Abstract

   Type 2 diabetes mellitus (T2DM) is a major public health concern with
   significant cardiovascular complications (CVD). Despite extensive
   epidemiological data, the molecular mechanisms relating hyperglycemia
   to CVD remain incompletely understood. We here investigated the impact
   of chronic hyperglycemia on human aortic smooth muscle cells (HASMCs)
   cultured under varying glucose conditions in vitro, mimicking normal (5
   mmol/L), pre-diabetic (10 mmol/L), and diabetic (20 mmol/L) conditions,
   respectively. Normal HASMC cultures served as baseline controls, and
   patient-derived T2DM-SMCs served as disease controls. Results showed
   significant increases in cellular proliferation, area, perimeter, and
   F-actin expression with increasing glucose concentration (p < 0.01),
   albeit not exceeding the levels in T2DM cells. Atomic force microscopy
   analysis revealed significant decreases in Young’s moduli, membrane
   tether forces, membrane tension, and surface adhesion in SMCs at higher
   glucose levels (p < 0.001), with T2DM-SMCs being the lowest among all
   the cases (p < 0.001). T2DM-SMCs exhibited elevated levels of selected
   pro-inflammatory markers (e.g., ILs-6, 8, 23; MCP-1; M-CSF; MMPs-1, 2,
   3) compared to glucose-treated SMCs (p < 0.01). Conversely, growth
   factors (e.g., VEGF-A, PDGF-AA, TGF-β1) were higher in SMCs exposed to
   high glucose levels but lower in T2DM-SMCs (p < 0.01). Pathway
   enrichment analysis showed significant increases in the expression of
   inflammatory cytokine-associated pathways, especially involving IL-10,
   IL-4 and IL-13 signaling in genes that are up-regulated by elevated
   glucose levels. Differentially regulated gene analysis showed that
   compared to SMCs receiving normal glucose, 513 genes were upregulated
   and 590 genes were downregulated in T2DM-SMCs; fewer genes were
   differentially expressed in SMCs receiving higher glucose levels.
   Finally, the altered levels in genes involved in ECM organization,
   elastic fiber synthesis and formation, laminin interactions, and ECM
   proteoglycans were identified. Growing literature suggests that
   phenotypic switching in SMCs lead to arterial wall remodeling (e.g.,
   change in stiffness, calcific deposits formation), with direct
   implications in the onset of CVD complications. Our results suggest
   that chronic hyperglycemia is one such factor that leads to
   morphological, biomechanical, and functional alterations in vascular
   SMCs, potentially contributing to the pathogenesis of T2DM-associated
   arterial remodeling. The observed differences in gene expression
   patterns between in vitro hyperglycemic models and patient-derived
   T2DM-SMCs highlight the complexity of T2DM pathophysiology and
   underline the need for further studies.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s13036-024-00457-w.

   Keywords: Hyperglycemia, Biomechanics, Vascular smooth muscle cells,
   Diabetes, Gene expression, Transcriptomics, Pathway analysis

Introduction

   T2DM is a serious public health concern and a global burden. It is one
   of the leading causes of mortality affecting more than half a billion
   people worldwide, and directly contributing to at least a few million
   deaths annually [[36]1]. T2DM detection is difficult in its early
   stages leaving many people undiagnosed, and often independently leads
   to the onset of CVD by the time of diagnosis [[37]2–[38]4] T2DM is a
   chronic metabolic and inflammatory condition, marked by inadequate
   insulin secretion by pancreatic β-cells and gradual buildup of tissue
   insulin resistance, with significant implications in accelerated
   vascular aging and CVD [[39]5, [40]6]. T2DM is a complex multifactorial
   disease and has been implicated in the onset of coronary and peripheral
   artery diseases (e.g., atherosclerosis, stroke), microvascular (e.g.,
   retinopathy, nephropathy) and neuropathic complications, hypertension,
   dyslipidemia, and arterial stiffness [[41]7].

   When the insulin secretion in diabetic patients cannot maintain glucose
   homeostasis, it results in hyperglycemia, hyperinsulinemia,
   inflammation, and higher body fat content, which in turn dysregulate
   the immune system, adipokines, gut microbiota, and a host of other
   tissue functions, resulting in arterial remodeling and CVD [[42]8].
   Despite its prevalence and the availability of epidemiological data
   related to T2DM pathophysiology, the molecular mechanisms by which
   hyperglycemia contributes to CVD are still being elucidated [[43]9,
   [44]10]. In healthy vasculature, ECs play a crucial role in maintaining
   vessel homeostasis and VSMC contractile phenotype by releasing numerous
   factors (e.g., VEGF, NO). T2DM conditions lead to endothelial
   dysfunction, release of various inflammatory markers (e.g., ROS,
   interleukins, TNF-α), and elevated oxidative stress that disturbs this
   homeostasis and activates SMCs, resulting in vasoconstriction, thrombus
   formation, arterial remodeling, and the onset of CVD [[45]11–[46]14].
   When the functioning EC layer is compromised, VSMCs are exposed to high
   glucose levels in their microenvironment and the glucose molecules
   could easily pass through their cell membrane leading to intracellular
   hyperglycemia and their dedifferentiation [[47]15–[48]17]. Elucidating
   the role of chronic higher glucose levels on VSMC phenotype and
   genotype could offer insights into the fundamental mechanisms by which
   hyperglycemia progresses and offer therapeutic avenues for modulating
   undesirable outcomes.

   The direct contribution of any singular factor on T2DM complications is
   hard to isolate in both humans and animal models, especially that of
   hyperglycemic levels. VSMCs cultured in vitro in high glucose
   conditions (≥ 20 mmol/L) exhibited proliferation, migration, adhesion,
   necrosis via H[2]O[2] formation, and elevated secretion of various
   biomolecules (e.g., ROS, TNF-α, MMP-2), along with activation of
   numerous pathways (e.g., JNK, ERK, AKT, NF-κB, MAPK) [[49]18–[50]23].
   In these cultures, cells were typically exposed to high glucose levels
   for a short duration (3 –72 h), or rodent SMCs were used in lieu of
   human cells, which do not efficiently mimic the chronic buildup of high
   blood glucose levels in humans in vivo. The potential benefits of
   numerous drugs tested in such studies to regulate VSMC behavior in
   vitro or in animal models in vivo have not led to breakthroughs in
   clinical trials, suggesting that a deeper understanding of the
   co-expression between metabolomic, transcriptomic, and proteomic
   transformations in these cells under prolonged exposure to elevated
   glucose concentrations mimicking pre-diabetic and diabetic conditions
   is critical to delineate the specific role of high glucose levels in
   T2DM-initiated arterial remodeling.

