Abstract

   Coronary artery endothelial cells (CAEC) exert an important role in the
   development of cardiovascular disease. Dysfunction of CAEC is
   associated with cardiovascular disease in subjects with type 2 diabetes
   mellitus (T2DM). However, comprehensive studies of the effects that a
   diabetic environment exerts on this cellular type are scarce. The
   present study characterized the molecular perturbations occurring on
   cultured bovine CAEC subjected to a prolonged diabetic environment
   (high glucose and high insulin). Changes at the metabolite and peptide
   level were assessed by Liquid Chromatography–Mass Spectrometry
   (LC–MS^2) and chemoinformatics. The results were integrated with
   published LC–MS^2-based quantitative proteomics on the same in vitro
   model. Our findings were consistent with reports on other endothelial
   cell types and identified novel signatures of DNA/RNA, amino acid,
   peptide, and lipid metabolism in cells under a diabetic environment.
   Manual data inspection revealed disturbances on tryptophan catabolism
   and biosynthesis of phenylalanine-based, glutathione-based, and
   proline-based peptide metabolites. Fluorescence microscopy detected an
   increase in binucleation in cells under treatment that also occurred
   when human CAEC were used. This multi-omics study identified particular
   molecular perturbations in an induced diabetic environment that could
   help unravel the mechanisms underlying the development of
   cardiovascular disease in subjects with T2DM.

   Subject terms: Proteomics, Metabolomics, Peptides, Proteomics,
   Mechanisms of disease, Cellular signalling networks

Introduction

   Damage to coronary artery endothelial cells (CAEC) leads to coronary
   endothelial dysfunction, which is associated with the development of
   cardiovascular disease (CVD) in subjects with and without coronary
   atherosclerosis^[36]1. Subjects with type 2 diabetes mellitus (T2DM)
   are particularly at increased risk of myocardial infarction^[37]2 and
   coronary endothelial dysfunction has been implicated in the
   prognosis^[38]3. A high-glucose (HG) environment—a hallmark of
   T2DM—leads to nitric oxide signaling, cell cycle^[39]4,
   apoptosis^[40]5, angiogenesis^[41]6, and DNA structure
   impairment^[42]7. However, given the intrinsic heterogeneity of the
   endothelium, the molecular perturbations caused by HG vary accordingly
   with the type of studied endothelial cells^[43]8,[44]9. For instance,
   human microvascular endothelial cells showed increased gene expression
   of endothelial nitric oxide synthase, superoxide dismutase 1,
   glutathione peroxidase 1, thioredoxin reductase 1 and 2 compared to the
   regulation observed in human umbilical vein endothelial cells (HUVEC)
   when cultured in HG for 24 h. Furthermore, the response of endothelial
   cells to HG is influenced by the duration of exposure^[45]10,[46]11 as
   demonstrated in bovine aortic and human microvascular endothelial cells
   where cell proliferation and apoptosis were higher at < 48 h compared
   to 8 weeks of exposure^[47]10. In another example of time-dependent
   response, increased apoptosis (derived from DNA fragmentation) and
   tumor necrosis factor alpha protein levels were reported in human
   coronary artery endothelial cells (HCAEC) after only 24 h of incubation
   with HG^[48]5. Hence, the molecular response to HG cannot be
   generalized among endothelial cell types.

   Incubation periods up to 48 h are commonly used to evaluate the
   molecular perturbations caused by HG on endothelial cells wherein
   transient increases in cell proliferation were shown. This reflects
   early cell adaptations to the environment rather than cell
   injury^[49]10,[50]12. Yet, other studies reported impaired
   mitochondrial function/structure and nitric oxide signaling in HG
   treated HCAEC for 48 h^[51]13. A 72-h study documented an increase in
   pro-inflammatory cytokines^[52]14 and oxidative stress in HCAEC^[53]15.
   However, the long-term (e.g., > 72 h) effect of HG in CAEC has not been
   as extensively documented compared to other endothelial cell types
   (i.e., HUVEC, Human Aortic Endothelial Cells).

   Characterizing the long-term effects of HG on CAEC, cells intrinsically
   involved in the development of CVD, may allow us to better identify key
   signaling pathways (or specific biomolecules) associated with the
   development of endothelial dysfunction and CVD.

   Here, we test the hypothesis that prolonged simulated diabetes impairs
   multiple signaling pathways or signal transductions in CAEC. To achieve
   that, we performed a Liquid Chromatography–Mass Spectrometry
   (LC–MS^2)-based multi-omics study utilizing various chemoinformatic and
   bioinformatic pipelines to extract biological data at a metabolomic,
   peptidomic, and proteomic level in CAEC perturbed by simulated
   diabetes. Molecular responses to simulated diabetes hinted at changes
   in the endothelial phenotype validated by fluorescence microscopy
   analysis. Our methodological approach facilitated the identification of
   signaling pathways and specific molecules associated with the
   development of coronary artery endothelial dysfunction and CVD.

Methods

Chemical and reagents

   Recombinant human insulin was purchased from Sigma Aldrich (St. Louis,
   MO, USA). Antibiotic-antimitotic solution, trypsin–EDTA solution 0.25%,
   Hank’s Balanced Salt Solution (HBSS) without phenol red, Dulbecco’s
   Modified Eagle’s Media (DMEM) with glutamine, Fetal Bovine Serum (FBS),
   Hoechst 33258, Pentahydrate (bis-Benzimide)-FluoroPure, MitoTracker Red
   FM (MTR) and methanol-free formaldehyde (16% solution) were obtained
   from Thermo Fisher Scientific (Waltham, MA, USA). Methanol,
   Acetonitrile, and water were Optim LC–MS Grade and obtained from Fisher
   Scientific (Hampton, NH, USA). Ethanol LiChrosolv Grade was obtained
   from Merck KGaA (Darmstadt, Germany). Rabbit anti-Von Willebrand factor
   (vWf) antibody and goat anti-rabbit IgG conjugated to Alexa Fluor 488
   were obtained from Abcam (Cambridge, MA, USA).

Cell culture

   BCAEC were purchased from Cell applications, Inc. (San Diego, CA, USA)
   and grown as previously described^[54]16. In brief, cells were
   proliferated in 100 mm × 20 mm dishes with DMEM (5.5 mmol/L glucose,
   supplemented with 10% FBS and 1% antibiotic-antimitotic solution) or
   growth-media at 37 °C in an incubator with a humidified atmosphere of
   5% CO[2].

