Concurrent Chronic-Plus-Binge Alcohol Consumption and Nicotine Vaping Alter the Cardiac Ventricular Proteome in a Preclinical Mouse Model

Abstract

Nicotine vaping has surged in recent years, particularly among young adults, and is strongly linked with concurrent alcohol use. Separately, chronic excessive alcohol use drives hypertension and cardiomyopathy, while nicotine vaping is linked to a modest rise in cardiovascular disease incidence and mortality. However, little is known about how concurrent use interacts to affect protein expression in the cardiovascular system. The aim of this study was to determine differential cardiac protein expression in mice exposed to concurrent chronic-plus-binge alcohol and nicotine vaping use. Male C57BL6/J mice received a 20-day 5% ethanol diet with 5 g/kg ethanol binges on days 10 and 20, alongside isocaloric controls. During this period, they were also exposed nightly to either 5% nicotine salt vapor, vegetable glycerin/propylene glycol vehicle vapor, or room air. The left ventricular free wall was collected and analyzed using discovery-based proteomics and subsequent Ingenuity Pathway Analysis. A total of 3144 proteins were identified across all groups. Compared to air-exposed, control-fed mice, 201 proteins were significantly altered by ethanol, 101 proteins by nicotine vaping, and 159 proteins by combined exposure. Both ethanol and nicotine vaping influenced pathways involved in lipid homeostasis, extracellular matrix remodeling, and mitochondrial bioenergetics; however, these alterations did not uniformly manifest in the dual-use group. This pattern highlights the nonadditive and potentially interaction-dependent nature of alcohol and nicotine vaping effects on cardiovascular protein expression patterns that may contribute to a distinct functional phenotype.

1. Introduction

Electronic cigarette use has increased markedly in recent years, with the highest prevalence observed among young adults aged 21–34 [1]. Just as seen with traditional combustible cigarette use, individuals who vape nicotine exhibit higher rates of alcohol consumption and alcohol use disorder compared with non-users [2,3]. This growing overlap in nicotine vaping and alcohol use stresses the importance of understanding their combined biological effects, particularly within the cardiovascular system.

Current knowledge of nicotine vaping–related cardiovascular effects is drawn largely from short-term e-cigarette exposure studies. Nicotine acutely activates sympathetic pathways, resulting in increased heart rate and blood pressure [4], and impairs endothelium-mediated peripheral vasodilation [5,6]. In addition to nicotine-derived effects, preclinical studies show that exposure to the commonly used e-liquid humectants propylene glycol (PG) and vegetable glycerin (VG) impair endothelial-dependent vasodilation [7]. Beyond these hemodynamic and vascular effects, recent multi-omics work has demonstrated protein changes associated with e-cigarette aerosol exposure in mice [8]. In this study, Dai et al. [8] reported increases in inflammatory signaling, enrichment of hematopoietic cell lineage pathways, and alterations in estrogen signaling and platelet activation pathways, suggesting systemic immune and vascular involvement. Although these findings highlight acute and subacute cardiovascular consequences of e-cigarette exposure, the long-term cardiovascular impact of sustained nicotine vaping remains unclear, particularly in the context of polysubstance use.

Chronic alcohol use independently produces well-established cardiovascular pathology. Long-term consumption is associated with new-onset hypertension [9] and excessive intake can lead to nonischemic dilated cardiomyopathy, marked by left ventricular (LV) dilation, normal or reduced wall thickness and mass, and ultimately heart failure with reduced ejection fraction [10]. These structural changes are caused, in part, by disrupted calcium homeostasis that impairs myocardial contractility [10] and by mitochondrial dysfunction driven by oxidative stress, which activates cell death pathways [11,12]. Chronic alcohol use also stimulates the sympathetic nervous system, promoting β1-adrenergic receptor activation, increased heart rate and stroke volume, and, with sustained activation, excitation–contraction coupling abnormalities and apoptosis [13]. Furthermore, heavy alcohol consumption is associated with increased risk of atrial fibrillation [14,15], likely mediated through left atrial enlargement, fibrosis, or vagally driven arrhythmic episodes [16,17]. Additionally, heavy alcohol intake impairs cardiac protein synthesis through inhibition of mTOR signaling and upregulation of autophagy, contributing to contractile dysfunction in animal models of alcohol-associated cardiomyopathy [18,19]. Prior proteomic analyses in mouse models of chronic alcohol exposure have identified alterations in proteins regulating fatty acid metabolism, oxidative stress responses, and contractile machinery [20]. Together, these findings illustrate the broad and multifactorial impact of alcohol on cardiac structure, function, and electrophysiology.