   In this study, we investigated the role of hyperglycemic conditions on
   the activation of VSMC pathways that contribute to early onset of
   endothelial dysfunction and arterial remodeling. We cultured human
   aortic smooth muscle cells (HASMCs; isolated from multiple donors) for
   21 days to broadly mimic normal (5 mmol/L glucose), prediabetic (10
   mmol/L glucose), and diabetic (20 mmol/L glucose) states. The
   phenotypic and biomechanical changes in cells under these conditions
   were assessed using AFM and confocal imaging. Metabolomic, proteomic,
   and RNA-seq analysis were performed to compare with those from human
   T2DM−HASMCs. Our study reveals the biological pathways activated during
   early stages of T2DM and identifies crucial biomarkers triggered during
   hyperglycemic conditions.

Methodology

Cell cultures

   Cryopreserved HASMCs from multiple patients (lot 70008916, isolated
   from 44-year old Caucasian female; lot 2164581, isolated from 44-year
   old Caucasian female; lot 1917076, isolated from 40-year old Caucasian
   female) and T2DM−HASMCs (lot 3125, isolated from tunica media of
   fibrous plaques-free aorta of 49-year old type 2 diabetic Caucasian
   female) were purchased from American Type Culture Collection (ATCC, VA,
   USA), Thermo Fisher Scientific (Waltham, MA, USA), and CELL Application
   Inc. (San Diego, CA, USA). The cell culture medium was prepared using
   normal D-glucose (5 mmol/L) [[51]24] added to Dulbecco’s Modified Eagle
   Medium (L-DMEM) supplemented with 10% heat-inactivated fetal bovine
   serum (FBS), and 1% penicillin/ streptomycin. All cell culture medium
   supplies were from Thermo Fisher Scientific.

   For all the experiments, primary HASMCs and T2DM−HASMCs cells were
   initially passaged in T-25 culture flasks (Nunc™, Thermo Fisher
   Scientific) coated with 0.1% gelatin. Prior to experiments, cells were
   serum starved in L-DMEM (containing 1% heat-inactivated FBS, 1%
   pen-strep) for 24 h to synchronize cell cycles. Then, all HASMCs were
   exposed to varying D-glucose levels (5 mmol/L, 10 mmol/L, 20 mmol/L)
   [[52]25], and T2DM−HASMCs were exposed to 5 mmol/L D-glucose levels for
   21 days in 6−well plates coated with 0.1% gelatin (n = 6 wells/
   condition/ assay). To mimic diabetic conditions in vitro, VSMCs were
   initially cultured in normal glucose (5 mmol/L) levels for at least
   72 h, before switching to high glucose (> 20 mmol/L) levels for the
   rest of the study [[53]26]. Cell media was replaced every 48 h, and
   during this time, no significant decrease in glucose concentration was
   noted at all the culture conditions. The glucose concentration in the
   medium was measured using the GlucCell™ glucose monitoring system
   (CESCO Bioengineering Co., Trevose, PA, USA). The final cultures were
   harvested and processed for biomechanical testing using AFM,
   transcriptomic, proteomic, and metabolic data analysis omics analysis,
   and cytokines/ chemokines/ growth factors quantification. Normal HASMC
   cultures (receiving 5 mM/L glucose) served as baseline controls, and
   patient-derived T2DM-SMCs served as disease controls.

Phenotype assessment

   Separately, SMCs (5 × 10^4 cells/well) were seeded into 24-well plates
   and cultured under various glucose conditions for up to 7 days. At the
   end of each time point (days 1, 4, 7), 100 µL of MTT reagent (Sigma
   Aldrich) was added to each well and incubated for 1 h (37 °C, 5%
   CO[2]). Then, 100 µL of detergent added to each well, incubated for 1 h
   at room temperature, and the absorbance measured at 570 nm with a
   BioTek Synergy H1 Hybrid multi-mode microplate reader. Wells with
   complete media alone and no cells were processed in similar way. The
   fold-change in cell density was calculated by normalizing to the
   readout from the initial seeding density. SMCs under various glucose
   conditions as well as T2DM cells were imaged at random locations using
   a Zeiss Axiovert A1 fluorescence microscope equipped with Hamamatsu
   camera and image acquisition software. At least ten images per
   condition were analyzed using NIH ImageJ software to quantify the
   perimeter and area of the cells.

Metabolomic analysis

   After 21 days of culture, the medium was removed while maintaining the
   plates on ice, and the cell layers were sequentially washed with cold
   1× PBS and cold deionized (DI) water. The cell layers were then
   incubated for three minutes with methanol containing 5% acetic acid at
   4 °C and scraped using a cell scraper. The cell layers were pooled in a
   15 mL centrifuge vial, placed on a cell rocker for 30 min in a cold
   room, and sonicated on ice for 15 s at 0.5 pulse rate (Branson Digital
   Sonifier; Marshall Scientific, Hampton, NH, USA). Metabolites were
   extracted by vortexing the samples for 30 s at high speed, followed by
   centrifugation at 17,000 ×g for 10 min at 4 °C. The supernatants were
   carefully transferred to autosampler vials and analyzed immediately by
   HPLC-MS untargeted conditions.

   LC/QTOF analysis was performed with Agilent 6545 QTOF Mass Spectrometer
   coupled with Agilent 1290 Infinity II UHPLC system (Agilent Corp.,
   Santa Clara, CA, USA). Chromatographic separation was achieved with
   Waters XSelect HSS T3 (2.1 mm × 100 mm, 2.1 μm) column. The mobile
   phase was composed of water/0.1% formic acid (A) and acetonitrile/0.1%
   formic acid (B). The gradient elution conditions for both positive and
   negative modes are as follows: 0–6 min, 5% B; 6–8 min, 35% B; 8–30 min,
   90% B; 30–35 min, 5% B; 35–37 min, 5% B. Post-run equilibration time
   was set to 5 min, flow rate set to 0.3 mL/min, and column temperature
   set to 35 °C. Untargeted polar metabolomics data was acquired with
   Agilent MassHunter Data Acquisition software (Version B.10.1.48).
   Source parameters were set as follows: drying gas (N[2]) temperature
   300 °C and flow 11 L/min, sheath gas (N[2]) temperature 350 °C and flow
   11 L/min; nebulizer gas (N[2]) pressure 40 psi; capillary voltage
   2000 V; nozzle voltage 0 V; fragmentor voltage, 70 V; skimmer voltage,
   30 V; The data obtained was processed using molecular feature
   extraction and time alignment using Agilent MassHunter Profinder 10.0.
   A 100% in at least one sample group feature presence was used to remove
   irreproducible features. Extracted molecular features were imported
   into Agilent Mass Profiler Professional (MPP) and Metaboanalyst for
   identification and statistical analysis. Statistical thresholds of
   p < 0.05 and fold of change (FC) > 1.5 were chosen for selecting
   significant molecular features. Identification of molecular features
   was performed utilizing both METLIN database and in-house built
   libraries with mass deviation < 5 ppm. Identified compounds validation
   was performed using internal standards and recursive analysis of pooled
   QC sample.