Cellular treatment

   Cells were seeded at 300,000 cells per well in 6-well plates (Corning
   CellBIND) and allowed to proliferate (using growth-media) until
   reaching an 80% confluence. Then, cells were switched to DMEM with 1%
   FBS for 12 h to maintain the cells under a quiescent state. Cells were
   first treated with 100 nmol/L insulin (high-insulin, HI) or vehicle in
   normal glucose (NG, 5.5 mmol/L in DMEM) for 3 days^[55]17 and then
   different concentrations of glucose were added to the media to achieve
   a high-glucose (HG: 15 mmol/L, 17.2 mmol/L or 20 mmol/L) ambient for up
   to 9 days. This sequential scheme tried to mimic the pathophysiological
   conditions that occur in T2DM patients, wherein hyperinsulinemia
   precedes hyperglycemia^[56]18. The control or NG group consisted of
   glucose 5.5 mmol/L without HI. As hyperosmolar controls, cells were
   incubated with 5.5 mmol/L glucose + mannitol 9.5, 12.2 and 14.5 mmol/L
   with or without HI for 9 days. For selected experiments (binucleation
   analysis), HCAEC (55 years old Caucasian male, history of T2DM
   for > 5 years) were purchased from Cell Applications, Inc. and
   subjected to the same conditions as BCAEC but using MesoEndo Growth
   Medium (Cell Applications, Inc.) to induce proliferation. For simulated
   diabetes, HCAEC were treated with HI and HG (20 mmol/L) for 9 days as
   with BCAEC but, MesoEndo Growth Medium was used instead. For all the
   experiments, three independent wells (in either 6 or 12 wells-plate)
   per condition were pooled to generate an independent biological
   replicate. BCAEC and HCAEC were used at passages between 6 and 12.

Measurement of mitochondrial membrane potential by flow cytometry

   To evaluate the individual and combined metabolic effects of HI and HG
   on BCAEC, we measured the mitochondrial membrane potential, as a
   surrogate marker of oxidative metabolism^[57]19, by flow cytometry.
   After treatment (in 12-wells plate), BCAEC were gently collected using
   a sterile cell scraper, washed with pre-warmed HBSS, and centrifuged to
   obtain a cell pellet. Cells were re suspended in fresh pre-warmed HBSS
   and incubated with 100 nmol/L MTR for 30 min at 37 °C under a dark
   environment. Cells were washed 3 × with HBSS (to remove the excess of
   dye), and finally re suspended in fresh HBSS for data acquisition. Flow
   cytometry analysis was performed using an Attune Acoustic Focusing Flow
   Cytometer (Thermo Fisher Scientific, Waltham, MA, USA). A minimum of
   10,000 cells was recorded and data was processed using FlowJo (v10.6.1,
   Becton, Dickinson and Company, Franklin Lakes, NJ, USA).

Immunofluorescence

   As previously described^[58]16, 100,000 cells per well were seeded onto
   12-well plates (Corning CellBIND) and exposed to simulated diabetes.
   Thereafter, BCAEC and HCAEC were washed with PBS to remove dead cells
   and debris. Cells were fixed, permeabilized, and blocked as described
   before^[59]20. Cells were then incubated with a polyclonal antibody
   against the vWf (1:400, 3% BSA in PBS) overnight at 4 °C and thereafter
   washed 3 × with PBS. Alexa Fluor 488-labeled anti-rabbit (1:400 in PBS)
   was then used as a secondary antibody for 1 h at RT and washed 3 × with
   PBS. As a negative control, cells were incubated only with secondary
   antibody to assess for non-specific binding. Cell nuclei were stained
   with Hoechst 33258 (2 µg/mL in HBSS) for 30 min and washed 3 × with
   PBS. Fluorescent images were taken in at least three random fields per
   condition using an EVOS FLoid Cell Imaging Station with a fixed
   20 × air objective. Image analysis was performed through ImageJ
   software (version 2.0.0).

Peptide and non-peptide metabolite extraction

   Cells were seeded at 300,000 cells per well in 6-well plates (Corning
   CellBIND) and treated as above. After HG and HI conditions, metabolites
   were extracted following a published protocol for adherent cells with
   some modifications^[60]21 (Fig. [61]1).

Figure 1.

   [62]Figure 1
   [63]Open in a new tab

   Illustration of the methodology followed in this study.

   In brief, after washing the cells 3 × with PBS, 500 µL of a cold
   mixture of methanol: ethanol (50:50, v:v) were added to each well,
   covered with aluminum foil, and incubated at − 80 °C for 4 h. Cells
   were then scrapped using a lifter (Fisher Scientific, Hampton, NH,
   USA), and the suspension was transferred to Eppendorf tubes before
   centrifugation for 10 min at 14,000 rpm at 4 °C. The supernatant was
   transferred to another tube and dried down by SpeedVac System (Thermo
   Fisher Scientific, Waltham, MA, USA). Samples were reconstituted in
   water/acetonitrile 95:5 v/v with 0.1% formic, centrifuged at 14,000 rpm
   for 10 min at 4 °C. The particle free supernatant was recovered for
   further LC–MS^2 analysis.

LC–MS^2 data acquisition for untargeted metabolomics and peptidomics

   Metabolites were loaded into an Eksigent nanoLC 400 system (AB Sciex,
   Foster City, CA, USA) with a HALO Phenyl-Hexyl column (0.5 × 50 mm,
   2.7 μm, 90 Å pore size, Eksigent AB Sciex, Foster City, CA, USA) for
   data acquisition using the LC–MS parameters previously described with
   some modifications^[64]22. In brief, the separation of metabolites was
   performed using gradient elution with 0.1% formic acid in water (A) and
   0.1% formic acid in ACN (B) as mobile phases at a constant flow rate of
   5 μL/min. The gradient started with 5% B for 1 min followed by a
   stepped increase to 100%, B over 26 min and held constant for 4 min.
   Solvent composition was returned to 5% B for 0.1 min. Column
   re-equilibration was carried out with 5% mobile phase B for 4 min.
   Potential carryover was minimized with a blank run (1 μL buffer A)
   between sample experimental samples. The eluate from the LC was
   delivered directly to the TurboV source of a TripleTOF 5600 + mass
   spectrometer (AB Sciex, Foster City, CA, USA) using electrospray
   ionization (ESI) under positive mode. ESI source conditions were set as
   follows: IonSpray Voltage Floating, 5500 V; Source temperature, 350 °C;
   Curtain gas, 20 psi; Ion source gases 1 and 2 were set to 40 and 45
   psi; Declustering potential, 100 V. Data was acquired using
   data-dependent acquisition (DDA) with high sensitivity mode selected,
   automatically switching between full-scan MS and MS/MS. The
   accumulation time for TOF MS was 0.25 s/spectra over the m/z range
   100–1500 Da and for MS/MS scan was 0.05 s/spectra over the m/z
   50–1500 Da. The DDA settings were as follows: charge state + 1 to + 2,
   intensity 125 cps, exclude isotopes within 6 Da, mass tolerance 50 mDa,
   and a maximum number of candidate ions 20. Under IDA settings, the
   ‘‘exclude former target ions’’ was set as 15 s after two occurrences
   and ‘‘dynamic background subtract’’ was selected. Manufacturer rolling
   collision energy (CE) option was used based on the size and charge of
   the precursor ion using formula CE = m/z × 0.0575 + 9. The instrument
   was automatically calibrated by the batch mode using appropriate
   positive TOF MS and MS/MS calibration solutions before sample injection
   and after injection of two samples (< 3.5 working hours) to ensure a
   mass accuracy of < 5 ppm for both MS and MS/MS data. Instrument
   performance was monitored during data acquisition by including QC
   samples (pooled samples of equal volume) every 4 experimental samples.
   Data acquisition of experimental samples was also randomized.