Despite these well-established independent effects, little is known about how concurrent nicotine vaping and alcohol use interact to influence cardiovascular health. The aim of this study was to identify differentially expressed cardiac proteins, and subsequent pathway analysis, in mice exposed to concurrent chronic-plus-binge alcohol and nicotine vape use.

2. Results

2.1. Serum Cotinine

Due to the shorter half-life of nicotine in mice compared to humans (~7 min in mice compared to 2 h in humans [21,22]), nicotine exposure was assessed by measurement of serum cotinine levels (Figure 1), which showed an average of 323 ng/mL ± 12 ng/mL in the nicotine vape only group and 288 ng/mL ± 12 ng/mL in the dual nicotine vape-ethanol exposure group. The serum cotinine levels in our nicotine vape exposure model were within the range seen in human nicotine vape users [23].

2.2. Assessment of Tissue Composition and Cellular Content

Because proteomic analyses were performed on whole left ventricular tissue lysates, we evaluated whether differences in protein abundance could be explained by variation in tissue composition rather than biological remodeling. Proteins were classified into blood-associated, immune cell–associated, cardiomyocyte-enriched, extracellular/secreted, and intracellular categories using Human Protein Atlas (v25.0) annotations mapped to mouse orthologs (Section 4). For each sample, the summed MaxLFQ intensity of proteins within each category was expressed as a fraction of total protein intensity.

Across all experimental groups, the relative contribution of each protein category was highly comparable, with no evidence of disproportionate enrichment of blood-derived or immune-associated proteins, depletion of cardiomyocyte-enriched proteins, or excess extracellular protein content (Supplementary Figures S1 and S2; Supplementary Table S5). These findings indicate that the observed differential protein expression is unlikely to be driven by differences in cellular or extracellular composition and instead reflects exposure-associated proteomic remodeling.

2.3. Differentially Expressed Proteins

A total of 3144 proteins were identified across all experimental groups. Comparative analysis revealed distinct proteomic alterations associated with individual and combined exposures (Figure 2; Table S1). Volcano plots of differentially regulated proteins for each experimental group compared to the air exposure/control diet (AC) group are shown in Figure 3 and Figure 4, and Table S2. VG/PG aerosol alone resulted in 25 upregulated and 43 downregulated proteins relative to air controls (Figure 3A; VC vs. AC). Nicotine vaping alone produced broader changes, with 62 proteins significantly upregulated and 39 downregulated (Figure 3B; NC vs. AC). Ethanol exposure elicited the most pronounced effect, with 109 proteins upregulated and 92 downregulated (Figure 3C; AE vs. AC). Combined VG/PG and ethanol exposure altered 82 proteins upward and 75 downward (Figure 4A; VE vs. AC), whereas dual nicotine vape–ethanol exposure resulted in 99 upregulated and 60 downregulated proteins (Figure 4B; NE vs. AC).

Further analysis was conducted to understand conserved or differential alterations to specific proteins across dual-exposure groups. Proteins that were upregulated or downregulated in the dual exposure groups were compared to detected, unchanged proteins in the single exposure group, and vice versa. These comparisons revealed that 29 proteins upregulated in the ethanol-only group were unchanged under dual nicotine vape–ethanol exposure, and 55 proteins downregulated by ethanol alone were similarly unaffected in the combined exposure group. Conversely, 31 proteins that were upregulated and 27 proteins that were downregulated in the dual nicotine vape-ethanol group were unchanged in the ethanol-only group. Additionally, 28 proteins that were upregulated and 14 proteins that were downregulated in the nicotine vape only group were not changed in the nicotine vape-ethanol dual use group, and 62 proteins that were upregulated and 14 proteins that were downregulated in the nicotine vape-ethanol group were unchanged in the nicotine vape only group. These results suggest an interaction that alters ethanol and nicotine vape-driven proteomic responses in the dual exposure group.

