., Krell, P. F., Borkhardt, A., Dugas, M., Hu, J., & Fischer, U. (2015). Next-generation-sequencing of recurrent childhood high hyperdiploid acute lymphoblastic leukemia reveals mutations typically associated with high risk patients. Leuk Res , 39 (9), 990-1001. https://doi.org/10.1016/j.leukres.2015.06.005 Czupalla, C. J., Liebner, S., & Devraj, K. (2014). In vitro models of the blood-brain barrier. Methods Mol Biol , 1135 , 415-437. https://doi.org/10.1007/978-1-4939-0320-7_34 Ebinger, M., & Uhr, M. (2006). ABC drug transporter at the blood-brain barrier: effects on drug metabolism and drug response. Eur Arch Psychiatry Clin Neurosci , 256 (5), 294-298. https://doi.org/10.1007/s00406-006-0664-4 Eigenmann, D. E., Jahne, E. A., Smiesko, M., Hamburger, M., & Oufir, M. (2016). Validation of an immortalized human (hBMEC) in vitro blood-brain barrier model. Anal Bioanal Chem , 408 (8), 2095-2107. https://doi.org/10.1007/s00216-016-9313-6 Eigenmann, D. E., Xue, G., Kim, K. S., Moses, A. V., Hamburger, M., & Oufir, M. (2013). Comparative study of four immortalized human brain capillary endothelial cell lines, hCMEC/D3, hBMEC, TY10, and BB19, and optimization of culture conditions, for an in vitro blood-brain barrier model for drug permeability studies. Fluids Barriers CNS , 10 (1), 33. https://doi.org/10.1186/2045-8118-10-33 Fan, S., Chen, P., & Li, S. (2021). miR-145-5p Inhibits the Proliferation, Migration, and Invasion of Esophageal Carcinoma Cells by Targeting ABRACL. Biomed Res Int , 2021 , 6692544. https://doi.org/10.1155/2021/6692544 Gao, B., Vavricka, S. R., Meier, P. J., & Stieger, B. (2015). Differential cellular expression of organic anion transporting peptides OATP1A2 and OATP2B1 in the human retina and brain: implications for carrier-mediated transport of neuropeptides and neurosteriods in the CNS. Pflugers Arch , 467 (7), 1481-1493. https://doi.org/10.1007/s00424-014-1596-x Gericke, B., Romermann, K., Noack, A., Noack, S., Kronenberg, J., Blasig, I. E., & Loscher, W. (2020). A face-to-face comparison of claudin-5 transduced human brain endothelial (hCMEC/D3) cells with porcine brain endothelial cells as blood-brain barrier models for drug transport studies. Fluids Barriers CNS , 17 (1), 53. https://doi.org/10.1186/s12987-020-00212-5 Groer, C., Bruck, S., Lai, Y., Paulick, A., Busemann, A., Heidecke, C. D., Siegmund, W., & Oswald, S. (2013). LC-MS/MS-based quantification of clinically relevant intestinal uptake and efflux transporter proteins. J Pharm Biomed Anal , 85 , 253-261. https://doi.org/10.1016/j.jpba.2013.07.031 Grube, M., Hagen, P., & Jedlitschky, G. (2018). Neurosteroid Transport in the Brain: Role of ABC and SLC Transporters. Front Pharmacol , 9 , 354. https://doi.org/10.3389/fphar.2018.00354 Hart-Smith, G. (2020). Combining Targeted and Untargeted Data Acquisition to Enhance Quantitative Plant Proteomics Experiments. Methods Mol Biol , 2139 , 169-178. https://doi.org/10.1007/978-1-0716-0528-8_13 Hoshi, Y., Uchida, Y., Tachikawa, M., Inoue, T., Ohtsuki, S., & Terasaki, T. (2013). Quantitative atlas of blood-brain barrier transporters, receptors, and tight junction proteins in rats and common marmoset. J Pharm Sci , 102 (9), 3343-3355. https://doi.org/10.1002/jps.23575 Ip, S. C., Rass, U., Blanco, M. G., Flynn, H. R., Skehel, J. M., & West, S. C. (2008). Identification of Holliday junction resolvases from humans and yeast. Nature , 456 (7220), 357-361. https://doi.org/10.1038/nature07470 Knauer, M. J., Urquhart, B. L., Meyer zu Schwabedissen, H. E., Schwarz, U. I., Lemke, C. J., Leake, B. F., Kim, R. B., & Tirona, R. G. (2010). Human skeletal muscle drug transporters determine local exposure and toxicity of statins. Circ Res , 106 (2), 297-306. https://doi.org/10.1161/CIRCRESAHA.109.203596 Lange, V., Picotti, P., Domon, B., & Aebersold, R. (2008). Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol , 4 , 222. https://doi.org/10.1038/msb.2008.61 Lee, S. H., Princz, L. N., Klugel, M. F., Habermann, B., Pfander, B., & Biertumpfel, C. (2015). Human Holliday junction resolvase GEN1 uses a chromodomain for efficient DNA recognition and cleavage. Elife , 4 . https://doi.org/10.7554/eLife.12256 Li, Y., Wagner, E. R., Yan, Z., Wang, Z., Luther, G., Jiang, W., Ye, J., Wei, Q., Wang, J., Zhao, L., Lu, S., Wang, X., Mohammed, M. K., Tang, S., Liu, H., Fan, J., Zhang, F., Zou, Y., Song, D.,…He, T. C. (2015). The Calcium-Binding Protein S100A6 Accelerates Human Osteosarcoma Growth by Promoting Cell Proliferation and Inhibiting Osteogenic Differentiation. Cell Physiol Biochem , 37 (6), 2375-2392. https://doi.org/10.1159/000438591 Lok, J., Gupta, P., Guo, S., Kim, W. J., Whalen, M. J., van Leyen, K., & Lo, E. H. (2007). Cell-cell signaling in the neurovascular unit. Neurochem Res , 32 (12), 2032-2045. https://doi.org/10.1007/s11064-007-9342-9 Masuda, T., Hoshiyama, T., Uemura, T., Hirayama-Kurogi, M., Ogata, S., Furukawa, A., Couraud, P. O., Furihata, T., Ito, S., & Ohtsuki, S. (2019). Large-Scale Quantitative Comparison of Plasma Transmembrane Proteins between Two Human Blood-Brain Barrier Model Cell Lines, hCMEC/D3 and HBMEC/cibeta. Mol Pharm , 16 (5), 2162-2171. https://doi.org/10.1021/acs.molpharmaceut.9b00114 Meyer Zu Schwabedissen, H. E., Begunk, R., Hussner, J., Juhnke, B. O., Gliesche, D., Bottcher, K., Sternberg, K., Schmitz, K. P., & Kroemer, H. K. (2014). Cell-specific expression of uptake transporters--a potential approach for cardiovascular drug delivery devices. Mol Pharm , 11 (3), 665-672. https://doi.org/10.1021/mp400245g Mori, S., Wu, C. Y., Yamaji, S., Saegusa, J., Shi, B., Ma, Z., Kuwabara, Y., Lam, K. S., Isseroff, R. R., Takada, Y. K., & Takada, Y. (2008). Direct binding of integrin alphavbeta3 to FGF1 plays a role in FGF1 signaling. J Biol Chem , 283 (26), 18066-18075. https://doi.org/10.1074/jbc.M801213200 Naik, P., & Cucullo, L. (2012). In vitro blood-brain barrier models: current and perspective technologies. J Pharm Sci , 101 (4), 1337-1354. https://doi.org/10.1002/jps.23022 Ohtsuki, S., Ikeda, C., Uchida, Y., Sakamoto, Y., Miller, F., Glacial, F., Decleves, X., Scherrmann, J. M., Couraud, P. O., Kubo, Y., Tachikawa, M., & Terasaki, T. (2013). Quantitative targeted absolute proteomic analysis of transporters, receptors and junction proteins for validation of human cerebral microvascular endothelial cell line hCMEC/D3 as a human blood-brain barrier model. Mol Pharm , 10 (1), 289-296. https://doi.org/10.1021/mp3004308 Oswald, S., Groer, C., Drozdzik, M., & Siegmund, W. (2013). Mass spectrometry-based targeted proteomics as a tool to elucidate the expression and function of intestinal drug transporters. AAPS J , 15 (4), 1128-1140. https://doi.org/10.1208/s12248-013-9521-3 Poller, B., Drewe, J., Krahenbuhl, S., Huwyler, J., & Gutmann, H. (2010). Regulation of BCRP (ABCG2) and P-glycoprotein (ABCB1) by cytokines in a model of the human blood-brain barrier. Cell Mol Neurobiol , 30 (1), 63-70. https://doi.org/10.1007/s10571-009-9431-1 Poller, B., Gutmann, H., Krahenbuhl, S., Weksler, B., Romero, I., Couraud, P. O., Tuffin, G., Drewe, J., & Huwyler, J. (2008). The human brain endothelial cell line hCMEC/D3 as a human blood-brain barrier model for drug transport studies. J Neurochem , 107 (5), 1358-1368. https://doi.org/10.1111/j.1471-4159.2008.05730.x Saegusa, J., Yamaji, S., Ieguchi, K., Wu, C. Y., Lam, K. S., Liu, F. T., Takada, Y. K., & Takada, Y. (2009). The direct binding of insulin-like growth factor-1 (IGF-1) to integrin alphavbeta3 is involved in IGF-1 signaling. J Biol Chem , 284 (36), 24106-24114. https://doi.org/10.1074/jbc.M109.013201 Schafer, A. M., Bock, T., & Meyer Zu Schwabedissen, H. E. (2018). Establishment and Validation of Competitive Counterflow as a Method To Detect Substrates of the Organic Anion Transporting Polypeptide 2B1. Mol Pharm , 15 (12), 5501-5513. https://doi.org/10.1021/acs.molpharmaceut.8b00631 Schafer, A. M., Meyer Zu Schwabedissen, H. E., & Grube, M. (2021). Expression and Function of Organic Anion Transporting Polypeptides in the Human Brain: Physiological and Pharmacological Implications. Pharmaceutics , 13 (6). https://doi.org/10.3390/pharmaceutics13060834 Sivandzade, F., & Cucullo, L. (2018). In-vitro blood-brain barrier modeling: A review of modern and fast-advancing technologies. J Cereb Blood Flow Metab , 38 (10), 1667-1681. https://doi.org/10.1177/0271678X18788769 Sobsey, C. A., Ibrahim, S., Richard, V. R., Gaspar, V., Mitsa, G., Lacasse, V., Zahedi, R. P., Batist, G., & Borchers, C. H. (2020). Targeted and Untargeted Proteomics Approaches in Biomarker Development. Proteomics , 20 (9), e1900029. https://doi.org/10.1002/pmic.201900029 Stins, M. F., Badger, J., & Sik Kim, K. (2001). Bacterial invasion and transcytosis in transfected human brain microvascular endothelial cells. Microb Pathog , 30 (1), 19-28. https://doi.org/10.1006/mpat.2000.0406 Taggi, V., Riera Romo, M., Piquette-Miller, M., Meyer Zu Schwabedissen, H. E., & Neuhoff, S. (2022). Transporter Regulation in Critical Protective Barriers: Focus on Brain and Placenta. Pharmaceutics , 14 (7). https://doi.org/10.3390/pharmaceutics14071376 Taggi, V., Schafer, A. M., Dolce, A., & Meyer Zu Schwabedissen, H. E. (2024). A face-to-face comparison of the BBB cell models hCMEC/D3 and hBMEC for their applicability to adenoviral expression of transporters. J Neurochem , 168 (9), 2611-2620. https://doi.org/10.1111/jnc.16125 Urich, E., Lazic, S. E., Molnos, J., Wells, I., & Freskgard, P. O. (2012). Transcriptional profiling of human brain endothelial cells reveals key properties crucial for predictive in vitro blood-brain barrier models. PLoS One , 7 (5), e38149. https://doi.org/10.1371/journal.pone.0038149 Vatine, G. D., Al-Ahmad, A., Barriga, B. K., Svendsen, S., Salim, A., Garcia, L., Garcia, V. J., Ho, R., Yucer, N., Qian, T., Lim, R. G., Wu, J., Thompson, L. M., Spivia, W. R., Chen, Z., Van Eyk, J., Palecek, S. P., Refetoff, S., Shusta, E. V., & Svendsen, C. N. (2017). Modeling Psychomotor Retardation using iPSCs from MCT8-Deficient Patients Indicates a Prominent Role for the Blood-Brain Barrier. Cell Stem Cell , 20 (6), 831-843 e835. https://doi.org/10.1016/j.stem.2017.04.002 Wegler, C., Gaugaz, F. Z., Andersson, T. B., Wisniewski, J. R., Busch, D., Groer, C., Oswald, S., Noren, A., Weiss, F., Hammer, H. S., Joos, T. O., Poetz, O., Achour, B., Rostami-Hodjegan, A., van de Steeg, E., Wortelboer, H. M., & Artursson, P. (2017). Variability in Mass Spectrometry-based Quantification of Clinically Relevant Drug Transporters