Processing of LC–MS^2 for untargeted metabolomics

   To identify peptide and non-peptide metabolites (hereafter referred as
   to untargeted metabolomics), we followed a workflow comprised of
   open-access software packages and on-line platforms (for library
   spectral matching) commonly used for untargeted metabolomics. Three
   complementary informatic approaches were utilized to analyze the
   LC–MS^2 datasets: (1) feature extraction, alignment, normalization, and
   univariate statistical analysis was performed using the XCMS (version
   2.7.2) online platform ([65]https://xcmsonline.scripps.edu)^[66]23; (2)
   MS^2 spectral data extraction for metabolite identification
   (Metabolomics Standards Initiative (MSI) classification level 2 and
   3)^[67]24 was performed with MZmine (version 2.53)^[68]25, the Global
   Natural Products Social Molecular Networking web platform (GNPS,
   [69]https://gnps.ucsd.edu)^[70]26,[71]27, and in-silico integrated
   tools within SIRIUS software (version 4.9.12)^[72]28, and; (3)
   multivariate statistical analysis and heatmap visualization was done
   using Metaboanalyst 5.0 ([73]https://www.metaboanalyst.ca)^[74]29. For
   approach 1, raw proprietary .wiff files were uploaded into the XCMS
   online platform to perform mass detection, chromatogram building and
   deconvolution, isotopic assignment, feature alignment, and gap-filling
   (to detect features missed during the initial alignment). For approach
   2, .wiff files were first converted to .mzML using ProteoWizard version
   3.0 and then imported into MZmine to perform the peak extraction steps
   as in the XCMS online platform. To identify or annotate the metabolites
   at the chemical structure and class level, the MS^2-containing features
   extracted with MZmine were further analyzed by molecular networking
   using the GNPS platform and associated in-silico dereplication tools,
   Network Annotation Propagation (NAP)^[75]30 and MS2LDA^[76]31, as well
   as the automated chemical classification by Classyfire^[77]32, as
   previously described^[78]22. The confidences of such annotations are
   level 2 (probable structure by library spectrum match) and level 3
   (tentative candidates) in agreement with the Metabolomics Standards
   Initiative (MSI) classification^[79]24. Molecular networking, NAP, and
   Classyfire outputs were integrated using the MolNetEnhancer
   workflow^[80]33. Molecular networks were visualized using Cytoscape
   version 3.8.2^[81]34. In addition, chemical substructures (co-occurring
   fragments and neutral losses referred to as “mass2motifs” [M2M]) were
   recognized using the MS2LDA web pipeline ([82]http://www.ms2lda.org) to
   further annotate metabolites (level 3, MSI). For select metabolites,
   high-confidence annotations (at the substructure and structure pattern,
   level 3, MSI) were made using in-silico integrated tools within SIRIUS
   software (version 4.9.12)^[83]28. For approach 3, normalized peak
   abundance data (.txt) retrieved by XCMS analysis was imported into
   Metaboanalyst and filtered for Principal Component Analysis (PCA) and
   HeatMap analysis. The detailed processing parameters for all the
   pipelines are found in supplemental experimental methods in supporting
   information.

Processing of LC–MS^2 for peptidomics

   To expand the identification of endogenous peptide metabolites not
   contained in standards-enriched spectral libraries (including GNPS and
   collaborators), we performed in-silico peptide identification using the
   Bos taurus proteome as a database to retrieve amino acid sequences for
   spectral matching (hereafter referred as to peptidomics). The raw files
   (derived from the same experiments) used for untargeted metabolomics
   (.wiff and .wiff.scan files) from the experimental and control groups
   were analyzed separately using ProteinPilot software version 4.2 (Ab
   Sciex, Foster City, CA, USA) with the Paragon algorithm. MS^1 and MS^2
   data were searched against the Bos taurus SwissProt sequence database
   (6006 reviewed proteins + common protein contaminants, February 2019
   release). The parameters input was: sample type, identification;
   digestion, none; Cys alkylation, none; instrument, TripleTOF 5600;
   special factors, none; species, Bos taurus; ID focus, biological
   modifications, and amino acid substitutions; search effort, thorough
   ID. False discovery rate analysis was also performed. All peptides were
   exported and those with a > 90% confidence were linked to the
   corresponding feature extracted by the XCMS algorithm using their
   accurate mass and retention time information. For peptide
   quantification, we employed the normalized feature abundances (MS^1
   level) generated by XCMS. A significance threshold of p < 0.05 (Welch’s
   t test) was utilized.

Processing of LC–SWATH–MS^2 for proteomics

   The SWATH-based proteomics data (identifier PXD013643), hosted in
   ProteomeXchange consortium via PRIDE^[84]35, was reanalyzed with some
   modifications. This dataset derived from independent biological
   replicates as those used in this study. The parameters used to build
   the spectral library remained the same^[85]16, while the parameter for
   peptides per protein was set to 100 in the software SWATH Acquisition
   MicroApp 2.0 in PeakView version 1.2 (AB Sciex, Foster City, CA, USA).
   The obtained protein peak areas were exported to Markerview version 1.3
   (AB Sciex, Foster City, CA, USA) for further data refinement, including
   assignment of IDs to files and removal of reversed and common
   contaminants. Peak areas were exported in a .tsv file, and normalized
   with NormalyzerDE online version 1.3.4^[86]36. The NormalyzerDE
   pipeline comprises 8 different normalization methods (Log2, variance
   stabilizing normalization, total intensity, median, mean, quantile,
   CycLoess, and robust linear regression). The results of qualitative (MA
   plots, scatter plots, box plots, density plots) and quantitative
   (pooled intragroup coefficient of variation [PCV], median absolute
   deviation [PMAD], estimate of variance [PEV]) parameters were compared
   between the normalization methods to select the most appropriate.

Bioinformatic analysis of proteomics data

   Proteins that passed the significance threshold were first converted to
   their corresponding Entrez Gene (GeneID) using
   [87]https://www.uniprot.org/uploadlists/ and then transformed to their
   human equivalents using the ortholog conversion feature in
   [88]https://biodbnet-abcc.ncifcrf.gov/db/dbOrtho.php. Bioinformatic
   analysis was done on OmicsNet website platform
   ([89]https://www.omicsnet.ca/)^[90]37,[91]38. First, a protein–protein
   interaction (PPI) molecular network (first-order network containing
   query or seeds molecules and their immediate interacting partners) with
   minimum network filtering selected using STRING PPI database was
   built^[92]39, and then pathway enrichment analysis was performed using
   the built-in REACTOME^[93]40 and the Kyoto Encyclopedia of Genes and
   Genomes (KEGG)^[94]41 databases. Hypergeometric test was used to
   compute p values.

Integrative bioinformatic analysis of proteomics and metabolomics data

   The molecular interactions between the proteins and metabolites
   differentially abundant between HG + HI and NG were determined in
   OmicsNet. The lists of proteins (EntrezGene ID) and metabolites (HMDB
   ID) were loaded to build a composite network using protein–protein
   (STRING database selected) and metabolite-protein (KEGG database
   selected) interaction types. The primary network relied on the
   metabolite input using the minimum network filtering option. Pathway
   enrichment analysis was performed using the built-in REACTOME and KEGG
   databases. Hypergeometric test was used to compute p values.