2.4. Ingenuity Pathway Analysis

Pathway analysis using Ingenuity Pathway Analysis (IPA) demonstrated exposure-specific effects (Figure 5, Table S3). VG/PG alone significantly altered one pathway, nicotine vaping alone altered seven pathways, ethanol exposure altered 21 pathways, VG/PG combined with ethanol altered 15 pathways, and dual nicotine–ethanol exposure altered 10 pathways. These findings indicate that concurrent exposure produces a distinct proteomic and pathway signature, differing from the additive effects of individual substances.

3. Discussion

3.1. Excitation–Contraction Coupling and Contractile Machinery

Cardiac excitation–contraction (E-C) coupling relies on precise regulation of calcium cycling and phosphorylation cascades that coordinate sarcomeric protein function. Disruption of these processes is a hallmark of pathological remodeling and heart failure [25]. In healthy murine myocardium, thick filaments are predominantly composed of MYH6, the fast α-myosin heavy chain isoform, which supports rapid contractile kinetics [26]. Pathological states, including hypertrophy, are characterized by an isoform shift toward MYH7, the slow β-myosin heavy chain, associated with reduced shortening velocity [27]. In our study, ethanol exposure and combined VG/PG-ethanol exposure were associated with increased MYH7 expression, suggesting early molecular signatures of hypertrophic remodeling. Interestingly, this isoform shift was absent in the dual nicotine–ethanol group, and nicotine vaping alone was linked to decreased MYH7 expression compared with VG/PG controls (NC vs. VC), indicating a potential nicotine-mediated suppression of hypertrophic signaling. While prior studies have shown that nicotine exposure leads to cardiac hypertrophy [28,29,30], the nicotine vaping paradigm used in this study produced a proteomic signature that is not indicative of cardiac hypertrophy.

Proteomic analysis revealed additional alterations in key regulators of calcium handling and β-adrenergic signaling. A-kinase anchoring protein 1 (AKAP1), a scaffolding protein that localizes protein kinase A (PKA) to the outer mitochondrial membrane and influences Ca²⁺ cycling and contractile protein phosphorylation [31], was significantly downregulated in both ethanol-only and dual nicotine–ethanol groups. Loss of AKAP1 has been shown to exacerbate maladaptive remodeling and accelerate heart failure progression in stress models, suggesting that its reduction may contribute to impaired contractile reserve under these exposure conditions [32].

Similarly, Ca²⁺/calmodulin-dependent protein kinase II gamma (CAMK2G), which participates in phosphorylation cascades critical for E-C coupling, arrhythmogenesis, and Ca²⁺ homeostasis, was downregulated in the dual exposure group. This finding implies potential disruption of CaMKII-mediated signaling, which is essential for maintaining excitation–contraction integrity and adaptive responses to stress [33].

Conversely, proteins regulating PKA signaling, including PKIA and PKIG, were upregulated in multiple exposure conditions. PKIA was elevated in ethanol-only, nicotine vape-only, and dual exposure groups, while PKIG was upregulated in nicotine vape-only and dual exposure groups. These changes may suggest compensatory modulation of PKA activity, possibly reflecting an attempt to stabilize β-adrenergic signaling in the context of AKAP1 loss and altered calcium handling. However, excessive or dysregulated PKA signaling can itself promote maladaptive phosphorylation of contractile proteins, contributing to arrhythmogenic risk and contractile dysfunction [34].

Collectively, these findings indicate that chronic ethanol and nicotine exposures exert complex and interactive effects on E-C coupling machinery. Ethanol appears to drive the expression of key proteins involved in hypertrophic remodeling while downregulating key proteins involved in PKA anchoring. Nicotine modifies these responses by suppressing MYH7 induction and altering PKA regulatory protein expression. The combined exposure pattern could suggest a non-additive interaction that may blunt hypertrophic signaling but simultaneously disrupt calcium homeostasis and β-adrenergic regulation. These proteomic signatures warrant further mechanistic investigation to determine whether these changes confer adaptive or maladaptive consequences for myocardial performance under chronic polysubstance exposure.

3.2. Energy Metabolism and Lipid Mediator Signaling

To evaluate the impact of ethanol and nicotine vapor on cardiac energy homeostasis and lipid-mediated signaling, we examined both individual proteins and relevant pathways. In ethanol-exposed mice, proteins involved in lipid uptake (LPL), transport (FATP4), and storage (PLIN2) were upregulated, along with proteins mediating fatty acid mobilization (ACOT1, PNPLA8). In nicotine vape–exposed mice, PNPLA8 and GPAM, which regulate fatty acid mobilization via hydrolysis from cell membrane glycerophospholipids (PNPLA8) [35] and triglyceride synthesis (GPAM) [36], were upregulated. These findings align with previous reports demonstrating that e-cigarette exposure promotes cardiac lipid accumulation and steatosis in diet-induced obese murine models [37].