Proteomics analysis

   After 21 days of culture (n = 6 wells/ condition), cell layers were
   harvested on an ice platform and centrifuged at 1.5 rcf for five
   minutes at 4 °C. Cell count was done using a mini automated cell
   counter (ORFLO Technologies, Ketchum, ID, USA). The cell supernatant
   solution was discarded, and the cell pellets were processed to extract
   the intracellular and extracellular proteins using the Compartmental
   Protein Extraction Kit (Millipore Sigma) protocols recommended by the
   vendor. The volume for each buffer used in our study was calculated
   based on cell count (3.8 × 10^6 to 4 × 10^6 cells). We separated
   cytoplasmic (C), nuclear (N), membrane (M), cytoskeleton (CS), and
   extracellular matrix protein (ECM) from each well (2 independent
   repeats). After the extractions, the samples were homogenized, and the
   protein concentrations measured were between 7.17 µg and 15 µg. The
   samples thus obtained comprise buffers that were not suitable for
   direct digestion and LC-MS/MS analysis. Hence, the samples were
   fractionated on an SDS-page gel, each gel lane was cut into five bands,
   the bands were digested with trypsin, and the digests were analyzed by
   LC-MS/MS (Thermo Scientific Orbitrap Exploris™ 480 Vanquish Neo UHPLC).
   LC-MS data was searched against the human SwissProtKB database using
   the program Sequest. Spectral count and peptide molecular weight was
   used to estimate the relative abundance of proteins in these samples.
   More than 5400 proteins were identified in the C samples (e.g., GAPDH,
   actin, myosin-9, pyruvate kinase, tubulin, filamin-A, filamin-C), and
   over 4900 proteins were identified in the CS samples (e.g., vimentin,
   myosin-9, actin, pyruvate kinase PKM, filamin-C, GAPDH). The final
   normalized data were calculated in log[10]-fold change to determine the
   magnitude of change in protein abundance between the experimental
   groups. Such changes were used to identify up- and down- regulation of
   genes. Furthermore, pathway enrichment analysis for differentially
   expressed genes using ReactomePA [[54]27] enabled identification of
   biological pathways that are differentially regulated.

Cytokine/ chemokine/ growth factors analysis

   The cytokines, chemokines and growth factors released in HASMC and
   T2DM−SMCs cultures were quantified using Discovery Assays^® (Eve
   Technologies, Alberta, Canada) as we detailed earlier [[55]28].
   Briefly, the pooled spent media from each culture condition at day 21
   were processed using Human Cytokine/ Chemokine 71-Plex Discovery
   Assay^® Array (sensitivity: 0.5–10 pg/mL) and Human MMP & TIMP Panel
   assay (sensitivity: 0.5–14 pg/mL). Analytes were quantified using
   multiplex LASER bead technology and bead analyzer (Bio-Plex 200), where
   antibody-conjugated fluorophore beads simultaneously detect multiple
   analytes from a single assay.

RNA isolation and sequencing

   RNA was extracted from cultured HASMCs and T2DM−HASMCs using Trizol RNA
   isolation reagent protocol (Thermo Fisher Scientific). Briefly, cell
   layers were washed with cold 1× PBS, and 1 mL of Trizol reagent was
   added to each well, and cells were harvested using a cell scraper.
   Cells were homogenized using a sonicator (Branson Digital Sonifier) and
   incubated at room temperature for five minutes. A 200 µL aliquot of
   chloroform was added to the cell supernatant and the colorless aqueous
   phase containing RNA was separated carefully without disturbing
   interphase. To ensure that each library meets the required standards
   for sequencing, the library quantity and quality are evaluated using
   Tapestation and Qubit. Tapestation (Agilent 4200 Tapestation System)
   high-throughput quality control device was used for RNA fragment
   analysis and a 2100 Agilent Bioanalyzer was used for checking RNA
   integrity number (RIN) score (that ranged from 9.6 − 9.8). Further, a
   Qubit Fluorometer (Invitrogen) was used to measure accurate
   concentration of RNA samples. All the samples were pooled, and molarity
   for sequencing is determined via qPCR (QuantaBio Q qPCR). Finally, the
   pooled libraries were sequenced using Novaseq 6000 platform with
   Illumina’s sequencing reagents. Data processing and quality control
   checks were performed on raw fastq files. Subsequently, the readings
   were aligned with bowtie2 and quantified using RSEM (RNA-Seq by
   Expectation-Maximization). Differential expression analysis was
   conducted on mapped read counts obtained from RSEM using the
   R/Bioconductor package EBSeq. The analysis identified upregulated and
   downregulated genes, which were then used for Gene Set Enrichment
   Analysis and Venn diagram plotting.

Confocal microscopy

   HASMCs under three different conditions mimicking healthy, prediabetic
   and diabetic conditions, and T2DM−HASMCs were cultured for 21 days in
   multi-chamber cell culture slides (Thermo Fisher Scientific). The cell
   culture medium was discarded, and cells were washed with 1× PBS, fixed
   with 4% paraformaldehyde for 30 min, permeabilized with 0.1% Triton
   X-100 in PBS for 5 min, and blocked with 2% bovine serum albumin (BSA)
   for 30 min. The cells were washed with 1× PBS and incubated with
   phalloidin-iFluro 488 (Abcam) and 4′,6-diamidino-2-phenylindole (DAPI,
   Sigma), at room temperature for 90 min, to stain for F-actin and
   nuclei, respectively. Cells were washed with 1× PBS to remove excess
   staining solution and imaged using a Nikon A1Rsi confocal laser
   scanning microscopy. To quantify actin expression in SMCs exposed to
   varying glucose levels, images were captured with a 40× objective,
   transformed into grayscale images, and mean fluorescence intensity of
   actin filaments analyzed using Fiji ImageJ software. Three independent
   experiments were performed. Several fields of view of equal size per
   glucose treatment were randomly selected and mean fluorescence
   intensity of actin expression were measured and compared to controls.

Atomic force microscopy

   HASMCs were cultured for 7 days in 60−mm gelation-coated petri dishes
   under various glucose concentrations, while T2DM−HASMCs were cultured
   under normal glucose conditions. Cells were maintained at 37 °C for
   live-cell nanoindentation performed using an MFP-3D-Bio AFM (Asylum
   Research, Oxford Instruments, Santa Barbara, CA) mounted on an inverted
   optical microscope (Nikon Eclipse Ti). Tip-less AFM cantilevers (Nano
   World, Arrow TL 1, nominal spring constant ~ 0.03 N/m) were modified by
   attaching a 5-µm polystyrene bead using epoxy. The actual spring
   constant was determined using the thermal calibration method in a clean
   culture dish before each experiment and thermal/mechanical equilibrium
   of AFM was achieved by submerging probe in cell medium for
   stabilization. For each condition, at least 50 cells were randomly
   chosen and intended at random locations at 0.3 Hz with constant trigger
   point of 0.3 V. Force-indentation curves were obtained and Young’s
   modulus (E[Y]) was determined from these curves using Hertz’s contact
   model. The force of adhesion (F[ad]) and tether forces (F[T]) were
   obtained from the force-indentation curves, from which membrane tension
   ( Inline graphic ) and tether radius ( Inline graphic ) were calculated
   [[56]29]. Frequency distribution and one-way ANOVA were performed for
   statistical analysis and p < 0.05 was considered significant.