Statistical analysis

   All experiments were performed with biological triplicates. Three
   independent wells per condition were pooled to generate an independent
   biological replicate. For all the data, excluding proteomics and
   metabolomics, statistical analysis was performed by either using
   one-way ANOVA followed by the Dunnett’s post hoc test or unpaired
   student’s t test, as appropriate. A p value ≤ 0.05 was considered
   statistically significant. For the proteomics data, based on the high
   reproducibility of SWATH-based quantification^[95]42,[96]43 and
   fold-change compression phenomenon (i.e., lower measured fold-change
   vs. real biological fold-change)^[97]43,[98]44, proteins with a fold
   change ≥ 1.2 or ≤ 1/1.2 and a p value < 0.05 (Welch’s t-test) were
   considered as differentially abundant between NG and HG + HI
   conditions. For the metabolomics data (DDA), also taking into account
   the fold-change compression phenomenon^[99]44—but lower accuracy of DDA
   than DIA-SWATH, features with a fold change ≥ 1.3 or ≤ 1/1.3 and a p
   value < 0.05 (Welch’s t-test) were considered as differentially
   abundant. We did not apply multiple-test corrections to calculate
   adjusted p values, because this process could obscure proteins or
   metabolites with real changes (true-positives)^[100]45. Instead, the
   analysis was focused on top-enriched signaling pathways (adjusted p
   value < 0.01) that allowed us to determine a set of interacting
   proteins and metabolites with relevant biological information and
   contributes in reducing false positives. For multivariate statistical
   analysis and heatmap visualization, Metaboanalyst 4.0
   ([101]https://www.metaboanalyst.ca) was utilized. PCA on
   log-transformed data was used to assess for sample clustering behavior
   and inter-group variation. No scaling was used for PCA and heatmap
   analysis. Software PRISM 6.0 (GraphPad Software, San Diego, CA) was
   used for the creation of volcano plots and column graphs.

Results

Mitochondrial membrane potential

   To define a simulated diabetes model, we tested the effects (time and
   concentration-dependent) of various HG concentrations with and without
   a single concentration of HI (100 nmol/L) on the endothelial
   mitochondrial function^[102]19. We noted differential effects of HG
   depending on the incubation time (3 vs. 6 vs. 9 days) and the presence
   of HI (Fig. [103]S1). Our results showed that 20 mmol/L glucose with
   100 nmol/L insulin at day 9 significantly reduced the mitochondrial
   membrane potential (Fig. [104]S1). Hyperosmolar controls at day 9
   (using mannitol) did not significantly affect the mitochondrial
   endpoint. Therefore, our simulated diabetes model was defined as an
   environment of 20 mmol/L glucose + 100 nmol/L insulin for 9 days, while
   the control groups consisted of cells treated with 5.5 mmol/L glucose
   for the same period.

Untargeted metabolomics

   Overall 5571 features or potential metabolites were detected using XCMS
   and MZmine, wherein 957 (~ 18%) features were commonly identified in
   both platforms (Fig. [105]2A). Based on the relative quantification
   using XCMS, 140 and 82 features were detected with reduced and
   increased abundances respectively in the experimental group compared to
   the control group (Fig. [106]2B). The effects of HG and HI in the
   experimental group are observed by PCA analysis wherein the
   experimental samples clustered away from the control group
   (Fig. [107]2C). The consistency of the LC–MS equipment is apparent by
   the tight-clustering of the QC samples (Fig. [108]2C). Further, the
   heatmap visualization of the top 100-modulated metabolites exhibited
   the different distribution patterns among groups (Fig. [109]2D). Using
   the GNPS platform for automatic metabolite annotation, 106 compounds
   (excluding duplicates and contaminants) were putatively annotated with
   a level 2 confidence annotation (MS^2 spectral match) (Table [110]S1)
   in agreeance with the MSI classification^[111]24. Some metabolites
   identified by the GNPS platform could not be quantified because they
   were not detected by the XCMS algorithm during feature area
   normalization and quantification.

Figure 2.

   [112]Figure 2
   [113]Open in a new tab

   Simulated diabetes induced changes in the metabolome of bovine coronary
   artery endothelial cells (BCAEC). (A) Venn diagram of features
   identified among MZmine and XCMS software (0.01 Da and 1 min retention
   time, thresholds) on LC–MS^2 datasets. (B) Volcano plot of all
   quantified metabolites displaying differences in relative abundance
   (> ± 30% change, < 0.05 p value cut-offs) between BCAEC cultured in
   control (NG) media and simulated diabetes (HG + HI) for twelve days.
   Values (dots) represent the HG + HI/NG ratio for all metabolites. Red
   and blue dots denote downregulated and upregulated metabolites in the
   HG + HI group versus NG group, respectively. (C) Principal Component
   Analysis (PCA) of LC–MS^2 datasets. Data was log transformed without
   scaling. Shade areas depict the 95% confidence intervals. (D) HeatMap
   of the top 100 metabolites ranked by t-test. Abbreviations: NG normal
   glucose, HG high glucose, HI high insulin, QC quality control.

   Moreover, GNPS Molecular Networking aligned the MS^2-containing
   features (n = 1013) based on their structural similarity, creating 118
   independent networks or clusters with at least two connected nodes
   (Fig. [114]3A). The use of MolNetEnhancer workflow allowed to
   putatively identify chemical classes (level 3, MSI) for 56 of the 118
   independent networks. The top-10 most abundant annotated chemical
   classes and associated metabolites are shown in Fig. [115]3A. A focused
   analysis on the chemical subclasses associated with the dysregulated
   metabolites by simulated diabetes, revealed that amino acids, peptides,
   and analogues were principally affected (Fig. [116]3B). Three-clusters
   from the network were further analyzed because they contained annotated
   metabolites by spectral matching, which facilitates the annotation of
   other cluster’s nodes. Cluster 1 revealed two metabolites linked to the
   organonitrogen compounds class with reduced abundance in the
   experimental group (Fig. [117]3C). Library spectral match (level 2,
   MSI) suggest PC(16:0/18:1(9Z)) and PC(18:0/18:2(9Z,12Z)) as putative
   candidates, which was supported by MS2LDA phosphocholine-substructure
   recognition (Fig. [118]3D).

Figure 3.

   [119]Figure 3
   [120]Open in a new tab

   Bovine coronary artery endothelial cells (BCAEC) metabolite molecular
   network. (A) Molecular classes (according to Classyfire) of the
   metabolome identified by the MolNetEnhancer workflow and visualized by
   Cytoscape version 3.8.2. Each node represents a unique feature and the
   color of the node denotes the associated chemical class. The thickness
   of the edge (connectivity) indicates the MS^2 similarity (Cosine score)
   among features. The m/z value of the feature is shown inside the node
   and is proportional to the size of the node. Three selected clusters or
   connected features as relevant are shown. (B) Chemical subclasses
   (predicted by CANOPUS) of the dysregulated metabolites by simulated
   diabetes. (C) Inset of cluster 1 denoting the presence of
   phosphocholine (PC)-containing lipids. Significant differential
   abundant features among simulated diabetes (HG + HI) and control (NG)
   groups are indicated with an asterisk (p value < 0.05). (D)
   Characterization of features in (B) aided by substructure recognition
   by MSLDA software using MS^1 visualization in [121]www.ms2lda.org.
   Fragment at m/z 184.0725 linked to a PC head group by mzCloud in silico
   prediction ([122]www.mzCloud.org). Abbreviations: M2M mass2motif, FC
   fold change, NG normal glucose, HG high glucose, HI high insulin.
   Chemical structures were drawn by ChemDraw Professional version
   16.0.1.4.