Dual exposure to nicotine and ethanol produced similar increases in LPL, FATP4, PNPLA8, and ACOT1, with additional upregulation of ACOT2 and CES1C—proteins linked to fatty acid mobilization for processes such as energy production through fatty acid oxidation in highly oxidative tissues [38,39]. These changes were accompanied by elevated levels of the oxidative stress–related protein GPX3, suggesting enhanced lipid peroxide generation under dual exposure. Interestingly, PNPLA8 is also stimulated by reactive oxygen species, which can lead to an antioxidant-like feedback mechanism wherein mitochondrial-derived superoxide production is ultimately decreased [40]. However, PNPLA8 has also been shown to increase the formation of the mitochondrial permeability transition pore (mPTP) in a myocyte-specific knockout mouse [41]. Further studies aimed at understanding the balance between cardioprotective functions and cell stress responses of PNPLA8 due to alcohol and nicotine are warranted.

Vehicle-only exposure did not alter lipid regulatory proteins, whereas combined VG/PG and ethanol exposure upregulated LPL, PNPLA8, PLIN2, and CES1C. Overall, nicotine and ethanol both drive upregulation of lipid regulatory proteins, with ethanol exerting the most pronounced effect. PNPLA8 abundance increased across all nicotine vape and ethanol groups, though dual exposure did not amplify this effect compared to single exposures, indicating a lack of additive interaction.

Mitochondrial dysfunction represents a well-characterized consequence of chronic ethanol consumption and nicotine vaping, with both exposures implicated in impairing oxidative phosphorylation and promoting cellular stress responses [42,43,44]. In the present study, the VG/PG vehicle group exhibited a unique proteomic signature, with significant downregulation of the “mitochondrial translation” pathway. This pathway encompasses the molecular machinery responsible for synthesizing proteins encoded by the mitochondrial genome. Specifically, three mitoribosomal proteins—MRPL1, MRPL32, and MRPL53—along with MT-CO3, a translation product encoding a subunit of complex IV, were markedly reduced. Although direct evidence linking VG/PG exposure to impaired mitochondrial translation is lacking, prior work demonstrates that VG/PG constituents diminished cellular metabolic activity in airway epithelial cells, suggesting a plausible mechanistic link [45].

In contrast, dual nicotine–ethanol exposure elicited robust activation of pathways associated with mitochondrial quality control, including “complex III assembly” and “mitochondrial protein degradation”. These changes likely reflect a compensatory response to heightened mitochondrial stress, aimed at restoring respiratory chain integrity and clearing damaged proteins [46]. Notably, nicotine-containing aerosols have been shown to inhibit complex III activity [47], supporting the interpretation that upregulation of assembly-related proteins represents an adaptive attempt to counteract functional deficits. Future work assessing complex activity in this model or respirometry of cardiac mitochondria will help clarify the functional implications of these pathway changes [48]. Collectively, these findings indicate that VG/PG exposure could disrupt mitochondrial translational capacity, whereas combined nicotine–ethanol exposure may trigger a stress-adaptive remodeling of mitochondrial proteostasis, highlighting distinct mechanistic pathways that could lead to mitochondrial vulnerability with vaping and alcohol co-use.

3.3. Extracellular Matrix (ECM) Remodeling

Both ethanol and nicotine have been implicated in myocardial ECM remodeling. Previous work in a canine model demonstrated that chronic administration of either substance increased myocardial stiffness, collagen content, and collagen crosslinking, with combined exposure producing an additive effect on left ventricular (LV) chamber stiffness and collagen deposition [49]. Studies with ethanol alone have demonstrated that chronic ethanol exposure promotes myocardial ECM remodeling, characterized by increased collagen deposition and activation of profibrotic signaling pathways. For example, in murine models of chronic ethanol feeding, a 4% ethanol diet induced significant histological increases in total collagen content, accompanied by elevated expression of fibrillar collagens (types I and III) and upregulation of the pro-fibrogenic cytokine transforming growth factor-β (TGF-β) within as little as two weeks of exposure [50]. Similarly, our laboratory has previously reported that two weeks of ethanol vapor exposure resulted in marked increases in collagen I and III expression and a significant rise in interstitial collagen content, as quantified by picrosirius red staining [51]. In the current study, nicotine vaping alone was associated with activation of ECM-related pathways, including “extracellular matrix organization,” “integrin cell surface interactions,” and “pulmonary fibrosis idiopathic signaling pathway.” These pathways govern ECM synthesis, integrin-mediated cell–matrix interactions, and profibrotic signaling cascades, all of which are central to structural remodeling and fibrosis.