and Drug Metabolizing Enzymes. Mol Pharm , 14 (9), 3142-3151. https://doi.org/10.1021/acs.molpharmaceut.7b00364 Wilson, S. R., Vehus, T., Berg, H. S., & Lundanes, E. (2015). Nano-LC in proteomics: recent advances and approaches. Bioanalysis , 7 (14), 1799-1815. https://doi.org/10.4155/bio.15.92 Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Schäfer","email":"","orcid":"","institution":"University of Basel","correspondingAuthor":false,"prefix":"","firstName":"Anima","middleName":"M.","lastName":"Schäfer","suffix":""},{"id":382034668,"identity":"03d6558e-7504-4e23-9574-37a014964df1","order_by":2,"name":"Jonny H. Kinzi","email":"","orcid":"","institution":"University of Basel","correspondingAuthor":false,"prefix":"","firstName":"Jonny","middleName":"H.","lastName":"Kinzi","suffix":""},{"id":382034670,"identity":"e02732ee-0695-4dd0-94df-da19d2b49eca","order_by":3,"name":"Danilo Ritz","email":"","orcid":"","institution":"University of Basel","correspondingAuthor":false,"prefix":"","firstName":"Danilo","middleName":"","lastName":"Ritz","suffix":""},{"id":382034671,"identity":"73076739-8a24-4623-9bc7-e3eec36c6eae","order_by":4,"name":"Isabell Seibert","email":"","orcid":"","institution":"University of Basel","correspondingAuthor":false,"prefix":"","firstName":"Isabell","middleName":"","lastName":"Seibert","suffix":""},{"id":382034675,"identity":"644acd24-3494-4f18-868c-815a059713d9","order_by":5,"name":"Stefan Oswald","email":"","orcid":"","institution":"Rostock University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Oswald","suffix":""},{"id":382034679,"identity":"c60bf57c-395f-4350-89c7-23bda800ab71","order_by":6,"name":"Henriette E. Meyer zu Schwabedissen","email":"data:image/png;base64,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","orcid":"","institution":"University of Basel Biopharmacy","correspondingAuthor":true,"prefix":"","firstName":"Henriette","middleName":"E. Meyer","lastName":"zu Schwabedissen","suffix":""}],"badges":[],"createdAt":"2024-11-04 12:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5388233/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5388233/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12035-025-04807-7","type":"published","date":"2025-03-14T15:58:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70648716,"identity":"6479bbf3-2cde-4e19-b43e-8b7db6371e46","added_by":"auto","created_at":"2024-12-05 08:46:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of BBB markers expression in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. \u003c/strong\u003ePECAM1, CDH5, OCLN, TFRC and Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e-ATPase protein abundance [pmol/mg of membrane protein] in hCMEC/D3 and hBMEC infected with either Ad-LacZ or Ad-OATP2B1. One-way ANOVA followed by Tukey's test for multiple comparisons or unpaired t-test were used. n=3-8. Samples below the lower limit of quantification (LLOQ) were excluded from the analysis. Results are reported as mean ± SD. Images created with GraphPad Prism version 10.2.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/085a76b67d4fe86135c28b33.png"},{"id":70649876,"identity":"e842334f-4c42-4d82-9b6c-95bdbf8fae3f","added_by":"auto","created_at":"2024-12-05 08:54:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of efflux transporters in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. \u003c/strong\u003eProtein level of Pgp, BCRP, MRP1 and MRP4 was quantified as pmol/mg of membrane protein in the differently infected hCMEC/D3 and hBMEC. One-way ANOVA followed by Tukey's test for multiple comparisons or unpaired t-test were used for statistical analysis. n=3-8. Samples below LLOQ were excluded from the analysis. Data are shown as mean ± SD. Images created with GraphPad Prism version 10.2.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/0e11ecd6177960bcf48b1c90.png"},{"id":70648724,"identity":"6930fa72-6bbd-44e9-8869-3519ce04cff6","added_by":"auto","created_at":"2024-12-05 08:46:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68995,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUptake transporters expression in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. \u003c/strong\u003eGLUT1, ENT1, MCT1, and OAT7 protein levels [pmol/mg of membrane protein] were quantified in Ad-LacZ and Ad-OATP2B1 infected hCMEC/D3 and hBMEC. Statistical comparisons were made using one-way ANOVA followed by Tukey’s test for multiple comparisons. Data from samples below the LLOQ were excluded from the analysis. Results are presented as mean ± SD, with sample sizes ranging from n=3 to n=8. Images created with GraphPad Prism version 10.2.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/1004df50178c8dfd0d8f2254.png"},{"id":70648717,"identity":"69cb1e5c-d127-422f-9aa0-30346fca7cbc","added_by":"auto","created_at":"2024-12-05 08:46:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano Plots showing the relative expression of membrane proteins in adenovirus-infected hCMEC/D3 and hBMEC. \u003c/strong\u003eRelative membrane protein expression in hCMEC/D3 Ad-LacZ in respect to hCMEC/D3 Ad-OATP2B1 (A) and hBMEC Ad-LacZ in respect to hBMEC Ad-OATP2B1 (B), with a focus on BBB markers, ABC and SLC transporters (C-D).\u003cstrong\u003e \u003c/strong\u003eUnpaired t-test was used for statistical analysis. Red or blue dots show protein with an at least 2-fold statistically significant difference (P-value ≤ 0.05) in expression. Three distinct cell culture preparations were performed. Triplicates were averaged. Images created with GraphPad Prism version 10.2.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/d94acab764fa348584d7adcb.png"},{"id":70648722,"identity":"4f5578a1-d054-449f-82f2-fb47165e4ae0","added_by":"auto","created_at":"2024-12-05 08:46:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plots displaying the relative expression of membrane proteins in hBMEC versus hCMEC/D3. \u003c/strong\u003eComparative membrane protein expression in hBMEC Ad-OATP2B1 in respect to hCMEC/D3 Ad-OATP2B1 (A). Emphasis was given to BBB markers (B), ABC transporters (C), and SLC transporters (D). Statistical analysis was performed using an unpaired t-test. Proteins with at least a 2-fold statistically significant difference (P-value ≤ 0.05) in expression are indicated by red or blue dots. Three individual cell culture preparations were conducted. Data from the replicates were averaged. Images created with GraphPad Prism version 10.2.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/3d449e63568feac0fe5a875a.png"},{"id":78690174,"identity":"2ca5e8a3-1533-4be4-b5b4-e38c5370aa59","added_by":"auto","created_at":"2025-03-17 16:14:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1677209,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/df34f15a-7366-4c95-900d-2158b4552ca8.pdf"},{"id":70648720,"identity":"6c2e7b7e-8f0d-4a68-b5cb-587afd7583bd","added_by":"auto","created_at":"2024-12-05 08:46:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":164446,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/daf1b65ab02004a9208ddc8a.docx"},{"id":70649877,"identity":"35935d33-a9f9-49d8-b6d7-c184cc9a9795","added_by":"auto","created_at":"2024-12-05 08:54:51","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1922429,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/b98f971408b1621c959dcbcc.xlsx"},{"id":70648721,"identity":"53a54f91-05be-44ca-98c1-ad4c300c4367","added_by":"auto","created_at":"2024-12-05 08:46:51","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13725,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5388233/v1/2ac41e63fb11be545e250dfe.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeted and untargeted proteomics-based comparison of adenoviral infected hCMEC/D3 and hBMEC as a human blood-brain barrier model to study the OATP2B1 transporter","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe unique environment within the central nervous system (CNS) is crucial for ensuring normal neurological function. A structure that plays a major role in maintaining CNS homeostasis is the blood-brain barrier (BBB). Due to its highly selective permeability, the BBB governs the entrance of substances from the bloodstream into the brain (Masuda et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The neurovascular unit composing the BBB consists of brain capillary endothelial cells (BCECs), pericytes, astrocytes, glial cells and neurons (Lok et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). While each of these cell types plays a role in the overall function of the brain's microvasculature, BCECs are assumed to be responsible for the selective BBB permeability (Urich et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHuman immortalised BCECs are often used as \u003cem\u003ein vitro\u003c/em\u003e models to study the BBB and its properties (Naik \u0026amp; Cucullo, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sivandzade \u0026amp; Cucullo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These models offer advantages, such as ease of use, reproducibility, and the ability to conduct high-throughput screenings. However, they also present limitations, namely the potential for genetic drift, de-differentiation and consequently an incomplete representation of \u003cem\u003ein vivo\u003c/em\u003e BCECs characteristics (Czupalla et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Among the various cell lines employed, the human cell lines hCMEC/D3 and hBMEC have been used and characterised for their utility as \u003cem\u003ein vitro\u003c/em\u003e BBB models. When looking into the literature, greater attention was given to hCMEC/D3, which have been studied for their expression of tight junction proteins, surface receptors and transporters (Masuda et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ohtsuki et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Urich et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), revealing that they maintain the expression of various proteins normally found at the BBB. In addition, permeability of a range of test compounds varying in physicochemical properties and sizes were investigated in hCMEC/D3 (Poller et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). hBMEC have also been evaluated, although less extensively. In particular, a study by Eigenmann \u003cem\u003eet al.