   In cluster 2, glutathione-based metabolites (MSI level 3) were detected
   through fragments m/z 308.0925, 233.0575, 179.0475, and 162.0225
   retrieved by the M2M_453 substructure and associated with glutathione
   structure using mzCloud in silico predictions (Fig. [123]4A). The
   precursor ion at m/z 713.1472 and glutathione (annotated at level 2,
   MSI) were detected with increased abundance in the experimental group.
   MS2LDA visualization, at the M2M level, correlated with the GNPS
   molecular networking clustering (Fig. [124]4B). In cluster 3, various
   phenylalanine-based metabolites were putatively annotated aided by
   MS2LDA substructure recognition (Fig. [125]4C and D). Within this
   cluster, glutamyl-phenylalanine, leucine-phenylalanine and the
   precursor ion at m/z 487.1548 (unknown) presented with increased
   abundance in the experimental versus control group. On the other hand,
   various amino acids were annotated (level 2, MSI) by GNPS spectral
   matching and manual inspection of data (Table [126]S2). Threonine,
   valine, proline, leucine, serine, glutamic acid, methionine, and
   tyrosine presented increased abundance (fold change range 1.3–1.7,
   p < 0.05) in the experimental versus control group. Particularly,
   metabolites linked to the catabolism of tryptophan via the serotonin
   and kynurenine pathway^[127]46 were annotated (level 2, MSI), including
   melatonin, acetyl serotonin, and kynurenine (Table [128]S2). However,
   only kynurenine was significantly elevated in the experimental group.
   The full list of annotated metabolites, differential abundances and
   another relevant feature information is shown in Table [129]S2.

Figure 4.

   [130]Figure 4
   [131]Open in a new tab

   Peptide metabolites modulated by simulated diabetes in bovine coronary
   artery endothelial cells (BCAEC). (A) Cluster 2 retrieved from the main
   molecular network linked to glutathione and derivatives. The fragments
   of mass-2-motif (M2M)_453 colored in red are characteristic of a
   glutathione core and the fragments are shown in red. (B) Features
   associated with M2M_453 using MS^1 visualization in
   [132]www.ms2lda.org. (C) Cluster 3 retrieved from the main molecular
   network linked to phenylalanine-based metabolites. A singular node at
   m/z 487.1548 is also shown. The fragments of M2M_59 colored in red are
   characteristic of a phenylalanine core (Heuristic and Quantum Chemical
   predictions by [133]www.mzCloud.org). (D) Features associated with
   M2M_59 using MS^1 visualization in [134]www.ms2lda.org. In GNPS’s
   clusters (A and C), the node’s color denotes the chemical class
   assigned to the cluster. The thickness of the edge (connectivity)
   indicates the cosine score (MS^2 similarity). The m/z value of the
   feature is shown inside the node and is proportional to the size of the
   node. Significant differential abundant features among simulated
   diabetes (HG + HI) and control (NG) groups are indicated with an
   asterisk (p value < 0.05). In MS2LDA’s nodes (B and D), the green node
   represents the M2M and squares indicate individual features. Edges
   represent connections to M2M. Significant differential abundant
   features among groups are indicated with an asterisk (p value < 0.05).
   Abbreviations: M2M mass2motif, FC fold change, NG normal glucose, HG
   high glucose, HI high insulin. Chemical structures were drawn by
   ChemDraw Professional version 16.0.1.4.

Peptidomics

   Experimental and control datasets were analyzed separately to identify
   the peptides and their biological modifications. The complete list of
   peptides identified by ProteinPilot between the experimental and
   control groups are described in Table [135]S3. Proline oxidation was
   the most frequent biological modification detected in the experimental
   group datasets. We identified 8 and 12 peptides with a confidence
   of > 90% in the control and experimental group, respectively.
   Differential abundance of 2 proline-rich peptides was observed in the
   experimental group compared to the control group. An additional
   tripeptide was manually annotated with a LPP sequence (Table [136]S4).

Proteomics

   The re-analysis of the SWATH data (PXD013643 dataset) facilitated the
   identification of 952 quantifiable proteins (717 proteins with at least
   2 unique peptides, 1% false discovery rate) and no missing values among
   technical and biological replicates (Table [137]S5). Sample datasets
   were normalized using 8 different methods to select the most
   appropriate based on quantitative and qualitative parameters on our
   dataset. Quantile normalization produced a better qualitative and
   quantitative profile and was selected to further process our data (Fig.
   [138]S1). PCA analysis of normalized data denoted a clear separation of
   the groups suggesting overall differences in their proteomes
   (Fig. [139]5A). Differential abundance analysis revealed 32 and 33
   proteins with increased and decreased abundance in the experimental
   group (Fig. [140]5B). Further, the heatmap visualization of the top
   50-modulated proteins exhibited the different distribution patterns
   among the experimental and control groups (Fig. [141]5C). To obtain a
   molecular insight we performed a functional enrichment analysis using a
   network-based approach. First, we created a composite network
   comprising PPI between the modulated proteins by simulated diabetes
   (seed proteins) and their immediate interacting partners (highest
   confidence > 0.9) retrieved from STRING Database (incorporated in
   OmicsNet platform). The principal network using the up-modulated
   proteins consisted of 91 proteins, 137 edges and 40 seed proteins
   (nodes with black shadow) and is illustrated in Fig. [142]5D. The 10
   most significant (adjusted p value < 0.05) REACTOME and KEGG pathways
   on the global network are shown in Table [143]1. Generally speaking,
   signaling pathways linked to DNA/RNA metabolism, mitochondria and
   apoptosis were significantly enriched within the PPI network
   (Fig. [144]5E).

Figure 5.

   [145]Figure 5
   [146]Open in a new tab

   Simulated diabetes induced changes in the proteome of bovine coronary
   artery endothelial cells (BCAEC). (A) Principal Component Analysis
   (PCA) of LC–SWATH–MS^2 datasets. Data was log transformed without
   scaling. Shade areas depict the 95% confidence intervals. No scaling
   was used. (B) Volcano plot of all quantified proteins (Quantile
   normalization) displaying differences in relative abundance (> ± 20%
   change, < 0.05 p value cut-offs) between BCAEC cultured in control (NG)
   media and simulated diabetes (HG + HI) for twelve days. Values (dots)
   represent the HG + HI/NG ratio for all proteins. Red and blue dots
   denote downregulated and upregulated proteins in the HG + HI group
   versus NG group, respectively. (C) HeatMap of the top 50 proteins
   ranked by t-test. (D) Protein–protein interactome (> 0.9 confidence)
   generated using the list of the dysregulated proteins by simulated
   diabetes. Seed or input proteins are illustrated with a black shade and
   the gene ID is also shown. (E) Select enriched (adjusted p
   value < 0.05) REACTOME pathways^[147]40 within the protein network.
   Abbreviations: NG normal glucose, HG high glucose, HI high insulin.