Proteomic analysis revealed unique expression of COL5A2, SPARC, and ADAM10 in the nicotine vaping group. COL5A2 encodes a fibrillar collagen critical for organizing collagen fibrils within the matrix, SPARC (Secreted Protein Acidic and Rich in Cysteine) functions as a matricellular regulator modulating ECM deposition and TGF-β signaling, and ADAM10 (a disintegrin and metalloproteinase 10) facilitates proteolytic cleavage of ECM-associated proteins, contributing to matrix turnover and cell adhesion dynamics [52]. The presence of these proteins suggests active ECM remodeling in response to nicotine vaping. Notably, these proteins were absent in the dual nicotine–ethanol exposure group, indicating that combined exposure may suppress ECM-associated pathways.

Ethanol alone and combined nicotine–ethanol exposure did not significantly activate ECM remodeling pathways in our model. These findings align with previous reports using C57BL/6 mice that demonstrated chronic-plus-binge ethanol feeding does not produce significant cardiac or ECM remodeling [44,53]. This variance emphasizes the complexity of polysubstance interactions and suggests that nicotine may lose its modulatory effects on ECM signaling when co-administered with ethanol. Further mechanistic studies are warranted to determine whether this suppression reflects adaptive remodeling or impaired reparative processes under dual exposure conditions.

3.4. Proteostasis

Ethanol exposure elicited activation of protein synthesis pathways, including “eukaryotic translation initiation, elongation, and termination”, consistent with prior evidence linking alcohol consumption to dysregulated proteostasis [18,19]. In contrast, nicotine exposure alone did not significantly activate these pathways, although in vitro studies have demonstrated nicotine-induced alterations in protein expression that may influence downstream proteostatic mechanisms [54]. The absence of translation pathway activation in the combined nicotine–ethanol group suggests an interaction that attenuates ethanol-driven stimulation of protein synthesis, potentially reflecting competitive or compensatory signaling effects.

Deeper proteomic analysis revealed that ethanol exposure was associated with pronounced downregulation of proteins involved in endoplasmic reticulum (ER) quality control and protein folding, including HSPB6, HSPA1A, HSPA2, UGGT1, and MOGS. Concurrently, components of the ubiquitin–proteasome system such as FBXO22, NEDD4, and PSMD8 were upregulated, indicating enhanced proteolytic activity and activation of degradation pathways in response to proteostatic stress. These findings align with established models of alcohol-induced ER stress and impaired protein homeostasis [20].

Interestingly, dual nicotine–ethanol exposure did not replicate these ethanol-specific alterations. Instead, the combined exposure group exhibited a distinct proteomic signature characterized by increased VPS4B expression and concomitant reductions in VPS4A, CDKN1B, and IDE, suggesting a shift toward ubiquitin-linked trafficking rather than canonical proteasomal degradation. This implies that nicotine may modulate ethanol-induced proteostatic responses by redirecting protein turnover toward endosomal sorting and vesicular trafficking pathways. Such adaptations could mitigate ER stress without engaging full proteasomal machinery, potentially influencing intracellular signaling and cardiac remodeling under chronic dual substance exposure.

Collectively, these findings emphasize the complexity of nicotine–ethanol interactions at the proteomic level. The observed attenuation of ethanol-driven protein synthesis and remodeling of proteostasis pathways in the presence of nicotine highlights the need for mechanistic studies to delineate how these interactions impact cardiac structure, function, and long-term disease risk. Future work should integrate functional assays and targeted validation to clarify whether these proteomic shifts confer adaptive or maladaptive consequences for myocardial health.