RNA-seq data analysis

   RNA-seq reads were mapped to the human reference genome (version:
   GRCh38) and corresponding annotated protein-coding genes using Bowtie
   [[57]30], allowing up to 2-mismatches. The gene expected read counts
   and Transcripts Per Million (TPM) were estimated by RSEM [[58]31]. The
   TPMs were further normalized by EBSeq [[59]32] R package to correct the
   potential batch effect. The EBSeq package [[60]32] was further used to
   assess the probability of gene expression (mRNAs) being differentially
   expressed between any two given conditions. We required that DEGs
   should have FDR < 5% via EBSeq and > 2 fold-change of “median-by-ratio
   normalized read counts + 1”.

Pathway enrichment analysis

   The Reactome pathway enrichment analysis was performed by the
   “ReactomePA” R package [[61]33].

Statistical analysis

   To evaluate the changes in SMC phenotype, metabolomics, proteomics, and
   RNA-seq, at least n = 6 wells/ condition/ cell type/ assay were
   cultured. The spent media from these wells were pooled and processed
   for cytokine/ chemokine/ growth factor analysis. For AFM analysis, at
   least 50 cells from each condition were analyzed. Data from human SMCs
   and T2DM SMCs were pooled and expressed as scatter plots with
   mean ± SEM where appropriate. Descriptive statistical analyses were
   conducted using the GraphPad Prism 10 software. A one-way ANOVA with
   post hoc Tukey’s test was used for multiple comparisons of the results
   from various assays, with statistical significance set at p < 0.05.

Results and discussion

Hyperglycemic conditions induce phenotypic changes in human aortic SMCs

   Significant differences in morphology and phenotype were evident in
   SMCs exposed to various glucose conditions (Fig. [62]1, A, B). It was
   noted that cells were more aggregated, irregular in shape, and larger
   in cross-sectional area within high glucose cultures, contrasting with
   the regular and standalone appearance in 5 mmol/L cultures. T2DM cells
   lacked the hill and valley configuration seen in normal SMCs, in line
   with that reported by others [[63]34]. Analysis of the cell
   morphologies from multiple such images revealed that the cell area and
   perimeter significantly increased at higher glucose concentration
   (Fig. [64]1, C; p < 0.05), whereas T2DM cells were the largest in size
   (p < 0.05) among all the cases. This is to be expected because high
   glucose levels reportedly have differential effects on the phenotype
   and function of many cell types. The cell morphology was analyzed using
   the cell shape index, i.e., Inline graphic , where A and P are the
   measured cell area and perimeter, respectively [[65]35]. Results
   indicated that SMCs exposed to 10 mmol/L and 20 mmol/L glucose had CSI
   around 0.23 ± 0.01, whereas T2DM cells and normal SMCs (5 mmol/L) had
   CSI around 0.48 ± 0.03 (p < 0.001). It should be noted that the area
   and perimeter of the SMCs in control (5 mmol/L glucose) cultures is in
   broad agreement with literature [[66]36]. Quantitative analysis
   revealed elevated levels of F-actin expression in SMCs receiving higher
   glucose concentrations (Fig. [67]1, D, E). The area covered by actin
   within the cells as well as the fluorescence intensity of actin were
   significantly higher in 20 mmol/L glucose treated cells and T2DM-SMCs
   compared to cells receiving 5 mmol/L or 10 mmol/L glucose (p < 0.05).
   Collectively, these results suggest that exposure of VSMCs to elevated
   glucose levels for longer periods induces significant phenotypic
   changes in these cells, broadly comparable to their T2DM counterparts.

Fig. 1.

   [68]Fig. 1
   [69]Open in a new tab

   A. Representative phase-contrast images of adult human aortic SMCs
   cultured under varying glucose concentrations (5, 10, or 20 mmol/L),
   and T2DM SMCs. B. Representative confocal images of filamentous-actin
   (F-actin) and nuclei (blue) stained with Alexa Fluro 488-phalloidin and
   DAPI, respectively. C. Significant differences in average cell area and
   perimeter were noted between SMCs treated with various dosages of
   glucose and with T2DM SMCs. Data shown represents mean ± standard error
   in respective cases (n > 50 cells/condition). **** indicates p < 0.0001
   vs. 5 mmol/L cultures; #### indicates p < 0.0001 vs. 10 mmol/L
   cultures; ++++ indicates p < 0.0001 vs. 20 mmol/L cultures. D.
   Quantitative analysis of area occupied by F-actin within SMCs exposed
   to various culture conditions. E. Quantitative analysis of fluorescence
   intensity of F-actin. Analysis of fluorescence intensity was done at
   the original magnification by measuring the mean gray value with Fiji
   ImageJ software. Data was pooled from n = 6 wells/ condition for each
   of these assays. Biomechanical characteristics such as elastic modulus
   (E[Y], F), forces of adhesion (F[ad], G), membrane tether forces (F[T],
   H), membrane tension (T[M], I), and tether radius (R[T], J) were
   quantified from the AFM data. E[Y] data were calculated by applying
   Hertz model to force–indentation curves (n ≥ 100 cells/condition)
   obtained from cells. F[ad] and F[T] were measured by retraction of
   beaded-AFM probe from the cell surface. In plots D-J, the center line
   in the box plots denotes the median, and bound of box shows 25th to
   75th percentiles, while upper and lower bounds of whiskers represent
   the maximum and minimum values, respectively. * indicates p < 0.05, **
   indicates p < 0.01, *** indicates p < 0.001, and **** indicates
   p < 0.0001. K. Proliferation of human SCMs under various glucose
   conditions as well as that of T2DM-SMCs. * indicates p < 0.05 vs.
   initial cell seeding density, ## indicates p < 0.01 for control (5
   mmol/L) vs. glucose treatment (10 mmol/L, 20 mmol/L; T2DM)

   The Young’s modulus (E[Y]) of SMCs in normal glucose conditions (5
   mmol/L) was noted as 7.03 ± 2.01 kPa (Fig. [70]1, F). While 10 mmol/L
   glucose did not contribute to significant changes in E[Y] (7.78 ± 1.65
   kPa; p > 0.05 vs. 5 mmol/L), 20 mmol/L glucose significantly reduced
   E[Y] to 3.32 ± 1.3 kPa (p < 0.05 vs. 5 mmol/L; p < 0.05 vs. 10 mmol/L
   glucose). T2DM cells exhibited E[Y] (3.19 ± 0.84 kPa) similar to SMCs
   that received 20 mmol/L (p < 0.01 vs. 5 mmol/L; p < 0.01 vs. 20
   mmol/L). The E[Y] of SMCs in 5 mmol/L glucose conditions was similar to
   our previous study [[71]37], whereas the E[Y] of T2DM-SMCs is in close
   range to values reported in literature [[72]38]. Human coronary SMCs
   derived from healthy patients reportedly had higher E[Y] than those
   isolated from T2DM patients [[73]38], in agreement with our results
   here. However, in coronary vascular SMC cultures, isolated from control
   or diabetic mice, no significant impact of glucose concentration on
   E[Y]was reported [[74]38]. SMCs receiving 5 mmol/L glucose recorded
   adhesion forces (F[ad]) of 558 ± 229 pN (Fig. [75]1, G), and increasing
   the glucose concentration significantly decreased these adhesion forces
   (p < 0.05 vs. 5 mmol/L; p < 0.05 for 10 mmol/L vs. 20 mmol/L). T2DM
   cells had the lowest adhesion forces of all the cases tested (233 ± 71
   pN; p < 0.05 vs. all other cases). The tether forces (F[T]) recorded in
   SMCs cultured with 5 mmol/L glucose was 787 ± 342 pN (Fig. [76]1, H),
   and while 10 mmol/L glucose had no effect on these values, 20 mmol/L
   significantly reduced the F[T] (286 ± 109 pN; p < 0.05 vs. 5 mmol/L and
   10 mmol/L). T2DM cells recorded the lowest F[T] among all the test
   cases and was not significantly different from 20 mmol/L condition.