\u003c/em\u003e proposed hBMEC as a model to distinguish between permeable and non-permeable molecules at the BBB (Eigenmann et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The two cell lines have also been compared for their use as an \u003cem\u003ein vitro\u003c/em\u003e BBB model, revealing a stronger BBB phenotype in hCMEC/D3 but higher resistance and lower permeability in hBMEC (Eigenmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn essential aspect of the BBB functionality is the presence of membrane transporters, which coordinate the flux of molecules across the BBB by controlling cellular efflux and uptake. More in detail, members of the ATP-binding cassette (ABC) family are responsible for the efflux of their substrates from the cells (Ebinger \u0026amp; Uhr, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), whereas the organic anion-transporting polypeptides (OATPs) are involved in the uptake of drugs and endogenous compounds (Grube et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schafer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among OATPs, OATP2B1 is of particular interest, as it has been detected at the BBB in humans (Al-Majdoub et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and it has been proposed to be involved in the uptake of statins and neurosteroids (Schafer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite that, its precise role remains unclear due to the lack of OATP2B1 expression at the BBB in animal models (Hoshi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and to limitations of human \u003cem\u003ein vitro\u003c/em\u003e BBB models, which may not fully replicate the expression patterns of all BBB transporters (Czupalla et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these limitations, methods such as adenoviral infection have been employed to transiently express specific proteins in cell models (Meyer Zu Schwabedissen et al., 2014). This approach allows the investigation of a specific transporter's function in the respective cellular environment. Of note, a first investigation of hCMEC/D3 and hBMEC as an \u003cem\u003ein vitro\u003c/em\u003e BBB model in which adenoviral expression of OATP2B1 was applied has already been performed in our research group (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, Western blot analysis and transport experiments revealed higher expression and activity of OATP2B1 in hBMEC, respectively, while other features of the BBB cell model were not affected by adenoviral infection. An additional method to characterise the two cell lines and to evaluate the efficacy of the adenoviral infection in expressing protein of interest is the use of proteomics. More in detail, targeted proteomics provides absolute quantification of specific proteins, whereas untargeted proteomics offers a macroscopic analysis of the protein landscape, although in a more relative and less quantitative manner. As a consequence, combining the two approaches may enhance the information obtained and improve the reproducibility of the data (Hart-Smith, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sobsey et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccordingly, the aim of the herein described study was to further evaluate and characterise the Ad-OATP2B1 infected hCMEC/D3 and hBMEC for their use as a human \u003cem\u003ein vitro\u003c/em\u003e BBB model to study the OATP2B1 transporter and to investigate the impact of the adenoviral infection on various proteins known to be of relevance in BCECs. MS-based targeted and untargeted proteomics were applied to hCMEC/D3 and hBMEC either infected with Ad-LacZ as control or Ad-OATP2B1 to test the transient expression of OATP2B1 and to assess the expression of key BBB markers, ABC and solute carrier (SLC) transporters in cells undergoing adenoviral infection.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehBMEC (ScienCell Research Laboratories, Carlsbad, CA; catalogue number: #1000) (Stins et al., 2001) and hCMEC/D3 (RRID:CVCL_U985) were kindly provided by Prof. Matthias Hamburger, Prof. Robin Teufel (Pharmaceutical Biology), and Prof. J\u0026ouml;rg Huwyler (Pharmaceutical Technology) from the Department of Pharmaceutical Sciences, University of Basel, respectively. Both hBMEC and hCMEC/D3 are not authenticated cell lines. Nevertheless, the International Cell Line Authentication Committee has not indicated hBMEC or hCMEC/D3 as a misidentified cell line. Following the previous work conducted by Eigenmann \u003cem\u003eet al.\u003c/em\u003e (Eigenmann et al., 2013), hBMEC were cultured in EGM-2 basal medium supplemented with 20% Fetal Calf Serum (FCS, Thermo-Fisher Scientific, Karlsruhe, Germany), 1% penicillin-streptomycin (Thermo-Fisher Scientific) and SingleQuots kit composed of hydrocortisone, human basic fibroblast growth factor, vascular endothelial growth factor, human insulin like growth factor 1, ascorbic acid, epidermal growth factor, and heparin (CC-4176, Lonza, Basel, Switzerland). hCMEC/D3 were cultured in EGM-2 medium with the addition of 5% FCS, 1% penicillin-streptomycin, 1% HEPES (BioConcept AG, Allschwil, Switzerland), 1% Chemically Defined Lipid Concentrate\u0026trade; (Thermo-Fisher Scientific), human basic fibroblast growth factor, ascorbic acid, and hydrocortisone (Sigma-Aldrich, Buchs, Switzerland) (Taggi et al., 2024). hBMEC used within the study were between passages 15 and 25, while hCMEC/D3 were between passages 28 and 35. Cells were grown at 37\u0026deg;C in a humidified atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e, and were cultured in rat-tail collagen type I (R\u0026amp;D Systems, Minneapolis, Minnesota) coated cultureware.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdenoviral infection of hCMEC/D3 and hBMEC and membrane protein sample preparation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyse protein expression using targeted and untargeted proteomics, hCMEC/D3 and hBMEC were seeded in pre-coated 10 cm culture dishes (Sarstedt, N\u0026uuml;mbrecht, Germany) at a density of 1,8 * 10\u003csup\u003e6\u003c/sup\u003e cells/dish. 24 hours later cells were infected with 200 plaque forming units (pfu) of either Ad-OATP2B1 or Ad-LacZ. The Ad-OATP2B1 was previously produced and characterised (Knauer et al., 2010; Schafer et al., 2018), whereas the Ad-LacZ was provided by the ViraPower\u0026trade; Adenoviral Expression System (Invitrogen, Carlsbad, CA, USA). 72 hours after seeding, cells were harvested in ice-cold extraction buffer I (ProteoExtract\u0026reg; Native Membrane Protein Extraction Kit, Sigma-Aldrich) supplemented with 5 \u0026micro;l/ml of Protease Inhibitor Cocktail (PIC) (P8340, Sigma-Aldrich), transferred in LoBindTubes (Eppendorf, Hamburg, Germany) and incubated for 15 minutes at 4\u0026deg;C while shaking at 100 rpm. Then, samples were centrifuged for 15 minutes at 16000 g and 4\u0026deg;C. The supernatant containing the cytoplasm protein was transferred in separated LoBindTubes, whereas the pellet was resuspended in extraction buffer II (ProteoExtract\u0026reg; Native Membrane Protein Extraction Kit, Sigma-Aldrich) supplemented with 5 \u0026micro;l/ml of PIC and incubated for 60 minutes at 4\u0026deg;C while shaking at 100 rpm, followed by centrifugation for 15 minutes at 16000 g and 4\u0026deg;C. Subsequently, supernatant containing membrane proteins was collected in separated LoBindTubes, whereas the pellet was discarded. Finally, protein content was measured using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific) and the Microplate Reader Tecan Infinite M200 Pro (Tecan, M\u0026auml;nnedorf, Switzerland). Eight distinct cell culture preparations were made for targeted proteomics analysis, while three were prepared for untargeted proteomics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS/MS based targeted proteomics of hBMEC and hCMEC/D3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the isolation of membrane proteins, if necessary membrane fractions were adjusted to a maximum protein concentration of 2 mg/ml using ammonium bicarbonate buffer (50 mM, pH 7.8, Sigma-Aldrich). Next, 50 \u0026micro;l of each membrane fraction were combined with 5 \u0026micro;l of dithiothreitol (200 mM, Sigma-Aldrich, Taufkirchen, Germany), 20 \u0026micro;l of ammonium bicarbonate buffer, and 5 \u0026micro;l of acetonitrile (Thermo Fisher Scientific) and incubated at 60\u0026deg;C for 20 minutes. Cooling down the samples at room temperature (RT) was followed by the addition of 5 \u0026micro;l iodoacetamide (400 mM, Sigma-Aldrich) and a further incubation at 37\u0026deg;C for 15 minutes. For protein digestion, 5 \u0026micro;l of trypsin (trypsin/protein ratio: 1/40, Promega, Mannheim, Germany) was added and the mixture was incubated at 37\u0026deg;C for 16 hours. Then, 10 \u0026micro;l of formic acid (10% v/v, Sigma-Aldrich) was added to stop the digestion process. The samples were subsequently centrifuged at 16000 g and 4\u0026deg;C for 15 minutes. From the resulting supernatant, 40 \u0026micro;l were taken and mixed with 40 \u0026micro;l of an isotope-labelled internal standard peptide mix (10 nM of each labelled peptide, Thermo Fisher Scientific). Analogous to a previously validated method (Groer et al., 2013), a 7500 QTRAP triple quadrupole mass spectrometer (AB Sciex, Darmstadt, Germany) coupled to an Agilent Technologies 1260 Infinity system (Agilent Technologies, Santa Clara, California) was used for protein quantification. Throughout the analysis, both precision (CV) and accuracy (error) were below 20%. The lower limit of quantification (LLOQ) was0.04 nmol/L and values below LLOQ were excluded from the analysis. Final protein abundance data were normalised to the total protein amount of the isolated membrane fraction, as measured by the BCA assay. Thus, the protein-normalised LLOQ was 0.02 pmol/mg. Protein amount of PECAM1 (UniProtKB-ID: P16284), CDH5 (P33151), OCLN (Q16625), TFRC (P02786), GLUT1 (P11166), ENT1 (Q99808), Pgp (P08183), BCRP (Q9UNQ0), MRP1 (P33527), MRP4 (O15439), MCT1 (P53985), OAT2 (Q9Y694), OAT3 (Q8TCC7), OAT7 (Q8IVM8), OATP1A2 (P46721), OATP2B1 (O94956), OCT1 (O15245), OCT3 (O75751) and Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e-ATPase (P05023) was quantified. The proteomic data have been submitted to the ProteomeXchange Consortium (https://www.proteomexchange.org/) through the MassIVE partner repository, using the MassIVE dataset identifier MSV000096107 and the ProteomeXchange identifier PXD056849. The targeted proteomics analysis via LC-MS/MS was funded by the German Research Foundation (project number: 505943254).