3.5. Study Limitations and Future Directions

One limitation of the study is the use of only male C57BL/6 mice. Sex-specific differences in cardiac physiology, remodeling, and substance metabolism are well documented in both preclinical and clinical research, and these differences can significantly influence cardiovascular outcomes under stress or toxic exposures. Our laboratory has previously reported that female C57BL/6J mice exhibit protection against chronic-plus-binge ethanol-induced cardiac injury, including attenuation of hypertrophic signaling and preservation of contractile function [53]. Further investigation is warranted to assess proteomics alterations that may be a result of sex-specific chromosomal or hormonal differences.

Additionally, this study is based solely on discovery-driven proteomic analysis at a single terminal time point, which limits our ability to capture dynamic changes in protein expression during disease progression. While proteomics provides valuable insight into molecular signatures, it does not establish causal mechanisms underlying these alterations. Accordingly, conclusions are restricted to proteomic signatures within the heart and do not establish function or physiological outcomes. Future studies should include longitudinal sampling and functional validation to determine how these proteomic shifts evolve over time and whether they translate into physiological consequences for cardiac structure and function under chronic dual substance exposure.

4. Materials and Methods

4.1. Animals

Eight-week-old male C57BL/6J mice (n = 30; 5 mice/group) were obtained from Jackson Laboratory and pair-housed in JAG cages (Allentown, LLC, Allentown, NJ, USA) under controlled conditions (12 h light/dark cycle, standard temperature and humidity). Air pumps for the vapor machine were placed on foam pads to decrease noise and vibration. Animals were acclimated for one week in the vapor exposure facility prior to initiation of experimental procedures to minimize stress-related variability. All experimental protocols, including housing and substance exposure, were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at Louisiana State University Health Sciences Center (Protocol #9381, 28 August 2025) and were conducted in accordance with NIH guidelines for the care and use of laboratory animals. Humane endpoints were predefined in accordance with the approved institutional animal care and use committee (IACUC) protocol. Animals were monitored for clinical signs including changes in body weight, grooming behavior, posture, activity level, respiratory distress, and signs of pain or distress. Animals exhibiting >15–20% body weight loss, persistent lethargy, labored breathing, or failure to eat or drink were humanely euthanized. Monitoring was performed daily throughout the study.

4.2. Ethanol and Nicotine Vape Exposure

Mice were randomly subjected to a combined chronic-plus-binge ethanol feeding paradigm [44,55] integrated with a controlled nicotine vaping protocol (represented in Figure 6). The vaping regimen consisted of three exposure conditions: room air, 1:1 vegetable glycerin (VG) to propylene glycol (PG), or 5% nicotine salt (prepared with benzoic acid) dissolved in a 1:1 VG/PG solution. Vapor chambers were programmed to deliver a 2 s aerosol puff every 10 min during a 12 h dark cycle (19:00–07:00). Exposure groups are outlined in

Table 1. Group abbreviations with vapor and/or ethanol feeding paradigm.

Group Abbreviation	Vapor Paradigm/Ethanol Paradigm
AC	Air Exposed/Control Diet
AE	Air Exposed/Ethanol Diet
VC	VGPG Exposed/Control Diet
VE	VGPG Exposed/Ethanol Diet
NC	Nicotine Vape Exposed/Control Diet
NE	Nicotine Vape Exposed/Ethanol Diet

. Mice were pair-housed and home cages were kept inside a sealed vapor chamber (La Jolla, CA, USA) to ensure consistent exposure. Group allocation and exposure procedures were performed by investigators aware of group assignments.

Following a 5-day acclimation period to the Lieber-DeCarli liquid diet (Bio-Serv, Flemington, NJ, USA), mice were assigned to either an ad libitum 5% ethanol-containing liquid diet or an isocaloric control diet, concurrent with the vaping paradigm. On experimental days 10 and 20, animals received binge ethanol exposure (5 g/kg ethanol) or an isocaloric maltose dextrin control (9 g/kg) via oral gavage. Approximately 5 days following alcohol binge (average 4.7 days; see Supplemental Table S4 for exact days for each mouse), mice were euthanized, and hearts were excised. The left ventricular free wall was isolated, washed in ice-cold phosphate-buffered saline, flash-frozen in liquid nitrogen, and stored at −80 °C for subsequent proteomic analysis.

4.3. Blood Collection and Cotinine Assay

On day 13, blood was collected from the submandibular/facial vein under 2% isoflurane within 30 min of last vape exposure. A puncture of the submandibular vein was made using a 25-gauge needle. Blood was collected in a collection tube containing EDTA (Braintree Scientific, Inc., Braintree, MA, USA, cat# 16444-BX). Whole blood was centrifuged at 2000× g for 6 min at 4 °C, and serum was collected.