   The force needed to deform a cell membrane, also termed as the apparent
   membrane tension (T[M]), was calculated from tether forces using Inline
   graphic , where K[B] is the bending stiffness that lies in the 0.1–0.3
   pN.µm range [[77]39, [78]40]. The T[M] values were as follows
   (Fig. [79]1, I): 5 mmol/L (50.4 ± 2.3 nN/µm), 10 mmol/L (53.7 ± 3.2
   nN/µm), 20 mmol/L (11.4 ± 0.9 nN/µm), and T2DM (3.5 ± 0.2 nN/µm). The
   T[M] was significantly lower in cells receiving 20 mmol/L glucose
   versus other glucose concentrations (p < 0.01), and T2DM cells have the
   lowest T[M] of all the cases tested (p < 0.01). The tether radius
   (R[T]) increased with increasing glucose concentration (Fig. I, J),
   with control (5 mmol/L) cells having 1.9 ± 0.03 nm on average and T2DM
   cells having 6.1 ± 0.13 nm. To the best of our knowledge, the F[T],
   F[ad], T[M] and R[T] values for human SMCs and specifically under
   diabetic conditions haven’t been reported earlier in literature.

   Previously, we reported that primary SMCs derived from human aneurysmal
   aortae and analyzed using AFM exhibited the following values [[80]41]:
   E[Y] = 20.9 ± 7.7 kPa, F[ad] = 1.87 ± 0.13 nN, F[T] = 218.8 ± 14.3 pN,
   T[M] = 6.07 ± 0.8 nN/µm, and R[T] = 2.91 ± 0.19 nm. Compared to T2DM
   SMCs and SMCs receiving higher glucose in the current study, it is
   evident that human aneurysmal aortic SMCs (yet another CVD condition)
   have significantly higher E[Y], F[ad] and R[T] (p < 0.01 in all the
   cases), although the tether forces and membrane tension of T2DM SMCs
   were comparable to that in aneurysmal SMCs, underlying the differences
   in the pathologies of these two vascular diseases.

   Compared to SMCs cultured for 24 h, cell proliferation increased
   1.3–1.75 −fold after 48 h, 4.1–5.35 −fold after 96 h, and 5.5–6.1 −fold
   after 7 days (Fig. [81]1, K) in all the cases. Cell proliferation
   increased with time at each glucose concentration, as well as in T2DM
   cultures. Our findings are consistent with previous reports where high
   glucose led to significant increase in the proliferation of various
   cell types compared to normal glucose levels [[82]42]. For instance,
   previous studies reported a 3−fold increase in VSMC proliferation
   exposed to high glucose (> 20 mmol/L) compared to controls (5 mmol/L),
   as measured using the MTT assay [[83]43–[84]45].

   Hyperglycemia has been linked to numerous facets of arterial modeling.
   Elevated glucose levels have been shown to alter trophoblast
   proliferation and invasion, thereby contributing to SMC mediated
   arterial remodeling during fetal growth and development [[85]46]. T2DM
   patients with higher glucose levels had larger common carotid artery
   intima-media thickness, luminal diameter, and brachial pulse pressure
   compared to their healthy counterparts, suggesting adaptive remodeling
   of stiffened arteries [[86]47]. Clinically, T2DM has been implicated in
   remodeling of calcified arteries that exhibit significantly greater
   necrotic cores and inflammation, primarily driven by macrophages and
   lymphocytes [[87]48]. Hyperglycemia induces significant changes in
   vascular ECM composition and remodeling capabilities, ultimately
   leading to compromised vessel function and stiffer vessel walls
   [[88]49]. A majority of these changes are driven by alternations in SMC
   phenotype, function, and mechanobiology, resulting in diseases such as
   stroke, aneurysm, and hypertensive vessels [[89]16, [90]50].

Differential effects of hyperglycemia on cytokine/ chemokine/ growth factor
release by SMCs

   The release of cytokines and chemokines in chronic SMC cultures were
   quantified (Fig. [91]2) and significant variations in analyte
   concentrations depending on glucose concentration were noted. Some
   interleukins that weren’t expressed at all or released in extremely low
   levels by T2DM cells (e.g., IL-9, IL-17 A, IL-17 F, IL-28 A, IL-4,
   IL-15, IL-25, IL-33) were seen in SMC cultures receiving glucose
   (Fig. [92]2, A). On the other hand, interleukins such as IL-6, IL-8,
   IL-23 and IL-27 were released in higher levels in T2DM cells than in
   SMCs receiving glucose. Among various cytokines and chemokines tested
   (Fig. [93]2, B), higher levels of SCD40L, RANTES, GROα, MCP-1, M-CSF,
   and MIP-1δ were noted in T2DM cultures than in SMC cultures receiving
   glucose. On the other hand, G-CSF was higher in SMC cultures receiving
   glucose than in T2DM cultures. There weren’t any significant
   differences in the expression of other cytokines and chemokines between
   the various culture conditions. T2DM has been linked to immune system
   disorders as well as elevated levels of IL-6, IL-18, MCP-1, and TNF-α
   [[94]51–[95]53]. Although TNF-α, IL-18, and IFN-γ levels were similar
   in all the cases we tested, IL-6 and MCP-1 levels were significantly
   higher in T2DM cells consistent with literature.

Fig. 2.

   [96]Fig. 2
   [97]Open in a new tab

   Heat maps of the levels of interleukins (A), cytokines & chemokines
   (B), growth factors (C), and MMPs/TIMPs (D) released in SMC cultures
   receiving various glucose concentrations and from T2DM cells over 21
   days. Spent media was pooled from at least n = 6 wells/ condition and
   processed to measure the levels of these markers

   FGF-2 levels were slightly higher in SMC cultures receiving 5 and 10
   mmol/L glucose, whereas TGF-β2 and TGF-β3 levels were significantly
   lower in T2DM cultures (Fig. [98]2, C). The levels of VEGF-A, PDGF-AA,
   and TGF-β1 were higher in cultures receiving 10 and 20 mmol/L glucose.
   Similar to our observation, high glucose (20 mmol/L) was shown to
   elevate TGF-β1 and TGF-β-R1 receptor expression in vascular SMCs, via
   protein kinase C (PKC-α) activation [[99]54]. Hyperglycemia was shown
   to induce VEGF-A expression in SMCs similar to acute insulin treatment
   [[100]55], with implications in decreased function and failure of
   multiple organs (e.g., retina, kidneys). Our study showed that VEGF-A
   levels were significantly elevated (> 40-fold; p < 0.001 vs. 5 mmol/L)
   in the presence of 10 mmol/L and 20 mmol/L glucose to levels noted in
   T2DM cells.