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of peptides and MRMs for protein analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003epredictions, peptides specific for the proteins mentioned above were identified applying a method discussed elsewhere(Oswald et al., 2013)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eFirst, protein sequences taken from the UniProtKB/Swiss-Prot databasewere subjected to\u003cem\u003e\u0026nbsp;in silico\u0026nbsp;\u003c/em\u003etrypsin digestion (www.expasy.org/tools)\u003cem\u003e,\u0026nbsp;\u003c/em\u003eleading to the generation of 7-25 amino acids peptide sequences, in which any missed cleavage site was excluded.Moreover, the following exclusion criteria were applied: peptides located in transmembrane regions, carrying N-terminal glutamic acid, methionine, cysteine or tryptophan, containing non-synonymousgenetic polymorphisms with a frequency higher than 1%,or subjected to experimentally proven post-translational modifications.The specificity of each observed peptide was validated using an NCBI protein BLAST search against the UniProtKB/Swiss-Prot database.To establish the best multiple reaction monitoring (MRM) methods for the top peptides, the most suitable mass transitions were identified and fine-tuned.This was achieved by manually infusing synthetic peptides along with their stable isotope-labelled versions into a 7500 QTRAP triple quadrupole mass spectrometer (AB Sciex). For each peptide, the four mass transitions with the highest signal intensity were selected. \u003cstrong\u003eTable 1\u003c/strong\u003e provides a summary of all proteotypic selected peptides and their optimised mass transitions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eOverview of the selected proteotypic peptides and their corresponding mass transitions used in targeted proteomic analysis. The amino acid residue at the C-terminus (R or K) of the stable isotope-labelled peptide is marked with an asterisk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehBMEC and hCMEC/D3 membrane protein digest and untargeted proteomics analysis\u003cem\u003e\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eSubsequent to the isolation of membrane proteins, samples were resuspended in 5% SDS, 10 mM Tris(2-carboxyethyl)phosphine hydrochloride (TCEP, Sigma-Aldrich), 0.1 M Triethylammonium bicarbonate (TEAB) and incubated for 10 minutes at 95\u0026deg;C while shaking at 500 rpm. Proteins were alkylated in 20 mM iodoacetamide for 30 minutes at 25\u0026deg;C and digested using S-Trap\u0026trade; micro spin columns (Protifi, New York, USA) according to the manufacturer\u0026rsquo;s instructions. Shortly, 12% phosphoric acid was added to each sample (final concentration of phosphoric acid 1.2%) followed by the addition of S-trap buffer (90% methanol, 100 mM TEAB pH 7.1) at a ratio of 6:1. Samples were mixed by vortexing and loaded onto S-trap columns by centrifugation at 4000 g for 1 minute followed by three washes with S-trap buffer. Digestion buffer (50 mM TEAB pH 8.0) containing sequencing-grade modified trypsin (Promega) was added to the S-trap column and samples were incubated for 1 hour at 47 \u0026deg;C. Peptides were eluted by the consecutive addition and collection by centrifugation at 4000 g for 1 minute of 40 ul digestion buffer, 40 ul of 0.2% formic acid and finally 35 ul 50% acetonitrile, 0.2% formic acid. Samples were dried under vacuum and stored at -20 \u0026deg;C. The day of the analysis, dried peptides were resuspended in 0.1% aqueous formic acid, 0.02% DDM (n-Dodecyl-B-D-maltoside) and subjected to LC\u0026ndash;MS/MS analysis using a timsTOF Ultra Mass Spectrometer (Bruker, F\u0026auml;llanden, Switzerland) equipped with a CaptiveSpray nano-electrospray ion source (Bruker) and fitted with a Vanquish Neo (Thermo Fisher Scientific). Peptides were resolved using a RP-HPLC column (100 \u0026micro;m \u0026times; 30 cm) packed in-house with C18 resin (ReproSil Saphir 100 C18, 1.5 \u0026micro;m resin; Dr. Maisch GmbH, Ammerbuch, Germany) at a flow rate of 0.4 \u0026micro;l/min and column heater set to 60\u0026deg;C. The following gradient was used for peptide separation: from 2% B to 25% B over 25 minutes to 35% B over 5 minutes to 95% B over 1 minute followed by 5 minutes at 95% B to 2% B over 1 minute followed by 3 minutes at 2% B. Buffer A was 0.1% formic acid in water and buffer B was 80% acetonitrile, 0.1% formic acid in water. The mass spectrometer was operated in dia-PASEF mode with a cycle time estimate of 0.95 s. MS1 and MS2 scans were acquired over a mass range from 100 to 1700 m/z. A method with 8 dia-PASEF scans separated into 3 ion mobility windows per scan covering a 400-1000 m/z range with 25 Da windows and an ion mobility range from 0.64 to 1.37 Vs cm\u003csup\u003e2\u003c/sup\u003e was used. Accumulation and ramp time were set to 100 ms, capillary voltage was set to 1600V, dry gas was set to 3 l/min and dry temperature was set to 200 \u0026deg;C. The collision energy was ramped linearly as a function of ion mobility from 59 eV at 1/K0 = 1.6 V s cm\u003csup\u003e-2\u003c/sup\u003e to 20 eV at 1/K0 = 0.6 V s cm\u003csup\u003e-2\u003c/sup\u003e. The acquired files were searched using the Spectronaut (Biognosys v18.6, Schlieren, Switzerland) directDIA workflow against a Homo sapiens database (consisting of 20360 protein sequences downloaded from Uniprot on 20220222) and 393 commonly observed contaminants using default settings. Proteomic data have been deposited to the ProteomeXchange Consortium (https://www.proteomexchange.org/) via the MassIVE partner repository with MassIVE data set identifier MSV000096107 and ProteomeXchange identifier PXD056849.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe targeted proteomics data were analysed using GraphPad Prism version 10.2 (GraphPad Software, La Jolla, California). Statistical significance was set at a P-value of \u0026le; 0.05. One-way ANOVA followed by Tukey\u0026rsquo;s post-hoc test for multiple comparisons or unpaired t-test were conducted. The data were not checked for normality, and no outlier tests were performed. For untargeted proteomics analysis, quantitative fragment ion data (F.Area) was exported from Spectronaut and analysed using the MSstats R package v.4.9.9. (https://doi.org/10.1093/bioinformatics/btu305). Data was normalised using the default normalisation option \u0026ldquo;equalizedMedians\u0026rdquo;, imputed using \u0026ldquo;AFT model-based imputation\u0026rdquo; and P-values for pairwise comparisons were calculated as implemented in MSstats. To identify expression differences, we defined our selection criteria for differentially expressed proteins as a minimum 2-fold change with statistical significance (P-value \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAbsolute quantification of BBB markers in adenoviral infected hCMEC/D3 and hBMEC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn important feature of a BBB cell model is the presence of endothelial proteins, tight junctions and receptors (Naik \u0026amp; Cucullo, 2012). Following a previous study, where we reported on the expression and functionality of OATP2B1 in adenoviral infected hCMEC/D3 and hBMEC (Taggi et al., 2024), we conducted targeted proteomics in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1, and hBMEC Ad-OATP2B1, comparing the expression of BBB markers between the two cell lines and evaluating the effects of the adenoviral infection on their expression. As shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e, the endothelial markers PECAM1 and CDH5 were detected only in hCMEC/D3, in which the protein abundance was found to be comparable between Ad-LacZ and Ad-OATP2B1 infected cells. Conversely, although not affected by the adenoviral infection, the protein level of the tight junction OCLN was above the lower limit of quantification (LLOQ) only in hBMEC. Finally, protein expression of the BBB receptor TFRC was measurable and comparable in both hCMEC/D3 and hBMEC independently from the adenoviral infection. As control, Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e-ATPase protein abundance was also evaluated.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e reports the values below LLOQ measured in the two cell lines for the above mentioned BBB markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. BBB markers under limit of quantification.\u0026nbsp;\u003c/strong\u003eAbsolute protein abundance below lower limit of quantification (LLOQ) of PECAM1 and CDH5 in hBMEC Ad-LacZ and hBMEC Ad-OATP2B1 and of OCLN in hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1. Values are expressed as pmol/ml. Results are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of n=8.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeted proteomics quantification of efflux transporters in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key feature of a robust \u003cem\u003ein vitro\u003c/em\u003e BBB cell model is the expression of efflux transporters, as these proteins play an important role in the selective permeability that characterises the BBB. Among them, Pgp, BCRP and multidrug resistance proteins (MRPs) are prominently expressed at the BBB (Begley, 2004). Accordingly, following the quantification of BBB endothelial, tight junction and receptor proteins, we wanted to compare the two cell lines for their protein level of efflux transporters and to verify that the adenoviral induced expression of OATP2B1 did not modify their expression levels. As displayed in \u003cstrong\u003eFigure 2\u003c/strong\u003e, targeted proteomics analysis showed that the abundance of Pgp was comparable between hCMEC/D3 and hBMEC independently from the adenoviral infection. Conversely, BCRP expression was measurable in hBMEC Ad-LacZ and hBMEC Ad-OATP2B1 only, whereas hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1 levels were found to be below LLOQ (\u003cstrong\u003eFigure 2\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eTable 3\u003c/strong\u003e). Finally, the protein quantification of MRPs revealed that MRP1 and MRP4 protein abundance was comparable between hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1, and hBMEC Ad-OATP2B1 (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. BCRP protein expression in hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1.\u0026nbsp;\u003c/strong\u003eBelow LLOQ absolute protein abundance of BCRP [pmol/ml] in hCMEC/D3 and hCMEC/D3 Ad-OATP2B1. Results are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of n=8.