Serum cotinine concentrations, serving as an indirect biomarker of systemic nicotine exposure, were quantified using a commercially available Mouse/Rat Cotinine ELISA kit (Calbiotech, El Cajon, CA, USA, cat# CO096D-100). Samples were diluted 1:10 in PBS to ensure compatibility with the assay’s dynamic range. All procedures were performed in accordance with the manufacturer’s protocol. Data are shown as mean ± standard error of the mean (SEM). Statistical analysis was performed using two-way analysis of variance (ANOVA).

4.4. Quantitative Discovery-Based Proteomics Using Label-Free Quantification and Liquid Chromatography–Mass Spectrometry

Frozen tissue samples (≥20 mg per sample; corresponding to ~2 mg total protein) were transferred on dry ice into 2.0 mL tubes designated for mechanical homogenization. An equal volume (µL per mg of tissue) of ammonium bicarbonate buffer (ABC) was added to each tube (e.g., 20 mg tissue received 20 µL ABC). The tissue–buffer suspension was then transferred into new tubes. Four volumes of 8 M urea in ABC were added to each sample based on the combined volume of tissue and buffer. Five large ceramic beads were added, and samples were homogenized using a Bead Ruptor 12 (Omni International, Inc., Kennesaw, GA, USA). Homogenates were vortexed and incubated for 5 min at room temperature, followed by centrifugation at 14,000× g for 5 min.

A 100 µL aliquot of the clarified supernatant was transferred into new tubes for downstream processing; residual material was stored at −80 °C for future use. Ice-cold acetone (1 mL) was added to each aliquot, samples were vortexed, and protein precipitation proceeded for 1 h at −20 °C. Precipitates were collected by centrifugation at 14,000× g for 10 min. Supernatants were carefully aspirated, avoiding disturbance of the pellet. A second acetone wash (1 mL) was performed with identical vortexing, incubation, and centrifugation steps. Supernatants were removed completely. Pellets were dried using a speed vacuum concentrator (no heat; ~5 min). Typical yield was ~1 mg total protein per sample.

Protein pellets were resuspended in 100 µL of NPC-S Denaturation Buffer (50 mM ammonium bicarbonate, pH 8.5; 10 mM tris(2-carboxyethyl)phosphine; 5% sodium deoxycholate) and sonicated for 15 min at room temperature. Pellets that remained partially insoluble were sonicated for an additional 15 min, resulting in near-complete solubilization. Samples were incubated at 60 °C for 10 min with tube caps covered to minimize evaporation. A total of 20 µL of NPC Alkylation Buffer (100 mM iodoacetamide) was added, and samples were incubated for 60 min at room temperature in the dark.

A total of 880 µL of NPC Dilution Buffer (50 mM ammonium bicarbonate, pH 8.5) was added, followed by vortexing. After brief settling, 100 µL of clarified supernatant (corresponding to ~100 µg protein) was transferred into new tubes. Two microliters of NPC Trypsin Solution (1 µg trypsin/µL NPC Dilution Buffer) were added, and samples were digested overnight at 37 °C.

Following digestion, 5 µL of 20% formic acid was added to each sample and incubated for 30 min at room temperature. Samples were then centrifuged at 15,000× g for 15 min, and the supernatant was transferred to a new Eppendorf tube.

Prior to mass spectrometry (MS) analysis, samples were briefly vortexed to ensure homogeneity. The samples were then centrifuged at 14,000× g for 2 min to pellet any remaining precipitate. From each sample, 22 µL was transferred into a 96-well plate. Subsequently, 20 µL from each well was loaded onto Evotips following the Evosep protocol, alongside seven additional sample sets. During the Evosep preparation steps, additional centrifugation spins were performed to ensure complete removal of sample and buffer from the Evotips: one extra spin during equilibration, two extra spins during sample loading, and one extra spin during the wash step.