   MMPs–1, 2, 3 and TIMPs–1, 2, 3 were elevated in T2DM cells than
   glucose-receiving HASMC cultures (Fig. [101]2, D). These results
   suggest that SMCs treated with varying glucose concentrations directly
   influence the expression of various cytokines, chemokines, growth
   factors and MMPs/ TIMPs. High glucose exposure was shown to increase
   transcription and translation of MMPs−1, 2, 9 and 13 in human SMC
   cultures and their enzymatic activity, especially in the presence of
   macrophages, representative of T2DM conditions in vivo [[102]56].
   Consistent with this report, we here note that these specific MMPs were
   significantly higher in T2DM cultures, whereas MMPs−2, 9 and 12 were
   also present in SMC cultures receiving high glucose levels. The
   interplay between MMPs and TIMPs regulate the development of
   atherosclerotic plaques and arterial remodeling under diabetic
   conditions [[103]57]. In humans with T2DM and hypertension, elevated
   levels of TIMPs-1 and 3, TIMP-1: MMP-2 ratio, and TIMP-1: MMP-9 ratio
   were reported [[104]58, [105]59], which mirrors our observations in
   this study.

   It is worth noting that previous studies suggest that when human aortic
   SMCs were co-cultured with macrophages under normal (5.5 mmol/L) and
   high glucose (25 mmol/L) conditions, SMCs exhibited augmented gene and
   protein expression of MMP-1 and MMP-9, significant increase in MMP-9
   enzymatic activity, higher levels of soluble and functionally-active
   MCP-1 linked to MMPs upregulation, and activated PKCα signaling pathway
   that together with NF-kB is responsible for MMPs-1, 9 upregulation
   [[106]56].

Inflammatory cytokines-associated pathways are enriched in hyperglycemia
induced upregulated genes

   Previous research has independently analyzed the transcriptome,
   proteomics, and metabolomics to identify distinct markers in diabetic
   SMCs [[107]60]. Our current study, however, profiles these as paired
   omics data. We explored which genes correspond with metabolomics
   profiling patterns and the implications of these correlations within
   the omics modules. We also examined how mRNA levels align with protein
   abundance. This integrated dataset analysis deepens our understanding
   of chronic hyperglycemia-induced changes in human aortic smooth muscle
   cells, advancing beyond mere biomarkers to explore their complex
   relationships.

   We first investigated the genes upregulated by hyperglycemia enriched
   pathways. Vascular SMCs receiving 5 mmol/L glucose were compared to
   those receiving 10 mmol/L (Fig. [108]3, A) or 20 mmol/L (Fig. [109]3,
   B) glucose, and T2DM-SMCs (Fig. [110]3, C). Pathway enrichment analysis
   was further performed by the differentially expressed genes (DEGs). We
   found that inflammatory cytokine-associated pathways, especially those
   involving IL-10 signaling, are prominently enriched in genes that are
   up-regulated by increased glucose levels of 10 mmol/L and 20 mmol/L
   glucose, compared to normal glucose levels. IL-10 signaling pathway has
   been shown to modulate the function of various immune cells. Studies
   have shown that elevated IL-10 pathways are associated with multiple
   autoimmune diseases [[111]35]. Additionally, we observed that genes
   activated by a higher glucose concentration of 20 mmol/L are more
   closely associated with inflammatory cytokine pathways, including not
   only IL−10 but also ILs − 1, 4 and 13 signaling pathways (Fig. [112]3,
   B). This aligns with the hypothesis that long-term exposure to high
   glucose levels leads to enhanced inflammatory responses, which are
   associated with chorionic inflammation. The genes involved in the
   activation of G-alpha signaling events were significantly enriched in
   SMCs receiving 20 mmol/L glucose but not in SMCs supplied with 10
   mmol/L glucose. G-alpha is involved in the inhibition of cAMP dependent
   pathway which in turn leads to reduced activity of cAMP-dependent
   protein kinases, as well as activation of the protein tyrosine kinase
   c-Src [[113]61, [114]62].

Fig. 3.

   [115]Fig. 3
   [116]Open in a new tab

   Volcano plots of differential gene expression patterns and enriched
   pathways in response to glucose concentration and diabetic conditions.
   (A) 10 mmol/L vs. 5 mmol/L glucose, (B) 20 mmol/L vs. 5 mmol/L glucose,
   and (C) T2DM Vs 5 mmol/L glucose. Cell pellets were pooled from n = 6
   wells/ condition for RNA-seq analysis

   The epidermal growth factor receptor family (EGFR or ErbB 1−4) has been
   implicated in various cellular functions (e.g., growth, division,
   differentiation, migration, apoptosis) and multiple downstream
   signaling pathways (e.g., ERK1/2, MAP, PI3-kinase/Akt) under
   hyperglycemia or diabetes (types I and II) conditions and related
   cardiovascular outcomes [[117]63]. In our study, compared to normal
   glucose levels, chronic exposure to 10 mmol/L but not 20 mmol/L glucose
   appears to have resulted in differentially expressed genes related to
   ErbB2 and ErbB4 and their functions and pathways (cell motility, PI3K,
   PTK6, SHC1) in human vascular SMCs.

   We found a notable distinction between the gene expression patterns in
   cells exposed to high glucose levels in vitro (10 or 20 mmol/L) and
   those compared to T2DM-SMCs (Fig. [118]3, C). Despite some
   similarities, the up-regulated genes in T2DM cells did not entirely
   correspond with those observed in the high glucose in vitro models.
   This could possibly be due to the multifactorial nature of T2DM in vivo
   and exposure of SMCs to chronically (multi-year) elevated levels of
   high glucose and inflammatory molecules under such conditions. In
   T2DM-SMCs, the dominant pathways involved relate to ECM organization
   (e.g., laminin, elastin, collagens, proteoglycans, GAGs) and the
   regulation of IGF, highlighting the complexity of diabetes pathogenesis
   beyond the changes induced by hyperglycemia alone. This is to be
   partially expected because T2DM diagnosis could also be an indicator of
   the onset of various proteolytic vascular conditions such as
   atherosclerosis that results in arterial remodeling and SMC activation,
   which is reflected in the cells isolated from a T2DM tissue.
   Interestingly, IL-10 pathways were enriched in both the in vitro models
   and in T2DM-SMCs, suggesting that increased inflammatory
   cytokine-associated pathways are a consistent feature of prolonged
   hyperglycemia. This could potentially contribute to the pathogenesis of
   diabetic complications, particularly in vascular tissues, implying that
   controlling inflammation may be crucial in preventing these
   complications. Interestingly, compared to SMCs receiving normal glucose
   levels, T2DM cells had altered gene expression for molecules involved
   in ECM organization, elastic fiber synthesis and formation, laminin
   interactions, and ECM proteoglycans, highlighting the role of T2DM in
   arterial remodeling.