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeted proteomics analysis of SLC transporters following adenoviral infection in hCMEC/D3 and hBMEC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApart from OATP2B1, other SLC transporters are essential at the BBB for the permeation of compounds that play a role in the physiological function of the brain, such as glucose, nucleosides, thyroid hormones and neurosteroids (Taggi et al., 2022; Vatine et al., 2017). Consequently, to broaden the comparison of the two cell lines for their applicability as a BBB cell model and to confirm that the Ad-OATP2B1 infection led to a selectively enhanced expression of OATP2B1, we also evaluated the protein level of other SLC transporters with a major role at the BBB.\u003cem\u003e\u0026nbsp;\u003c/em\u003eAs shown in \u003cstrong\u003eFigure 3\u003c/strong\u003e, the expression of GLUT1 and ENT1 was comparable among hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. Similar results were found for MCT1, whose protein abundance did not differ in the BBB models (\u003cstrong\u003eFigure 3\u003c/strong\u003e and \u003cstrong\u003eTable 4\u003c/strong\u003e).\u003cem\u003e\u0026nbsp;\u003c/em\u003eWhen focusing on OATs and OCTs, diverse results emerged. OAT7 abundance was similar between hCMEC/D3 and hBMEC, regardless of adenoviral infection (\u003cstrong\u003eFigure 3\u003c/strong\u003e). In contrast, the protein levels of OAT2, OAT3, OCT1, and OCT3 were either below LLOQ or undetectable. Similarly, OATP1A2 was found to be below LLOQ or not detected in both cell lines, independent of infection status (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003cem\u003e\u0026nbsp;\u003c/em\u003eAs one of the main goals of this study was to compare the expression of the OATP2B1 transporter in hCMEC/D3 and hBMEC following Ad-OATP2B1 infection, two distinct peptides - SSPAVEQQLLVSGPGK and YYNNDLLR - were used for quantification. Despite previous confirmation of increased OATP2B1 expression in both cell lines after adenoviral infection via Western blot analysis (Taggi et al., 2024), OATP2B1 abundance for both peptides was found to be below LLOQ in hCMEC/D3 and hBMEC, regardless of adenoviral infection \u003cstrong\u003e(Table 4)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. SLC transporters below LLOQ in hCMEC/D3 Ad-LacZ, hBMEC Ad-LacZ, hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1.\u0026nbsp;\u003c/strong\u003eAbsolute protein abundance of OAT2, OAT3, OCT1, OCT3, OATP1A2, and OATP2B1 below LLOQ in hCMEC/D3 and hBMEC either infected with Ad-LacZ or Ad-OATP2B1. The data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD in pmol/ml, with a total sample size of n=8. ND= not detected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUntargeted proteomics analysis of hCMEC/D3 and hBMEC infected with Ad-LacZ and Ad-OATP2B1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn effective way to describe and compare hCMEC/D3 and hBMEC is the use of untargeted proteomics analysis. While this approach does not quantify absolute protein levels, it provides a broad characterization of the two cell lines. At first, we wanted to verify the efficacy of the Ad-OATP2B1 infection in selectively increasing the expression of the transporter without modifying the level of other key BBB proteins. Consequently, we performed a comparison within the same cell line but between Ad-LacZ and Ad-OATP2B1 infected cells. As reported in \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e, we identified 6690 proteins in hCMEC/D3 and 6860 proteins in hBMEC. A generally lower variation in protein level and no protein with a biologically meaningful different expression level was found in hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1, whereas a higher variation in expression could be observed when comparing hBMEC Ad-LacZ and hBMEC Ad-OATP2B1, mainly for proteins related to the cell cycle, the adenoviral infection and the subsequent DNA modification (\u003cstrong\u003eFigure 4A\u003c/strong\u003e and \u003cstrong\u003e4B\u003c/strong\u003e). Specifically, the level of GEN1 (involved in Holliday junction resolution, DNA repair, meiosis and regulation of the cell cycle (Chan \u0026amp; West, 2015; Ip et al., 2008; Lee et al., 2015)) was increased in hBMEC Ad-OATP2B1, whereas in hBMEC Ad-LacZ enhanced protein expression was found for ITB3 (partaking in cell signalling (Mori et al., 2008; Saegusa et al., 2009)), LRRN4 (contributing to cell adhesion and signal transduction (Bando et al., 2013; Chen et al., 2015)), ABRAL (active in cell migration (Fan et al., 2021)), S100A6 (associated with cell proliferation and differentiation (Li et al., 2015)), and SPB5 (connected to cell signalling (Bonuccelli et al., 2009)). In addition, the analysis revealed that the Ad-OATP2B1 infection increased the expression of OATP2B1 in both hCMEC/D3 and hBMEC but did not modify the expression of BBB markers, ABC and SLC transporters (\u003cstrong\u003eFigure 4C\u003c/strong\u003e and \u003cstrong\u003e4D\u003c/strong\u003e). Importantly, although OATP2B1 protein level was enhanced in both cell lines, the difference in OATP2B1 level between Ad-LacZ infected and Ad-OATP2B1 infected cells was statistically significant in hBMEC only. The list of selected BBB markers, ABC and SLC transporters is reported in \u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of Ad-OATP2B1 infected hCMEC/D3 and hBMEC by untargeted proteomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter confirming that Ad-OATP2B1 infection increased OATP2B1 expression without affecting the protein levels of key BBB markers, we proceeded \u0026nbsp;with comparing hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. One major goal of this study is to evaluate these two cell lines as a human \u003cem\u003ein vitro\u003c/em\u003e model for studying OATP2B1 at the BBB. Although the analysis revealed 6498 shared proteins (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e), the expression of several proteins differed between hBMEC Ad-OATP2B1 and hCMEC/D3 Ad-OATP2B1 (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). More in detail, a thorough assessment of the two cell lines displayed that hCMEC/D3 Ad-OATP2B1 had a higher protein expression of the BBB markers B2M, PECAM1, CDH5, FCGRT, LDLR and VLDLR (\u003cstrong\u003eFigure 5B\u003c/strong\u003e). Furthermore, hCMEC/D3 Ad-OATP2B1 showed enhanced protein level of the ABC transporters Pgp and MRP1, whereas hBMEC Ad-OATP2B1 had an augmented level of ABC50 (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). Finally, when observing the expression of SLC transporters, higher expression was observed for various SLC transporters in hCMEC/D3 Ad-OATP2B1, whereas only OATP4A1, S22AI and ZIP8 were elevated in hBMEC Ad-OATP2B1 (\u003cstrong\u003eFigure 5D\u003c/strong\u003e). Of note, although not statistically significant, OATP2B1 expression was higher in hBMEC Ad-OATP2B1 compared to hCMEC/D3 Ad-OATP2B1.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we characterised and compared the applicability of Ad-OATP2B1 infected hCMEC/D3 and hBMEC as a human \u003cem\u003ein vitro\u003c/em\u003e BBB cell model to study the OATP2B1 transporter. For this, we determined the change in OATP2B1 expression after adenoviral gene transfer in the two cell lines and focused on investigating the influence of the adenoviral infection on the expression of key BBB proteins. A robust BBB cell model should express a broad spectrum of proteins normally found at the BBB \u003cem\u003ein vivo\u003c/em\u003e, such as endothelial markers, tight junction proteins, receptors, efflux and uptake transporters (Naik \u0026amp; Cucullo, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Performing targeted proteomics, hCMEC/D3 and hBMEC were compared for their expression of 18 proteins among BBB markers, efflux, and uptake transporters. Independently from the adenoviral infection, hCMEC/D3 cells showed a higher expression of the endothelial markers PECAM1 and CDH5, whereas hBMEC levels of these endothelial markers were below LLOQ. However, the expression of TFRC was comparable between the two cell lines. These results are in line with a previous study performed in our research group, in which the expression of BBB markers was evaluated performing real-time quantitative PCR and Western blot analysis (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), further pointing to the tendency of hCMEC/D3 cells to better preserve the BBB endothelial phenotype compared to hBMEC cells. Conversely, the expression of the tight junction protein OCLN was above LLOQ only in hBMEC. As OCLN is involved in cell permeability (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), its higher expression may explain the previously observed greater tightness of hBMEC compared to hCMEC/D3 (Eigenmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEfflux transporters are critical for maintaining the selective permeability of the BBB, as they are involved in the protection of the brain against toxic compounds (Taggi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast to what we showed in our previous study (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), targeted proteomics revealed comparable levels of Pgp in hCMEC/D3 and hBMEC either infected with Ad-LacZ or Ad-OATP2B1. On the contrary, the BCRP level was found to be below LLOQ in hCMEC/D3 Ad-lacZ and hCMEC/D3 Ad-OATP2B1. These results are also in opposition to previous literature (Masuda et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ohtsuki et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in which BCRP expression was shown in hCMEC/D3 by RT-qPCR, Western blot and proteomic analyses. One potential explanation for the below LLOQ level of BCRP observed in this study could be the low protein concentrations obtained from the hCMEC/D3 Ad-LacZ and hCMEC/D3 Ad-OATP2B1 samples. When protein concentrations are low, the resulting samples may not provide sufficient material for accurate detection. Additionally, the subsequent dilution that occurs during the analysis by the LC-MS/MS device may further contribute to this issue. This dilution can further reduce the concentration of BCRP in the final sample, making it more difficult to quantify it accurately (Wilson et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Thus, both the initial protein concentration and the dilution occurring during the analysis likely play a role in the observed low LLOQ for BCRP in this study.