Forty-eight fractions (200 µL each) were collected into a 96-well plate and recombined in a checkerboard pattern to produce 12 super-fractions. The 12 super-fractions were dried and prepared for LC-MS/MS. Samples loaded onto Evotip were analyzed using an Evosep One LC (EVOSEP, Odense M, Syddanmark, DK) LC system connected to a CaptiveSpray ionization source of timsTOF fleX MALDI-2 mass spectrometer (Bruker, Billerica, MA, USA). For separation, the 60 SPD Evosep One method (21 min gradient, cycle time of 24 min) using a C18 column (80 × 0.150 mm; 1.5-μm particle size) from PepSep and the mobile phases composed of Solvent A (0.1% formic acid in ddH₂O) and Solvent B (0.1% formic acid in acetonitrile) were used. Data was collected in data-dependent acquisition—parallel accumulation serial fragmentation mode (DDA-PASEF) under the following conditions: positive mode with a capillary voltage of 1500 V; the source temperature was set at 180 °C; dry gas flow was maintained 3 L/min; acquisition range was 100–1700 m/z. TIMS setting was as follows: 1/K₀ start: 0.6 Vs/cm²; 1/K₀ end: 1.60 Vs/cm²; ramp time: 100 ms; accumulation time: 100 ms; duty cycle: 100%; ramp rate: 9.43 Hz.

4.5. Proteomic Data Analysis

Proteomic profiling was performed to identify global protein expression changes induced by chronic-plus-binge ethanol and nicotine vape treatments in five experimental conditions: VGPG aerosol/Control diet (VC; n = 5), Nicotine vape/Control diet (NC; n = 5), Air exposed/Ethanol diet (AE; n = 5), VGPG aerosol/Ethanol diet (VE; n = 5), Nicotine vape/Ethanol diet (NE; n = 5). Outcome assessments and data analysis were conducted with investigators blinded to group allocation until completion of the analyses. Each biological replicate represented an independent mouse heart left ventricle tissue processed separately for protein extraction and LC–MS/MS analysis. Raw LC–MS/MS data were processed using FragPipe (version 23.0) with the Mus musculus UniProt reference proteome as the search database [56]. Protein quantification was performed using the label-free quantification (LFQ) workflow integrated within FragPipe. LFQ intensity values were subsequently curated and imputed using custom in-house R scripts.

For each condition, proteins were identified according to their reproducibility across biological replicates. A protein was considered detected if it was present in all replicates or missing in only one replicate within a group. Missing LFQ intensity values among detected proteins were imputed using a Perseus-style “missing not at random” (MNAR) method, which replaces missing values with random draws from a Gaussian distribution left-shifted by 1.8 standard deviations and with a width of 0.3 to simulate low-abundance signals near the detection limit. Proteins detected in one or fewer biological replicates were classified as undetected for that condition, and their intensity values were set to zero to denote the absence of reliable quantification. Proteins that did not meet either criterion, detected or undetected, were excluded from downstream analyses to minimize detection uncertainty. Differential expression analysis was conducted using the limma R package (version 3.58.1), applying a moderated Student’s t-test with empirical Bayes correction [57]. Proteins with a p-value < 0.05 and an absolute log₂ fold change (|log₂FC|) > 0.332 (corresponding to a 1.25-fold linear change) were considered significantly regulated. Significantly altered proteins were then subjected to Ingenuity Pathway Analysis (IPA, QIAGEN Inc., Redwood City, CA, USA) to identify enriched canonical pathways. Canonical pathways analysis using IPA was used to identify potential pathways influenced by the observed proteomic changes. These analyses are predictive and intended to guide future mechanistic studies.

To assess whether differential protein expression could be influenced by variation in tissue or cellular composition across experimental groups, a protein category–based composition analysis was performed. Proteins quantified by LFQ were annotated using Human Protein Atlas (HPA, version 25.0) annotations and mapped to mouse orthologs, and classified as blood-associated, immune-associated, cardiomyocyte-enriched, extracellular/secreted, or intracellular. For each sample, MaxLFQ intensities were summed within each category and expressed as a fraction of total MaxLFQ intensity to compare relative protein composition across experimental groups independent of absolute abundance.

5. Conclusions

Our findings demonstrate that chronic nicotine vaping and ethanol exposure exert distinct and interactive effects on protein expression in several key cardiac signaling pathways: proteostasis, energy metabolism, and structural remodeling. Rather than producing additive toxicity, dual exposure differentially affected several ethanol-driven responses while introducing unique alterations in proteins involved in mitochondrial quality control and ubiquitin-linked trafficking. These non-linear interactions highlight the complexity of polysubstance use and emphasize the need for mechanistic studies to determine whether these proteomic signatures confer adaptive or maladaptive consequences.