   Similar outcomes were noted from the differentially regulated gene
   (DGE) analysis (Fig. [119]4) performed on these cells. We performed
   statistical analysis on the normalized read count data to assess
   quantitative changes in expression levels between the various groups.
   Compared to SMCs receiving normal glucose levels, 513 genes were
   upregulated, and 590 genes were downregulated in T2DM cells (list of
   genes provided in Table [120]S1). We note that, among others, the genes
   involved in arterial remodeling were significantly upregulated in T2DM
   cells (e.g., COL, ELN, GLB, FBLN, LMN). We performed a ReactomePA
   analysis to identify the pathways enriched for the upregulated and
   downregulated genes, respectively). Cells receiving 10 and 20 mmol/L
   glucose have much fewer genes that were differentially expressed, while
   very few of these genes were common between all the cell types.

Fig. 4.

   [121]Fig. 4
   [122]Open in a new tab

   Venn diagram indicating the overlap between differentially expression
   genes (DEG) in SMCs receiving various glucose concentrations (10 mmol/L
   and 20 mmol/L). RNA-seq data for each condition was compared to cells
   receiving 5 mmol/L glucose. T2DM cells were also shown for comparison.
   (A) Up-regulated differentially expressed genes; (B) down-regulated
   differentially expressed genes. Cell pellets were pooled from n = 6
   wells/ condition for RNA-seq analysis

   In the medial layers of healthy vascular ECM, circumferential layers of
   collagens, elastic fiber associated proteins (elastin, fibrillin,
   fibulins, microfibril-associated glycoproteins), as well as basement
   membrane proteins (collagen type IV, laminins, fibronectin) are present
   [[123]64]. The signaling from healthy collagens and elastic fibers
   maintain vascular SMCs in a quiescent, differentiated state and
   contributes to normal SMC functions. Vascular injuries and diseases
   such as T2DM induce changes in the tissue ECM (e.g., proteolysis of ECM
   proteins) and activates and dedifferentiates SMCs to a more
   proliferative and invasive phenotype, disturbing the ECM protein-SMC
   signaling pathways [[124]49]. Hyperglycemic conditions were shown to
   increase osteogenic genes expression in vascular SMCs, and enhance the
   stiffness of arterial elastin and glycation of elastic fibers, leading
   to arterial calcification associated with diabetes [[125]65]. Others
   reported that hyperglycemia and T2DM conditions significantly alter the
   expression of ECM genes such as elastin, fibulin, laminin, and collagen
   [[126]66–[127]69], which in turn contribute to undesirable outcomes in
   arterial remodeling. Our results indicate upregulation of COL, ELN,
   GLB, FBLN, LMN genes in T2DM cells, highlighting their role in disease
   pathogenesis and arterial remodeling. Upregulation of these genes could
   lead to significant increases in their protein synthesis and
   deposition, which contributes to ECM remodeling, cell-ECM interactions,
   and cell migration.

   Using a microarray dataset ([128]GSE26168) from the Gene Expression
   Omnibus database, Zhu et al.. identified 981 DEGs, of which 301 were
   upregulated and the rest downregulated [[129]70]. These DEGs were
   highly enriched in cell differentiation, cell adhesion, intracellular
   signal transduction, and regulation of protein kinase activity, as well
   as cAMP signaling pathway, Rap1 signaling pathway, regulation of
   lipolysis in adipocytes, PI3K-Akt signaling pathway, and MAPK signaling
   pathway. Based on the PPI network of these DEGs, the top 6 genes
   contributing to T2DM initiation, progression, and intervention strategy
   were identified as PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. While
   none of these specific genes were in measurable levels in our cultures,
   GNG2, CDC20, CDCP1, and ITGB8 were upregulated, whereas PIK3IP1 and
   ITGB3 were downregulated in T2DM cells. However, despite differences in
   the individual gene signatures between the two datasets and the
   underlying approaches (microarray in their study vs. RNA-seq here),
   pathway analysis highlights broad consistencies between our data and
   that reported by Zhu et al.

Significant correlations between mRNA and protein expression levels

   We conducted a comparative analysis (Figs. [130]5 and [131]6) to
   understand the relationship between mRNA and protein expression levels
   by comparing gene expression data obtained from RNA-seq with protein
   levels determined through mass spectrometry (MS). Proteins were
   isolated from the extracellular matrix that was synthesized by SMCs and
   deposited in the culture wells (ECM), as well as from the cytosol (C)
   and cytoskeleton (CS) of cells. We observed a moderate yet
   statistically significant correlation between mRNA abundance as
   estimated by RNA-seq, and protein abundance as determined from MS. This
   correlation was quantified using Spearman’s correlation coefficient
   (Rho), which ranged from 0.24 to 0.6 across various samples. These
   findings suggest that alterations at the transcriptional level are
   broadly reflected at the protein level, influencing biological
   pathways.

Fig. 5.

   [132]Fig. 5
   [133]Open in a new tab

   Correlation between ECM proteins and mRNA levels across various glucose
   conditions and T2DM patient samples for 448 genes. (A) 5 mmol/L:
   Spearman correlation coefficient of 0.438 (p = 1.67 × 10^− 21). (B) 10
   mmol/L: Spearman correlation coefficient of 0.453 (p = 4.11 × 10^− 23).
   (C) 20 mmol/L: Spearman correlation coefficient of 0.439
   (p = 1.53 × 10^− 21). (D) T2DM: Spearman correlation coefficient of
   0.533 (p = 7.55 × 10^− 33). Cell pellets were pooled from n = 6 wells/
   condition for RNA-seq and proteomic analysis

Fig. 6.