Table 1.

   Pathway enrichment analysis of the dysregulated proteins by simulated
   diabetes.
   REACTOME database Total Hits FDR KEGG database Total Hits FDR
   Cell cycle 508 19 0.000192 Maturity onset diabetes of the young 149 9
   0.00248
   Cell cycle checkpoints 131 10 0.000259 Renal cell carcinoma 201 10
   0.00248
   G1/S transition 113 9 0.000489 SNARE interactions in vesicular
   transport 124 8 0.00248
   G1/S DNA Damage checkpoints 62 7 0.000606 Transcriptional misregulation
   in cancer 186 9 0.00549
   Activation of NF-kappaB in B cells 66 7 0.000671 Human T-cell leukemia
   virus 1 infection 162 8 0.00998
   Synthesis of DNA 95 8 0.000671 Phagosome 65 5 0.018
   DNA replication 102 8 0.000824 Aldosterone synthesis and secretion 69 5
   0.0206
   Regulation of activated PAK-2p34 by proteasome mediated degradation 48
   6 0.000824 Mitophagy—animal 72 5 0.0206
   Mitotic G1-G1/S phases 140 9 0.000824 Chemical carcinogenesis 201 8
   0.0206
   APC/C:Cdc20 mediated degradation of mitotic proteins 76 7 0.000824
   Human papillomavirus infection 155 7 0.0206
   [148]Open in a new tab

Integration of metabolomics and proteomics

   The signaling pathways perturbed by simulated diabetes were identified
   by a composite network of interacting metabolites and proteins using
   OmicsNet built-in databases. Figure [149]6 illustrates the composite
   metabolite-PPI network generated, using the dysregulated molecules
   (under simulated diabetes), comprised of 10 metabolites (seed
   metabolites), 32 edges, and 21 proteins (7 seed proteins). The enriched
   signaling pathways identified in the composite network are shown in
   Table [150]2. Generally speaking, signaling pathways linked to the
   metabolism of amino acids were significantly enriched within the
   protein-metabolite network. The pathways containing more hits
   (proteins) are highlighted (bue tones) in Fig. [151]6. We also noted a
   smaller interaction between the Acyl-protein thioesterase 1
   (LYPLA1,UniProtKB:[152]Q3MHR0) protein and a phosphatidylcholine
   metabolite.

Figure 6.

   [153]Figure 6
   [154]Open in a new tab

   Integrative network of the proteomic and metabolomic perturbations
   caused by simulated diabetes in bovine coronary artery endothelial
   cells (BCAEC). Composite protein-metabolite network created by OmicsNet
   using dysregulated proteins (pink nodes with black shade) and
   metabolites (gray nodes) in the HG + HI group (simulated diabetes).
   Interacting proteins (< 0.9 confidence) were retrieved from STRING
   Database and are shown as pink nodes without black shade.
   Abbreviations: NG normal glucose, HG high glucose, HI high insulin.

Table 2.

   Integrative pathway enrichment analysis of the dysregulated proteins
   and metabolites by simulated diabetes.
   REACTOME database Total Hits FDR KEGG database Total Hits FDR
   Metabolism of amino acids and derivatives 190 8 2.8E−6 Phenylalanine,
   tyrosine and tryptophan biosynthesis 5 4 3.9E−8
   Phenylalanine and tyrosine catabolism 9 3 5.3E−4 EGFR tyrosine kinase
   inhibitor resistance 1490 16 2.2E−7
   Metabolism 1490 12 1.5E−3 Phenylalanine metabolism 17 4 6.1E−6
   Abnormal metabolism in phenylketonuria 4 2 9.8E−3 Cysteine and
   methionine metabolism 49 5 6.7E−6
   ABC transporters 75 5 4.6E−6
   Antifolate resistance 18 3 5.2E−4
   Valine, leucine and isoleucine biosynthesis 4 2 1.5E−3
   Alanine, aspartate and glutamate metabolism 36 3 2.9E−3
   Tyrosine metabolism 36 3 2.9E−3
   [155]Open in a new tab

Cellular morphology

   To better understand the effects that simulated diabetes exerts on
   endothelial cells the changes on cellular structure endpoints were
   evaluated. The endothelial nuclei morphology in the BCAEC control and
   experimental groups were evaluated using fluorescent-staining and image
   analysis. We also evaluated the presence of vWF (marker of endothelial
   cells) in BCAEC and HCAEC, to reveal the cellular boundary and to
   demonstrate their endothelial phenotype^[156]47. We noted an increase
   in the percentage of binucleated BCAEC in the experimental group
   compared to the control group (top panel Fig. [157]7A and B). A similar
   result with larger nuclei, was observed when using HCAEC as a human in
   vitro model (bottom panel Fig. [158]7A and B). Finally, as expected, we
   observed a typical intracellular localization of vWF and a 100%
   positivity in endothelial cells.

Figure 7.

   [159]Figure 7
   [160]Open in a new tab

   Increased cellular binucleation by simulated diabetes in bovine
   coronary artery endothelial cells (BCAEC) and human coronary artery
   endothelial cells (HCAEC). (A) Representative immunofluorescence
   micrographs showing the localization of the von-Willebrand factor (vWf,
   1:400, 3% BSA in PBS) in fixed and permeabilized cells. The nuclei were
   stained using the dye Hoechst 33258 (2 µg/mL in HBSS). White arrows
   indicate binucleated cells. (B) Quantification of binucleated cells in
   HCAEC and BCAEC under simulated diabetes (HG + HI) versus control (NG)
   group. Fluorescence images were taken in at least three random fields
   per condition using an EVOS FLoid Cell Imaging Station with a fixed
   × 20 air objective. Image analysis was performed by ImageJ software
   (version 2.0.0). Abbreviations: NG normal glucose, HG high glucose, HI
   high insulin.