\u003c/p\u003e \u003cp\u003eIn the next step, we evaluated uptake transporters, for which targeted proteomics-based characterization revealed comparable levels of GLUT1, ENT1, MCT1, and OAT7 among hCMEC/D3 and hBMEC independently of the adenoviral infection. The expression of OAT2, OAT3, OCT1, OCT3, and OATP1A2, however, was found to be below LLOQ in both cell lines regardless of the adenoviral infection. Importantly, applying this proteomics analysis we were unable to quantify OATP2B1 in hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. This finding contrasts with the results observed performing untargeted proteomics, which showed a significant increase in OATP2B1 expression in hBMEC following Ad-OATP2B1 infection, while no significant change was observed in hCMEC/D3. Such results are in line with our previous study, in which performing Western blot analysis and transport assay enhanced OATP2B1 expression and functionality could be observed only in hBMEC following Ad-OATP2B1 infection (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The lack of OATP2B1 detection in targeted proteomics can be attributed to several factors. Despite applying exclusion criteria during the selection of proteotypic peptides, our inability to detect OATP2B1 may still be due to post-translational modifications or artifactual chemical changes during sample processing (Lange et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the targeted proteomics the peptides YYNNDLLR and SSPAVEQQLLVSGPGK were used for the measurement of OATP2B1 protein amount. However, performing untargeted proteomics only YYNNDLLR was detected within the OATP2B1 identifying peptides. These findings suggest post-translational modification of the SSPAVEQQLLVGSPGK peptide. In fact, using the NetPhos predictor (Blom et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), we identified a phosphorylation probability exceeding 99% for this peptide. In addition, the inability to detect YYNNDLLR during targeted proteomics may be attributed to variations in sample preparation methods (Masuda et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wegler et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Specifically, only in the preparation of untargeted proteomics the samples were dried and resuspended, thereby increasing their concentration.\u003c/p\u003e \u003cp\u003eThe elements discussed above to explain the differences in OATP2B1 measurement among the two employed methods can be also applied to understand the further discrepancies found between targeted and untargeted proteomics comparison of hCMEC/D3 Ad-OATP2B1 and hBMEC Ad-OATP2B1. More in detail, untargeted proteomics comparison showed enhanced expression of BBB markers in hCMEC/D3 Ad-OATP2B1 when compared to hBMEC Ad-OATP2B1, in line with what was observed in the targeted proteomics analysis. On the contrary, evaluation by untargeted proteomics displayed higher levels of the ABC transporters Pgp and MRP1 and of several SLC transporters in hCMEC/D3 Ad-OATP2B1, thus further emphasising the stronger BBB phenotype of this cell line. Of note, despite not being statistically significant, OATP2B1 expression was higher in hBMEC Ad-OATP2B1 when compared to hCMEC/D3 Ad-OATP2B1, which is in line with our previous comparison study (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, the broader characterization of the two cell lines provided by the untargeted proteomics analysis revealed a higher variation in the expression of proteins related to the cell cycle and viral infection in hBMEC, thus suggesting a more pronounced cellular response to adenoviral infection. Despite that, the expression levels of other BBB markers, ABC, and SLC transporters remained unchanged in both cell lines, confirming the specificity of the adenoviral system for OATP2B1 without broadly altering the BBB phenotype.\u003c/p\u003e \u003cp\u003eThis project intended to continue the characterization and comparison of hCMEC/D3 and hBMEC for their applicability as a cell model to study transport across the BBB (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Taken together, our results indicate that hCMEC/D3 have a more solid BBB phenotype, making them usable as a BBB model to study the function of transporters endogenously expressed in this cell line. In fact, hCMEC/D3 have been previously employed to investigate Pgp and BCRP regulation at the BBB (Poller et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Considering the low efficacy of Ad-OATP2B1 infection in increasing OATP2B1 expression and functionality (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), a potential alternative method to drive protein expression in hCMEC/D3 might be the use of lentiviral infection, as previously performed by Gericke \u003cem\u003eet al.\u003c/em\u003e for the expression of claudin-5 (Gericke et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, our current and previous findings (Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), along with studies from other groups (Balzer et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Eigenmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), suggest that hCMEC/D3 cells exhibit leakiness, posing a significant challenge given the need for a tight cell monolayer in cellular permeability studies. On the contrary, hBMEC showed higher tightness (Eigenmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Taggi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and have been previously used as a model to predict the permeability of compounds at the BBB (Eigenmann et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, their stronger response to adenoviral-induced modification suggests that hBMEC protein expression can be more easily modified when compared to hCMEC/D3. Nevertheless, hBMEC showed a lower level of endothelial markers and transporters and consequently have a weaker BBB phenotype than hCMEC/D3.\u003c/p\u003e \u003cp\u003eIn summary, the discrepancies found between targeted and untargeted proteomics highlight the importance of employing orthogonal methods to validate protein expression levels. Despite that, we can conclude that the adenoviral infection can be used to selectively express transporters of interest without modifying the expression of BBB markers, ABC and SLC transporters in hCMEC/D3 and hBMEC, although the efficacy of this technique is higher in hBMEC. Therefore, it is crucial to carefully assess the efficacy of the selected transfection method to ensure it effectively manipulates cells and meets the specific requirements of the study. Based on these considerations, although with their limitations, we still suggest that both hCMEC/D3 and hBMEC can be used as a human cell model to study transport at the BBB. However, it is essential to select the model that best aligns with the specific requirements of the research question at hand. Doing so will enhance the validity of the findings and ensure that the results accurately reflect the phenomena being investigated.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABC: ATP-binding cassette\u003c/p\u003e\n\u003cp\u003eBBB: Blood-brain barrier\u003c/p\u003e\n\u003cp\u003eBCECs: Brain capillary endothelial cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCNS: Central nervous system\u003c/p\u003e\n\u003cp\u003eDDM: n-dodecyl-B-D-maltoside\u003c/p\u003e\n\u003cp\u003eFCS: Fetal calf serum\u003c/p\u003e\n\u003cp\u003eLLOQ: Lower limit of quantification\u003c/p\u003e\n\u003cp\u003eMRM: Multiple reaction monitoring\u003c/p\u003e\n\u003cp\u003eMRPs: Multidrug resistance protein\u003c/p\u003e\n\u003cp\u003eOATPs: Organic anion-transporting polypeptides\u003c/p\u003e\n\u003cp\u003ePIC: Protease inhibitor cocktail\u003c/p\u003e\n\u003cp\u003eRT: Room temperature\u003c/p\u003e\n\u003cp\u003eSLC: Solute carrier\u003c/p\u003e\n\u003cp\u003eTEAB: Triethylammonium bicarbonate\u003c/p\u003e\n\u003cp\u003eTCEP: Tris(2-carboxyethyl)phosphine hydrochloride\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the Biopharmacy at the University of Basel, by the Swiss 3R Competence Center, and by the German Research Foundation (project number: 505943254).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests with the contents of this manuscript. This manuscript will be part of the PhD thesis of Valerio Taggi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValerio Taggi: contributed to the conception and design of the work, acquisition, analysis and interpretation of data, draft and revision of the work.\u003c/p\u003e\n\u003cp\u003eAnima Magdalena Schäfer: contributed to the conception and design of the work, acquisition, analysis and interpretation of data, draft and revision of the work.\u003c/p\u003e\n\u003cp\u003eJonny Hanna Kinzi: contributed to the analysis and interpretation of data, draft and revision of the work.\u003c/p\u003e\n\u003cp\u003eDanilo Ritz: contributed to the acquisition, analysis and interpretation of data, revision of the work.\u003c/p\u003e\n\u003cp\u003eIsabell Seibert: contributed to the acquisition, analysis and interpretation of data.\u003c/p\u003e\n\u003cp\u003eStefan Oswald: contributed to the acquisition, analysis and interpretation of data, revision of the work.\u003c/p\u003e\n\u003cp\u003eHenriette E. Meyer zu Schwabedissen: contributed to the conception and design of the work, interpretation of data, draft and revision of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proteomic data have been submitted to the ProteomeXchange Consortium (https://www.proteomexchange.org/) through the MassIVE partner repository, using the MassIVE dataset identifier MSV000096107 and the ProteomeXchange identifier PXD056849. Additionally, the data supporting the findings of this study are available upon reasonable request from the corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Majdoub, Z. M., Al Feteisi, H., Achour, B., Warwood, S., Neuhoff, S., Rostami-Hodjegan, A., \u0026amp; Barber, J. (2019). Proteomic Quantification of Human Blood-Brain Barrier SLC and ABC Transporters in Healthy Individuals and Dementia Patients. \u003cem\u003eMol Pharm\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(3), 1220-1233. https://doi.org/10.1021/acs.molpharmaceut.8b01189 \u003c/li\u003e\n\u003cli\u003eBalzer, V., Poc, P., Puris, E., Martin, S., Aliasgari, M., Auriola, S., \u0026amp; Fricker, G. (2022). Re-evaluation of the hCMEC/D3 based in vitro BBB model for ABC transporter studies. \u003cem\u003eEur J Pharm Biopharm\u003c/em\u003e,\u003cem\u003e 173\u003c/em\u003e, 12-21. https://doi.org/10.1016/j.ejpb.2022.02.017 \u003c/li\u003e\n\u003cli\u003eBando, T., Morikawa, Y., Hisaoka, T., Komori, T., Miyajima, A., \u0026amp; Senba, E. (2013). Dynamic expression pattern of leucine-rich repeat neuronal protein 4 in the mouse dorsal root ganglia during development. \u003cem\u003eNeurosci Lett\u003c/em\u003e,\u003cem\u003e 548\u003c/em\u003e, 73-78. https://doi.org/10.1016/j.neulet.2013.05.024 \u003c/li\u003e\n\u003cli\u003eBegley, D. J. (2004). ABC transporters and the blood-brain barrier. \u003cem\u003eCurr Pharm Des\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(12), 1295-1312. https://doi.org/10.2174/1381612043384844 \u003c/li\u003e\n\u003cli\u003eBlom, N., Gammeltoft, S., \u0026amp; Brunak, S. (1999). Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. \u003cem\u003eJ Mol Biol\u003c/em\u003e,\u003cem\u003e 294\u003c/em\u003e(5), 1351-1362. https://doi.org/10.1006/jmbi.1999.3310 \u003c/li\u003e\n\u003cli\u003eBonuccelli, G., Casimiro, M. C., Sotgia, F., Wang, C., Liu, M., Katiyar, S., Zhou, J., Dew, E., Capozza, F., Daumer, K. M., Minetti, C., Milliman, J. N., Alpy, F., Rio, M. C., Tomasetto, C., Mercier, I., Flomenberg, N., Frank, P. G., Pestell, R. G., \u0026amp; Lisanti, M. P. (2009). Caveolin-1 (P132L), a common breast cancer mutation, confers mammary cell invasiveness and defines a novel stem cell/metastasis-associated gene signature. \u003cem\u003eAm J Pathol\u003c/em\u003e,\u003cem\u003e 174\u003c/em\u003e(5), 1650-1662. https://doi.org/10.2353/ajpath.2009.080648 \u003c/li\u003e\n\u003cli\u003eChan, Y. W., \u0026amp; West, S. (2015). GEN1 promotes Holliday junction resolution by a coordinated nick and counter-nick mechanism. \u003cem\u003eNucleic Acids Res\u003c/em\u003e,\u003cem\u003e 43\u003c/em\u003e(22), 10882-10892. https://doi.org/10.1093/nar/gkv1207 \u003c/li\u003e\n\u003cli\u003eChen, C., Bartenhagen, C., Gombert, M., Okpanyi, V., Binder, V., Rottgers, S., Bradtke, J., Teigler-Schlegel, A., Harbott, J., Ginzel, S., Thiele, R., Husemann, P., Krell, P. F., Borkhardt, A., Dugas, M., Hu, J., \u0026amp; Fischer, U. (2015). Next-generation-sequencing of recurrent childhood high hyperdiploid acute lymphoblastic leukemia reveals mutations typically associated with high risk patients. \u003cem\u003eLeuk Res\u003c/em\u003e,\u003cem\u003e 39\u003c/em\u003e(9), 990-1001. https://doi.org/10.1016/j.leukres.2015.06.005 \u003c/li\u003e\n\u003cli\u003eCzupalla, C. J., Liebner, S., \u0026amp; Devraj, K. (2014). In vitro models of the blood-brain barrier. \u003cem\u003eMethods Mol Biol\u003c/em\u003e,\u003cem\u003e 1135\u003c/em\u003e, 415-437. https://doi.org/10.1007/978-1-4939-0320-7_34 \u003c/li\u003e\n\u003cli\u003eEbinger, M., \u0026amp; Uhr, M. (2006). ABC drug transporter at the blood-brain barrier: effects on drug metabolism and drug response. \u003cem\u003eEur Arch Psychiatry Clin Neurosci\u003c/em\u003e,\u003cem\u003e 256\u003c/em\u003e(5), 294-298. https://doi.org/10.1007/s00406-006-0664-4 \u003c/li\u003e\n\u003cli\u003eEigenmann, D. E., Jahne, E. A., Smiesko, M., Hamburger, M., \u0026amp; Oufir, M. (2016). Validation of an immortalized human (hBMEC) in vitro blood-brain barrier model. \u003cem\u003eAnal Bioanal Chem\u003c/em\u003e,\u003cem\u003e 408\u003c/em\u003e(8), 2095-2107. https://doi.org/10.1007/s00216-016-9313-6 \u003c/li\u003e\n\u003cli\u003eEigenmann, D. E., Xue, G., Kim, K. S., Moses, A. V., Hamburger, M., \u0026amp; Oufir, M. (2013). Comparative study of four immortalized human brain capillary endothelial cell lines, hCMEC/D3, hBMEC, TY10, and BB19, and optimization of culture conditions, for an in vitro blood-brain barrier model for drug permeability studies. \u003cem\u003eFluids Barriers CNS\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(1), 33. https://doi.org/10.1186/2045-8118-10-33 \u003c/li\u003e\n\u003cli\u003eFan, S., Chen, P., \u0026amp; Li, S. (2021). miR-145-5p Inhibits the Proliferation, Migration, and Invasion of Esophageal Carcinoma Cells by Targeting ABRACL. \u003cem\u003eBiomed Res Int\u003c/em\u003e,\u003cem\u003e 2021\u003c/em\u003e, 6692544. https://doi.org/10.1155/2021/6692544 \u003c/li\u003e\n\u003cli\u003eGao, B., Vavricka, S. R., Meier, P. J., \u0026amp; Stieger, B. (2015). Differential cellular expression of organic anion transporting peptides OATP1A2 and OATP2B1 in the human retina and brain: implications for carrier-mediated transport of neuropeptides and neurosteriods in the CNS. \u003cem\u003ePflugers Arch\u003c/em\u003e,\u003cem\u003e 467\u003c/em\u003e(7), 1481-1493. https://doi.org/10.1007/s00424-014-1596-x \u003c/li\u003e\n\u003cli\u003eGericke, B., Romermann, K., Noack, A., Noack, S., Kronenberg, J., Blasig, I. E., \u0026amp; Loscher, W. (2020). A face-to-face comparison of claudin-5 transduced human brain endothelial (hCMEC/D3) cells with porcine brain endothelial cells as blood-brain barrier models for drug transport studies. \u003cem\u003eFluids Barriers CNS\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(1), 53. https://doi.org/10.1186/s12987-020-00212-5 \u003c/li\u003e\n\u003cli\u003eGroer, C., Bruck, S., Lai, Y., Paulick, A., Busemann, A., Heidecke, C. D., Siegmund, W., \u0026amp; Oswald, S. (2013). LC-MS/MS-based quantification of clinically relevant intestinal uptake and efflux transporter proteins. \u003cem\u003eJ Pharm Biomed Anal\u003c/em\u003e,\u003cem\u003e 85\u003c/em\u003e, 253-261. https://doi.org/10.1016/j.jpba.2013.07.031 \u003c/li\u003e\n\u003cli\u003eGrube, M., Hagen, P., \u0026amp; Jedlitschky, G. (2018). Neurosteroid Transport in the Brain: Role of ABC and SLC Transporters. \u003cem\u003eFront Pharmacol\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e, 354. https://doi.org/10.3389/fphar.2018.00354 \u003c/li\u003e\n\u003cli\u003eHart-Smith, G. (2020). Combining Targeted and Untargeted Data Acquisition to Enhance Quantitative Plant Proteomics Experiments. \u003cem\u003eMethods Mol Biol\u003c/em\u003e,\u003cem\u003e 2139\u003c/em\u003e, 169-178. https://doi.org/10.1007/978-1-0716-0528-8_13 \u003c/li\u003e\n\u003cli\u003eHoshi, Y., Uchida, Y., Tachikawa, M., Inoue, T., Ohtsuki, S., \u0026amp; Terasaki, T. (2013). Quantitative atlas of blood-brain barrier transporters, receptors, and tight junction proteins in rats and common marmoset. \u003cem\u003eJ Pharm Sci\u003c/em\u003e,\u003cem\u003e 102\u003c/em\u003e(9), 3343-3355. https://doi.org/10.1002/jps.23575 \u003c/li\u003e\n\u003cli\u003eIp, S. C., Rass, U., Blanco, M. G., Flynn, H. R., Skehel, J. M., \u0026amp; West, S. C. (2008). Identification of Holliday junction resolvases from humans and yeast. \u003cem\u003eNature\u003c/em\u003e,\u003cem\u003e 456\u003c/em\u003e(7220), 357-361. https://doi.org/10.1038/nature07470 \u003c/li\u003e\n\u003cli\u003eKnauer, M. J., Urquhart, B. L., Meyer zu Schwabedissen, H. E., Schwarz, U. I., Lemke, C. J., Leake, B. F., Kim, R. B., \u0026amp; Tirona, R. G. (2010). Human skeletal muscle drug transporters determine local exposure and toxicity of statins. \u003cem\u003eCirc Res\u003c/em\u003e,\u003cem\u003e 106\u003c/em\u003e(2), 297-306. https://doi.org/10.1161/CIRCRESAHA.109.203596 \u003c/li\u003e\n\u003cli\u003eLange, V., Picotti, P., Domon, B., \u0026amp; Aebersold, R. (2008). Selected reaction monitoring for quantitative proteomics: a tutorial. \u003cem\u003eMol Syst Biol\u003c/em\u003e,\u003cem\u003e 4\u003c/em\u003e, 222. https://doi.org/10.1038/msb.2008.61 \u003c/li\u003e\n\u003cli\u003eLee, S. H., Princz, L. N., Klugel, M. F., Habermann, B., Pfander, B., \u0026amp; Biertumpfel, C. (2015). Human Holliday junction resolvase GEN1 uses a chromodomain for efficient DNA recognition and cleavage. \u003cem\u003eElife\u003c/em\u003e,\u003cem\u003e 4\u003c/em\u003e. https://doi.org/10.7554/eLife.12256 \u003c/li\u003e\n\u003cli\u003eLi, Y., Wagner, E. R., Yan, Z., Wang, Z., Luther, G., Jiang, W., Ye, J., Wei, Q., Wang, J., Zhao, L., Lu, S., Wang, X., Mohammed, M. K., Tang, S., Liu, H., Fan, J., Zhang, F., Zou, Y., Song, D.,\u0026hellip;He, T. C. (2015). The Calcium-Binding Protein S100A6 Accelerates Human Osteosarcoma Growth by Promoting Cell Proliferation and Inhibiting Osteogenic Differentiation. \u003cem\u003eCell Physiol Biochem\u003c/em\u003e,\u003cem\u003e 37\u003c/em\u003e(6), 2375-2392. https://doi.org/10.1159/000438591 \u003c/li\u003e\n\u003cli\u003eLok, J., Gupta, P., Guo, S., Kim, W. J., Whalen, M. J., van Leyen, K., \u0026amp; Lo, E. H. (2007). Cell-cell signaling in the neurovascular unit. \u003cem\u003eNeurochem Res\u003c/em\u003e,\u003cem\u003e 32\u003c/em\u003e(12), 2032-2045. https://doi.org/10.1007/s11064-007-9342-9 \u003c/li\u003e\n\u003cli\u003eMasuda, T., Hoshiyama, T., Uemura, T., Hirayama-Kurogi, M., Ogata, S., Furukawa, A., Couraud, P. O., Furihata, T., Ito, S., \u0026amp; Ohtsuki, S. (2019). Large-Scale Quantitative Comparison of Plasma Transmembrane Proteins between Two Human Blood-Brain Barrier Model Cell Lines, hCMEC/D3 and HBMEC/cibeta. \u003cem\u003eMol Pharm\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(5), 2162-2171. https://doi.org/10.1021/acs.molpharmaceut.9b00114 \u003c/li\u003e\n\u003cli\u003eMeyer Zu Schwabedissen, H. E., Begunk, R., Hussner, J., Juhnke, B. O., Gliesche, D., Bottcher, K., Sternberg, K., Schmitz, K. P., \u0026amp; Kroemer, H. K. (2014). Cell-specific expression of uptake transporters--a potential approach for cardiovascular drug delivery devices. \u003cem\u003eMol Pharm\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(3), 665-672. https://doi.org/10.1021/mp400245g \u003c/li\u003e\n\u003cli\u003eMori, S., Wu, C. Y., Yamaji, S., Saegusa, J., Shi, B., Ma, Z., Kuwabara, Y., Lam, K. S., Isseroff, R. R., Takada, Y. K., \u0026amp; Takada, Y. (2008). Direct binding of integrin alphavbeta3 to FGF1 plays a role in FGF1 signaling. \u003cem\u003eJ Biol Chem\u003c/em\u003e,\u003cem\u003e 283\u003c/em\u003e(26), 18066-18075. https://doi.org/10.1074/jbc.M801213200 \u003c/li\u003e\n\u003cli\u003eNaik, P., \u0026amp; Cucullo, L. (2012). In vitro blood-brain barrier models: current and perspective technologies. \u003cem\u003eJ Pharm Sci\u003c/em\u003e,\u003cem\u003e 101\u003c/em\u003e(4), 1337-1354. https://doi.org/10.1002/jps.23022 \u003c/li\u003e\n\u003cli\u003eOhtsuki, S., Ikeda, C., Uchida, Y., Sakamoto, Y., Miller, F., Glacial, F., Decleves, X., Scherrmann, J. M., Couraud, P. O., Kubo, Y., Tachikawa, M., \u0026amp; Terasaki, T. (2013). Quantitative targeted absolute proteomic