   [134]Fig. 6
   [135]Open in a new tab

   The correlation between mRNA expression levels and cytosol (C) /
   cytoskeletal (CS) protein levels for 2323 genes across different
   culture conditions. (A) 5 mmol/L glucose: Spearman correlation
   coefficient of 0.245 (p = 2.67 × 10^− 33) for cytosol protein. (B) 5
   mmol/L condition: Spearman correlation coefficient of 0.343
   (p = 3.71 × 10^− 65) for cytoskeleton protein. (C) 10 mmol/L: Spearman
   correlation coefficient of 0.363 (p = 2.61 × 10^− 72) for both cytosol
   (C) and cytoskeleton (CS) protein. (D) 20 mmol/L: Spearman correlation
   coefficient of 0.371 (p = 7.58 × 10^− 77) for both cytosol (C) and
   cytoskeleton (CS) protein. (E) T2DM: Spearman correlation coefficient
   of 0.461 (p = 1.19 × 10^− 122) for cytosol protein. (F) T2DM patient
   samples: Spearman correlation coefficient of 0.606
   (p = 7.57 × 10^− 234) for cytoskeleton protein. Cell pellets were
   pooled from n = 6 wells/ condition for RNA-seq and proteomic analysis

   The correlation between mRNA and protein abundance has been widely
   debated, as it varies significantly depending on the biological
   context. Studies have demonstrated that the correlation between mRNA
   and protein under specific conditions can range from 0.09 to 0.57,
   based on Spearman’s rank correlations [[136]71]. In our data, the
   Spearman’s rank correlation between mRNA and protein ranges from 0.2 to
   0.6, which is consistent with this typical range. While the
   relationship between mRNA and protein is context-dependent and complex,
   it does not necessarily mean that mRNA findings cannot inform protein
   levels. Rather, mRNA and proteins represent distinct regulatory layers,
   and protein abundance can be inferred from mRNA levels – not through
   direct correlation but using machine learning models that account for
   these intricate relationships [[137]72].

Identifying metabolites co-expressed genes and enriched pathways

   Gene expressions and metabolites constitute two distinct layers of
   features that can characterize dynamic responses to changes in glucose
   levels. We investigated which genes are co-expressed with metabolites.
   For each metabolite, we identified gene sets that exhibited similar
   dynamic patterns, using Spearman’s rank correlation coefficient (Rho)
   with a threshold of |Rho| > 0.9. Subsequently, we conducted a pathway
   enrichment analysis to determine which pathways are enriched in these
   co-expressed gene modules, which show similar patterns to the
   metabolites.

   Notably, the L13a-mediated translational silencing pathway and the
   neutrophil degranulation pathway were repeatedly found to be enriched,
   linked to genes co-expressed with 9 and 7 different compounds
   respectively, as depicted in Figs. [138]7 and [139]8. The L13a pathway
   plays a crucial role in cellular stress responses and could potentially
   reduce inflammation triggered by elevated glucose levels by regulating
   the translation of pro-inflammatory cytokines, thus acting as a defense
   mechanism against stress induced by hyperglycemia. Meanwhile, the
   neutrophil degranulation pathway, crucial for immune defense through
   the release of enzymes and reactive substances, might aggravate tissue
   damage under hyperglycemic conditions by promoting neutrophil
   activation and degranulation. These findings suggest that metabolites
   induced by hyperglycemia can indirectly indicate immune response
   signals, as inferred through pathway enrichment analysis using
   co-expressed genes with metabolites. A comprehensive list of enriched
   pathways associated with each metabolite’s co-expressed genes is
   presented in Table [140]S2.

Fig. 7.

   [141]Fig. 7
   [142]Open in a new tab

   Nine enriched pathways from co-expression analysis associated with
   compounds involving L13a-mediated translational silencing of
   ceruloplasmin expression: aspartate, picolinate,
   adenosine-monophosphate, hypoxanthine, allothreonine, phenol, proline,
   taurocheno-desoxycholic acid, and phosphorylcholine. Cell pellets were
   pooled from n = 6 wells/ condition for metabolomic analysis

Fig. 8.

   [143]Fig. 8
   [144]Open in a new tab

   Seven enriched pathways from co-expression analysis associated with
   compounds involving Neutrophil degranulation: Malate, N-acetyltyrosine,
   glutathione reduced, Myristate, Histidine, undecenoic acid, and
   N-acetylserine. Cell pellets were pooled from n = 6 wells/ condition
   for metabolomic analysis

   Among the compounds involving L13a-mediated translational silencing of
   ceruloplasmin expression (Fig. [145]7), proline and picolinate are
   interesting. For instance, proline elevation is associated with lactic
   acidosis, and plasma proline levels strongly correlate with hemoglobin
   A1c and insulin-related variables (e.g., C-peptide, insulin, HOMA-IR)
   [[146]73], although dietary proline intake was not significantly
   associated with T2DM in an adult cohort study [[147]74]. Picolinic
   acid, on the other hand, is an efficient chelator, and chromium
   picolinate has been promoted as a dietary supplement for T2DM patients
   owing to its role in blood glucose regulation, carbohydrate and lipid
   metabolism, and body composition [[148]75].

   In a similar vein, prior studies have shown that high glucose and
   inflammatory conditions coax primary aortic SMCs to express higher
   levels of G6PDH and NADPH oxidase, elevated pentose phosphate pathway
   activity, and upper glycolysis pathway [[149]76, [150]77]. Other
   pathways that are enhanced in SMCs under hyperglycemic conditions
   include protein kinase C signaling, RAGE (receptor for advanced
   glycation end products)/ERK/NF-κB signaling pathway, and ERK/Akt
   signaling pathway, which contribute to increased formation and
   accumulation of AGEs, progression of atherosclerosis and vascular
   calcification, induction of iNOS activity, autophagy, and VSMC
   proliferation, among others [[151]50].

Limitations of this study

   While our study extends human SMC exposure to hyperglycemia in vitro
   from acute (typically < 72 h) to chronic (3-week culture) culture,
   diabetes develops over a long period in vivo, sometimes over decades,
   which would be hard to replicate in vitro. T2DM is a complicated
   process involving interactions between multiple cell types, and
   hyperglycemia is perhaps just one of the contributors to this
   condition, which warrants further in vitro investigations on other
   contributors. For these findings to be relevant to human T2DM, selected
   studies in animal models are required to elucidate the mechanisms
   involved and identify possible therapeutic applications. Furthermore,
   our in vitro results should be validated in other healthy and T2DM
   patient derived SMCs in vitro, and in vivo in future studies.

Conclusions

   This study investigates the effects of hyperglycemic condition on human
   aortic SMCs cultured under varying glucose concentrations (5 mmol/L, 10
   mmol/L, 20 mmol/L), and compared to SMCs isolated from patients with
   type 2 diabetes (T2DM). We noted that chronic hyperglycemia, in a
   glucose concentration dependent manner, induced significant changes
   within human SMCs in vitro in their (i) phenotype and biophysical
   characteristics, (ii) biomechanical properties (Young’s modulus,
   membrane tension, tether and adhesion forces), (iii) cytokines/
   chemokines/ growth factors/ and MMPs-TIMPs release, and (iv)
   differential expressions in gene-sets and pathways representing
   inflammation, matrix turnover, and metabolic pathways. The
   biomechanical results (F[T], F[ad], T[M] and R[T]) presented here for
   human T2DM cells and those of human SMCs under hyperglycemic conditions
   haven’t been reported earlier in literature. Our integrated analysis of
   omics datasets revealed specific biomarkers and enriched pathways,
   which we report for the first time on T2DM cells and on human SMCs
   receiving higher glucose levels, furthering our knowledge on chronic
   hyperglycemia-induced changes in arterial remodeling and their complex
   relationships.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [152]Supplementary Material 1^ (28.7KB, xlsx)
   [153]Supplementary Material 2^ (443.9KB, xlsx)

Acknowledgements