Discussion

   Endothelial cells cover the inner surface of blood vessels and are
   distributed across the body. Their functions include: acting as a
   mechanical barrier between the circulating blood and adjacent tissues
   as well as modulating multiple functions in distinct organs^[161]48.
   These regulatory functions vary according to localization and vascular
   bed-origin^[162]49. HG blood levels are detrimental to endothelial
   cells function in T2DM leading to coronary endothelial dysfunction and
   development of CVD^[163]50,[164]51. The molecular effects of HG on
   endothelial cells have been previously
   characterized^[165]4,[166]6,[167]7,[168]10,[169]11; nevertheless, the
   endothelial cell types used in these studies are not intrinsically
   involved in CVD. The present study used an in-vitro model involving
   endothelial cells that modulate heart function, CAEC^[170]52.
   Noteworthy, most in-vitro published studies have arbitrarily selected a
   single concentration of HG and time point as a means to decipher the
   molecular mechanisms altered by simulated
   diabetes^[171]4,[172]6,[173]7,[174]10,[175]11. Before selecting a
   particular HG concentration, we first tested the effects of increasing
   concentrations of glucose with and without HI on the BCAEC
   mitochondria. The mitochondrial membrane potential was selected as an
   endpoint and surrogate marker for mitochondrial function and
   metabolism^[176]19, given that mitochondria are highly relevant for
   glucose metabolism and ATP production and because mitochondrial
   dysfunction is thought to contribute to T2DM^[177]53. Our approach
   consisted of pre-treating (before challenging them with HG) cells with
   HI for 3 days to create a prolonged hyperinsulinemic environment and
   then challenging cells with HG + HI for up to 9 days, trying to mimic
   the pathophysiological conditions that occur in T2DM subjects wherein
   hyperinsulinemia precedes hyperglycemia^[178]18. We noted a significant
   decrease in the mitochondrial membrane potential when using 20 mmol/L
   HG + 100 nmol/L HI until day 9 (Fig. [179]S1), which is consistent with
   other reports using HG or HI on muscle cells^[180]54, endothelial
   cells^[181]55, epithelial cells^[182]56, and hepatocytes^[183]57.
   Mannitol, at equivalent concentrations on day 9, did not reduce the
   membrane potential; thus, we could rule out a hyperosmolar effect. We,
   therefore, for all our experiments, employed 20 mmol/L HG + 100 nmol/L
   HI for 9 days as the simulated diabetes model. Noteworthy, 9 days was
   the longest time point analyzed trying to mimic a chronic HG exposure
   and preventing measuring cell proliferation known to occur in early
   HG^[184]10,[185]12.

   Manual inspection of the proteomics data revealed dysregulated proteins
   involved in the signaling of insulin; a serine/threonine-protein
   phosphatase (PPP2R2B, [UniProtKB:[186]Q5E9Q7]) and 14-3-3 adapter
   protein (YWHAB, [UniprotKB: [187]P68250]). Elevated levels of the
   former protein, are linked to insulin resistance, reduced GLUT4
   translocation and glucose transport by inhibition of AKT activity; a
   serine/threonine kinase responsible for the phosphorylation of the
   insulin receptor substrate (IRS-1, a direct intracellular effector of
   the insulin receptor) and mediator of down-stream insulin
   signaling^[188]58. On the other hand, 14-3-3 adapters are up-stream
   proteins essential in the signaling by insulin^[189]59. When insulin
   binds to its receptor, 14-3-3 interacts with the insulin receptor
   substrate 2 (IRS-2) and promotes the activation of PI3-kinase
   (activator of AKT) and subsequent down-stream signaling. Simulated
   diabetes augmented and reduced the protein levels of PPP2R2B and YWHAB
   in BCAEC, respectively, which suggest impaired insulin signaling in our
   model.

   This LC–MS^2-based methodological pipeline that included appropriate
   controls during data acquisition (QC) and processing (e.g.,
   normalization, filtering, annotation, dereplication, etc.), allowed the
   identification of global changes in the metabolome and peptidome of
   CAEC under HG + HI. Using the GNPS/NAP pipeline we retrieved biological
   information at the metabolome level. Specifically, we noted increased
   abundance of valine, leucine, tyrosine, serine, leucine, proline,
   methionine, and glutamic acid in cells under HG conditions; and this is
   consistent with reports on human aortic endothelial cells^[190]60.
   Notably, several clinical studies have established a direct
   relationship between prevalence/incidence of T2DM and increased levels
   of valine, leucine and tyrosine in serum and plasma^[191]61–[192]65.
   Our results support the role of CAEC in contributing to the elevated
   pool of amino acids seen in circulation under a HG environment. We
   speculate that increased levels of these amino acids could result from
   either increased production or reduced utilization as suggested in
   endothelial cells (immortalized cell line, EA.hy 926) that transition
   from a glycolytic metabolism towards lipid and amino acid oxidation
   when challenged by HG^[193]66. Furthermore, evidence of increased
   tryptophan catabolism was identified through the kynurenine pathway. In
   this regard, a non-significant decrease of ~ 40% in the abundance of
   tryptophan was detected. However, a significant increase of ~ 450% in
   kynurenine (tryptophan’s main metabolite)^[194]67 between the HG + HI
   group and NG group was also observed, which is a key finding as
   elevated plasma levels of kynurenine are known to increase CVD
   risk^[195]68,[196]69. This novel finding contributes to expanding the
   understanding of amino acid metabolism in endothelial cells under
   simulated diabetes. Acetyl serotonin and melatonin which are components
   of the serotonin pathway that degrades tryptophan^[197]70 were also
   detected with only minor abundancy increases (20–30%) in the HG + HI
   group compared to control.

   In endothelial cells, the tripeptide glutathione (cysteine-glutamic
   acid-glycine) is believed to be the most critical antioxidant thiol
   scavenging reactive oxygen species when oxidative insults (e.g.,
   ambient HG) are triggered^[198]71,[199]72. Here, we noted an increased
   abundance of glutathione and its metabolite precursor glutamic
   acid^[200]71, suggesting an increased response to oxidative stress by
   ambient HG and HI. In line with this evidence, previous research
   reported a glutathione-dependent reaction to ambient HG in
   artery-derived endothelial cells^[201]73,[202]74 but the same could not
   be observed in vein-derived endothelial cells^[203]75,[204]76. That
   emphasizes the different responses to HG among endothelial phenotypes.
   The use of exhaustive and complementary dereplication tools further
   allowed us to provide evidence of up-regulated novel glutathione-based
   metabolites, suggesting a coordinated boost of antioxidant metabolite
   peptides. Likewise, antioxidant enzymes conforming the cells natural
   enzymatic defense^[205]77 were found with increased abundance in the
   experimental group, including peroxiredoxin-2 (PRDX2,
   [UniProtKB:[206]Q9BGI3], peroxiredoxin-6 [ PRDX6,
   UniProtKB:[207]O77834]), and mitochondrial thioredoxin (TXN2,
   [UniProtKB:[208]Q95108]). Our methodology also allowed us to
   identifying other peptide metabolites, including glutamic acid- and
   phenylalanine-based metabolites, presumably di- or tri-peptides,
   including the annotated metabolite glutamyl-phenylalanine and
   leucine-phenylalanine.

   To further extract biological information at the peptidome level, we
   performed an in-silico analysis with PeakView software on the same
   LC–MS^2 datasets used for untargeted metabolomics. GNPS/NAP and
   PeakView pipelines use MS^2 spectral matching between experimental and
   reference spectral data for the identification of peptides, but they
   rely on different reference spectral databases. Regarding peptide
   metabolites, GNPS/NAP^[209]26,[210]27,[211]30 pipeline is focused
   principally on the identification of peptidic natural products (as well
   as other types of metabolites), while the Paragon algorithm in PeakView
   software^[212]78 allows identifying endogenous peptides (by in-silico
   prediction) derived from an organism’s proteome when used as a
   reference (e.g., human, animals). The CAEC peptidome analysis by
   PeakView pipeline suggested an increase in proline-containing peptides.
   This type of peptide is of particular interest because of its
   resistance to non-specific proteolytic degradation, body distribution
   and remarkable biological effects^[213]79–[214]82. The origin of
   phenylalanine- and proline-based peptides may be derived from protein
   degradation supported by the noted increased protein abundance of core
   and regulatory subunits from the proteasome complex, including PSMA4
   [UniProtKB:[215]Q3ZCK9] and PSMD3 [UniProtKB:[216]Q2KJ46]). This
   complex is a central proteolytic system that degrades proteins,
   releasing peptides with 3 to 22 residues for further degradation into
   amino acids^[217]83. Yet, the question to be answered is if these
   peptides are biologically active or merely products of protein
   degradation. However, that is beyond the scope of our study.