analysis of transporters, receptors and junction proteins for validation of human cerebral microvascular endothelial cell line hCMEC/D3 as a human blood-brain barrier model. \u003cem\u003eMol Pharm\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(1), 289-296. https://doi.org/10.1021/mp3004308 \u003c/li\u003e\n\u003cli\u003eOswald, S., Groer, C., Drozdzik, M., \u0026amp; Siegmund, W. (2013). Mass spectrometry-based targeted proteomics as a tool to elucidate the expression and function of intestinal drug transporters. \u003cem\u003eAAPS J\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(4), 1128-1140. https://doi.org/10.1208/s12248-013-9521-3 \u003c/li\u003e\n\u003cli\u003ePoller, B., Drewe, J., Krahenbuhl, S., Huwyler, J., \u0026amp; Gutmann, H. (2010). Regulation of BCRP (ABCG2) and P-glycoprotein (ABCB1) by cytokines in a model of the human blood-brain barrier. \u003cem\u003eCell Mol Neurobiol\u003c/em\u003e,\u003cem\u003e 30\u003c/em\u003e(1), 63-70. https://doi.org/10.1007/s10571-009-9431-1 \u003c/li\u003e\n\u003cli\u003ePoller, B., Gutmann, H., Krahenbuhl, S., Weksler, B., Romero, I., Couraud, P. O., Tuffin, G., Drewe, J., \u0026amp; Huwyler, J. (2008). The human brain endothelial cell line hCMEC/D3 as a human blood-brain barrier model for drug transport studies. \u003cem\u003eJ Neurochem\u003c/em\u003e,\u003cem\u003e 107\u003c/em\u003e(5), 1358-1368. https://doi.org/10.1111/j.1471-4159.2008.05730.x \u003c/li\u003e\n\u003cli\u003eSaegusa, J., Yamaji, S., Ieguchi, K., Wu, C. Y., Lam, K. S., Liu, F. T., Takada, Y. K., \u0026amp; Takada, Y. (2009). The direct binding of insulin-like growth factor-1 (IGF-1) to integrin alphavbeta3 is involved in IGF-1 signaling. \u003cem\u003eJ Biol Chem\u003c/em\u003e,\u003cem\u003e 284\u003c/em\u003e(36), 24106-24114. https://doi.org/10.1074/jbc.M109.013201 \u003c/li\u003e\n\u003cli\u003eSchafer, A. M., Bock, T., \u0026amp; Meyer Zu Schwabedissen, H. E. (2018). Establishment and Validation of Competitive Counterflow as a Method To Detect Substrates of the Organic Anion Transporting Polypeptide 2B1. \u003cem\u003eMol Pharm\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(12), 5501-5513. https://doi.org/10.1021/acs.molpharmaceut.8b00631 \u003c/li\u003e\n\u003cli\u003eSchafer, A. M., Meyer Zu Schwabedissen, H. E., \u0026amp; Grube, M. (2021). Expression and Function of Organic Anion Transporting Polypeptides in the Human Brain: Physiological and Pharmacological Implications. \u003cem\u003ePharmaceutics\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e(6). https://doi.org/10.3390/pharmaceutics13060834 \u003c/li\u003e\n\u003cli\u003eSivandzade, F., \u0026amp; Cucullo, L. (2018). In-vitro blood-brain barrier modeling: A review of modern and fast-advancing technologies. \u003cem\u003eJ Cereb Blood Flow Metab\u003c/em\u003e,\u003cem\u003e 38\u003c/em\u003e(10), 1667-1681. https://doi.org/10.1177/0271678X18788769 \u003c/li\u003e\n\u003cli\u003eSobsey, C. A., Ibrahim, S., Richard, V. R., Gaspar, V., Mitsa, G., Lacasse, V., Zahedi, R. P., Batist, G., \u0026amp; Borchers, C. H. (2020). Targeted and Untargeted Proteomics Approaches in Biomarker Development. \u003cem\u003eProteomics\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(9), e1900029. https://doi.org/10.1002/pmic.201900029 \u003c/li\u003e\n\u003cli\u003eStins, M. F., Badger, J., \u0026amp; Sik Kim, K. (2001). Bacterial invasion and transcytosis in transfected human brain microvascular endothelial cells. \u003cem\u003eMicrob Pathog\u003c/em\u003e,\u003cem\u003e 30\u003c/em\u003e(1), 19-28. https://doi.org/10.1006/mpat.2000.0406 \u003c/li\u003e\n\u003cli\u003eTaggi, V., Riera Romo, M., Piquette-Miller, M., Meyer Zu Schwabedissen, H. E., \u0026amp; Neuhoff, S. (2022). Transporter Regulation in Critical Protective Barriers: Focus on Brain and Placenta. \u003cem\u003ePharmaceutics\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(7). https://doi.org/10.3390/pharmaceutics14071376 \u003c/li\u003e\n\u003cli\u003eTaggi, V., Schafer, A. M., Dolce, A., \u0026amp; Meyer Zu Schwabedissen, H. E. (2024). A face-to-face comparison of the BBB cell models hCMEC/D3 and hBMEC for their applicability to adenoviral expression of transporters. \u003cem\u003eJ Neurochem\u003c/em\u003e,\u003cem\u003e 168\u003c/em\u003e(9), 2611-2620. https://doi.org/10.1111/jnc.16125 \u003c/li\u003e\n\u003cli\u003eUrich, E., Lazic, S. E., Molnos, J., Wells, I., \u0026amp; Freskgard, P. O. (2012). Transcriptional profiling of human brain endothelial cells reveals key properties crucial for predictive in vitro blood-brain barrier models. \u003cem\u003ePLoS One\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(5), e38149. https://doi.org/10.1371/journal.pone.0038149 \u003c/li\u003e\n\u003cli\u003eVatine, G. D., Al-Ahmad, A., Barriga, B. K., Svendsen, S., Salim, A., Garcia, L., Garcia, V. J., Ho, R., Yucer, N., Qian, T., Lim, R. G., Wu, J., Thompson, L. M., Spivia, W. R., Chen, Z., Van Eyk, J., Palecek, S. P., Refetoff, S., Shusta, E. V., \u0026amp; Svendsen, C. N. (2017). Modeling Psychomotor Retardation using iPSCs from MCT8-Deficient Patients Indicates a Prominent Role for the Blood-Brain Barrier. \u003cem\u003eCell Stem Cell\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(6), 831-843 e835. https://doi.org/10.1016/j.stem.2017.04.002 \u003c/li\u003e\n\u003cli\u003eWegler, C., Gaugaz, F. Z., Andersson, T. B., Wisniewski, J. R., Busch, D., Groer, C., Oswald, S., Noren, A., Weiss, F., Hammer, H. S., Joos, T. O., Poetz, O., Achour, B., Rostami-Hodjegan, A., van de Steeg, E., Wortelboer, H. M., \u0026amp; Artursson, P. (2017). Variability in Mass Spectrometry-based Quantification of Clinically Relevant Drug Transporters and Drug Metabolizing Enzymes. \u003cem\u003eMol Pharm\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(9), 3142-3151. https://doi.org/10.1021/acs.molpharmaceut.7b00364 \u003c/li\u003e\n\u003cli\u003eWilson, S. R., Vehus, T., Berg, H. S., \u0026amp; Lundanes, E. (2015). Nano-LC in proteomics: recent advances and approaches. \u003cem\u003eBioanalysis\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(14), 1799-1815. https://doi.org/10.4155/bio.15.92 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"hCMEC/D3, hBMEC, OATP2B1, Adenoviral infection, BBB model, Proteomics ","lastPublishedDoi":"10.21203/rs.3.rs-5388233/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5388233/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe blood-brain barrier (BBB) is essential for central nervous system (CNS) homeostasis by regulating permeability between the bloodstream and brain. This study evaluates the immortalized human brain capillary endothelial cell lines hCMEC/D3 and hBMEC for their use as an \u003cem\u003ein vitro\u003c/em\u003e BBB model to investigate the OATP2B1 transporter following adenoviral infection. We assessed the impact of adenoviral-mediated OATP2B1 expression on BBB marker proteins and transporters using targeted and untargeted mass spectrometry-based proteomics. Targeted proteomics identified measurable levels of endothelial markers PECAM1 and CDH5 in hCMEC/D3, whereas these markers were undetectable in hBMEC. Both cell lines exhibited similar Pgp levels, while BCRP was absent in hCMEC/D3. Although OATP2B1 levels did not significantly increase post-infection in targeted proteomics, untargeted proteomics confirmed enhanced OATP2B1 expression. Other BBB markers and transporters remained unaffected in both cell lines. Notably, hCMEC/D3 demonstrated a stronger BBB phenotype, indicated by higher expression of BBB markers and transporters, while adenoviral infection was more effective in hBMEC. The differences between targeted and untargeted proteomics underscore the need for diverse methods to verify protein expression levels. This comparative analysis provides insights into the strengths and limitations of hCMEC/D3 and hBMEC for BBB research, particularly regarding drug transport mechanisms.\u003c/p\u003e","manuscriptTitle":"Targeted and untargeted proteomics-based comparison of adenoviral infected hCMEC/D3 and hBMEC as a human blood-brain barrier model to study the OATP2B1 transporter","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-05 08:46:46","doi":"10.21203/rs.3.rs-5388233/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-11T19:49:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-02T12:20:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-29T16:54:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-28T11:15:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-28T01:12:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-23T15:33:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-23T10:46:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191449231203292562898849231850701411964","date":"2024-12-10T15:58:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34704455563430741738265233906010142981","date":"2024-12-10T15:58:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227694787190673400390336238863631869996","date":"2024-12-07T21:55:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43853488771522423014511616308638799768","date":"2024-12-07T20:57:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114818505076615262796923906423872804973","date":"2024-12-07T19:08:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125433909355499870761998839257370516164","date":"2024-12-05T17:07:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247699785934638850633593552410947472667","date":"2024-12-05T16:25:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-05T15:55:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T06:17:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T06:14:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2024-11-04T12:43:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4e1fcfb3-9965-4528-ab3c-a39abc275e74","owner":[],"postedDate":"December 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:12:56+00:00","versionOfRecord":{"articleIdentity":"rs-5388233","link":"https://doi.org/10.1007/s12035-025-04807-7","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2025-03-14 15:58:37","publishedOnDateReadable":"March 14th, 2025"},"versionCreatedAt":"2024-12-05 08:46:46","video":"","vorDoi":"10.1007/s12035-025-04807-7","vorDoiUrl":"https://doi.org/10.1007/s12035-025-04807-7","workflowStages":[]},"version":"v1","identity":"rs-5388233","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5388233","identity":"rs-5388233","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}