   Metabolomic profiling also revealed changes in the lipidome of CAEC
   challenged with HG + HI, wherein a reduction in phosphatidylcholine
   (PC) lipids and subsequent increase in phosphocholine were noted.
   Changes in the phospholipidomic profile of bovine aortic endothelial
   cells treated with HG for 24 h has been reported in a lipidome
   study^[218]84. Here, proteomics and metabolomics data were manually
   integrated and this allowed to determine critical roles for PAFAH1B2
   (UniProtKB:[219]P68401) and LYPLA1 in mediating the degradation of PC
   lipids (Fig. [220]8). PAFAH1B2 was found to be up-regulated in this
   study and it is known to be associated with inflammation and higher
   levels of lysoPC^[221]85. As a result, PAFAH1B2 could increase the pool
   of lysoPC lipids, further exacerbating inflammation in the
   cardiovascular system^[222]86. On the other hand, LYPLA1 has a
   lysophospholipase activity that can hydrolyze a range of
   lysophospholipids, including LysoPC, thereby generating a fatty acid
   and glycerophosphocholine as products^[223]87. Increased levels of
   phosphocholine (~ 460%) were detected in HG treated cells compared to
   control, that could be associated with the degradation of LysoPC
   lipids. It should be noted that the use of pathways databases such as
   KEGG and REACTOME possess some limitations when dealing with lipid
   metabolites because its chemical diversity is not well
   annotated/defined within the databases. For example, KEGG provides a
   chemical class identifier instead of individual identity to lipids,
   constricting their biological importance^[224]88. Thus, based on our
   manual inspection of the metabolomics-proteomics data and in line with
   the evidence, we suggest that simulated diabetes evokes inflammation on
   BCAEC and that PAFAH1B2 and LYPLA1 play a role in modulating such
   process.

Figure 8.

   [225]Figure 8
   [226]Open in a new tab

   Summary illustration of study findings. Cellular structures were
   created using Servier Medical Art templates, which are licensed under a
   Creative Commons Attribution 3.0 Unported License;
   [227]https://smart.servier.com. Chemical structures were drawn by
   ChemDraw Professional version 16.0.1.4.

   Previously, we reported the multinucleation of CAEC cultured under
   simulated diabetes^[228]16. In the present study, we replicated our
   previous findings of increased binucleation in BCAEC. Notably, the same
   outcome was obtained when using HCAEC as a human in vitro model
   (Fig. [229]7A and B), validating the binucleation process in other
   CAEC, which indicates that such a process is not species specific but
   rather a true biological process. After refinement of LC–MS^2 data and
   bioinformatics re-processing of published SWATH-based datasets of BCAEC
   under simulated diabetes^[230]16, molecular signatures and pathways
   that could be linked to the binucleation process were found
   (Fig. [231]8). For instance, we noted an increased abundance of
   proteins, under simulated diabetes, with reported nuclei localization
   and linked to DNA metabolism, including ribosomal proteins RPS7
   (UniProtKB:[232]A6H769), RPS13 (UniProtKB:[233]Q56JX8), and RPL9
   (UniProtKB:[234]Q3SYR7)^[235]89. Further, we observed an increased
   abundance of proteasome proteins, PSMA4 and PSMD3, which are linked to
   protein metabolism^[236]90. Hence, we infer that the CAEC binucleation
   occurs as a compensatory mechanism to increase the cell capacity to
   metabolize the excess of ambient glucose by increasing the cell
   metabolic machinery (transcription/translation processes).

   Despite a lack of apparent increase in cell proliferation in the
   experimental group compared to control group after 12 days, an increase
   in overall protein abundance was inferred from the total ion
   chromatogram (TIC) of MS (Fig. [237]S1A). Previous studies have shown
   reduced endothelial cell proliferation (mostly in HUVEC) after
   long-term (7–14 days) HG exposure^[238]4,[239]11,[240]91–[241]97,
   accompanied by an increase in protein synthesis^[242]97. Although an
   increase in cell proliferation could boost a coordinated increase of
   ribosomal and proteasome proteins, we do not believe this is the case
   here, as mentioned before. After 4–5 days of simulated diabetes, cells
   occupied 100% of the well's plate surface, thereby impeding to harbor
   more cells because endothelial cells grow as a monolayer. This is
   consistent with findings stating that when endothelial cells become
   highly confluent, they stop growing due to cell–cell contact, even in
   the presence of growth factors^[243]98. In support of this, up-stream
   (CTGF [UniProtKB:[244]O18739]) and CD62
   [UniProtKB:[245]P98107])^[246]99,[247]100 (Table [248]S5) and
   down-stream proteins (FABP4 [UniProtKB:[249]P48035])^[250]101 (Table
   [251]S5) involved in angiogenesis and proliferation were down-regulated
   by simulated diabetes. Importantly, there is evidence (not in
   endothelial cells) of cellular processes contributing to the
   stimulation of cellular binucleation without increases in cell
   proliferation, including cellular enhancement of antimicrobial
   defenses^[252]102, senescence^[253]103, and malignancy^[254]104.
   Various mechanisms have been linked to the binucleation process, such
   as cytokinesis failure, cellular fusion, mitotic slippage, and
   endoreduplication^[255]105. The elucidation of the exact molecular
   mechanisms leading to the binucleation process of CAEC is beyond the
   scope of our study.

   The chosen period (9-days) to simulate diabetes may represent a
   limitation, as longer periods may better reflect chronicity and trigger
   more severe endothelial dysfunction. The findings observed in BCAEC may
   not be equivalent to those occurring in human cells. However, the
   binucletation process triggered in BCAEC by simulated diabetes was also
   observed in human endothelial cells.

   In conclusion, integration of omics and bio/chemoinformatics data
   revealed dysregulations in the metabolism of amino acids, peptides, and
   phospholipids, impaired insulin signaling, reduced mitochondrial mass,
   angiogenesis, and increased apoptosis and oxidative stress when CAEC
   were subjected to simulated diabetes. The appearance of
   non-proliferative binucleated CAEC cells is thought to be a strategy to
   metabolize the excess ambient glucose was also reported. Collectively,
   we believe that these dysregulated factors contribute to the
   development of CAEC dysfunction and may be associated with critical
   mechanisms underlying the onset of CVD in subjects with T2DM. We,
   therefore, suggest a multi-target therapeutic modality when protecting
   diabetic patients from CVD.

Supplementary Information

   [256]Supplementary Information.^ (2.7MB, docx)
   [257]Supplementary Table S1.^ (80KB, xls)
   [258]Supplementary Table S3.^ (108.5KB, xls)
   [259]Supplementary Table S5.^ (837.3KB, xlsx)

Acknowledgements