Temporal proteomic changes induced by nicotine in human cells: A quantitative proteomics approach

Abstract

Nicotine is a prominent active compound in tobacco and many smoking cessation products. Some of the biological effects of nicotine are well documented in in vitro and in vivo systems; however, nominal data are available concerning the time-dependent changes on protein and phosphorylation events in response to nicotine. Here, we profiled the proteomes of SH-SY5Y and A549 cell lines subjected to acute (15min, 1h and 4h) or chronic (24h, 48h) nicotine exposures. We used sample multiplexing (TMTpro16) and quantified more than 9,000 proteins and over 7,000 phosphorylation events per cell line. Among our findings, we determined a decrease in mitochondrial protein abundance for SH-SY5Y, while we detected alterations in several immune pathways, such as the complement system, for A549 following nicotine treatment. We also explored the proposed association between smoking and SARS-CoV2. Here, we found several host proteins known to interact with viral proteins that were affected by nicotine in a time dependent manner. This dataset can be mined further to investigate the potential role of nicotine in different biological contexts.

Graphical Abstract

1. Introduction

Nicotine is the main addictive compound in cigarette smoke and a common ingredient in smoking cessation products such as e-cigarettes or transdermal patches. Nicotine has also been shown to have a wide range of biological effects, for example: promotion of cell proliferation and invasion of lung and breast cancer cell lines [1], cardiac toxicity [2], induction of β cell senescence [3], dysregulation of glucose metabolism [4], and the development of asthma [5]. However, not all the biological consequences of nicotine administration are negative. Interestingly, nicotine has also been associated with immune-related effects, including the downregulation of inflammation in a murine model of preeclampsia [6] and the reduction of inflammatory cytokine expression in human and murine chondrocytes [7]. Thus, deciphering the proteomics changes in cells upon nicotine administration can help shed light onto the proteins and signaling events associated with both the positive and negative effects of nicotine.

Multiplexing strategies in mass spectrometry-based proteomics have considerably increased the depth, efficiency, and reproducibility of protein measurements in proteome-wide studies. Currently, we can simultaneously measure the peptide abundance profiles (and by inference, those of proteins) in up to 16 samples using tandem mass tags (TMTpro) [8]. This methodology in combination with state-of-the-art mass spectrometry instrumentation (e.g., FAIMS-equipped Orbitrap Fusion Lumos) enables reliable measurements for the relative abundance of several thousand proteins and phosphorylation events in a single experiment.

Here, we explore the temporal proteomic and phosphoproteomic alterations in response to nicotine treatment in two widely-used human cell lines: SH-SY5Y (neuroblastoma model) and A549 (lung cancer model). We subject the cells to acute (15min, 1h and 4h) or chronic (24h, 48h) nicotine exposure. Following treatment, the cells are harvested, and the samples are prepared following the SL-TMT protocol [9], such that a TMTpro16-plex is performed for each cell line. Our strategy quantified over 9,000 proteins and more than 7,000 phosphorylation events per cell line. Nicotine reduces the abundance of several mitochondrial proteins, mostly related to metabolic processes in SH-SY5Y cells. In contrast, the changes in protein abundance are associated with immune function, such as the complement system in A549 cells. We also use our dataset to explore the protein abundance profiles of known interactors of SARS-CoV2, the etiological agent of COVID19, given confounding observations on smoking and COVID19 prevalence [10–12]. We highlight host proteins that are affected by nicotine in a time dependent manner and have been shown previously to interact with viral proteins.

2. Material and Methods

Materials.

Tandem mass tag (TMTpro) isobaric reagents were from ThermoFisher Scientific (Waltham, MA). Nicotine was purchased from Sigma (St. Louis, MO). Water and organic solvents were from J.T. Baker (Center Valley, PA). Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) was from Life Technologies (Waltham, MA), penicillin-streptomycin antibiotics were from Gibco (Waltham, MA). Trypsin was purchased from Pierce Biotechnology (Rockford, IL) and LysC from Wako Chemicals (Richmond, VA). Unless otherwise noted, all other chemicals were from Pierce Biotechnology (Rockford, IL).

Nicotine treatment.

SH-SY5Y and A549 cells were purchased from ATCC (Manassas, VA). Nicotine treatment was performed, as described previously [13]. Briefly, cells were maintained in DMEM with high glucose/pyruvate supplemented with 10% FBS and 50µL/mL penicillin and 50 µL/mL streptomycin. The culture dishes were kept in a 5% CO₂ incubator at 37°C. Low passage cell stocks were used for all the experiments. When the plates achieved ≈40% confluency, the growth media was aspirated, and the cells were washed with phosphate-buffered saline (PBS). The media was then changed and supplemented with 1 mM nicotine. Cell culture dishes were harvested at different time points: acute nicotine exposure (15min, 1h and 4h) or chronic (24h and 48h) nicotine exposure. The cell pellets were stored at −80°C until use.

Mass spectrometry sample preparation.

Samples were prepared as described previously [8, 9]. Briefly, cell pellets were syringe-lysed in 8M urea complemented with protease and phosphatase inhibitors. Samples were reduced using 5mM TCEP for 30 min, alkylated with 10 mM iodoacetamide for 30 min and the excess of iodoacetamide was quenched using 10 mM DTT for 15 min. Protein was quantified using the BCA protein assay. Approximately 30 micrograms of protein were chloroform-methanol precipitated and reconstituted in 100 µL of 200 mM EPPS (pH 8.5). Protein was digested using Lys-C overnight at room temperature followed by trypsin for 6h at 37°C, both at a 100:1 protein-to-protease ratio. After digestion, the samples were labeled using the TMTpro16 reagents for 60 mins, the reactions were quenched using hydroxylamine (final concentration of 0.3% v/v). The samples were combined equally and subsequently desalted.

We enriched phosphopeptides from the pooled TMT-labeled mixtures (“mini-phos”) using the Pierce High-Select Fe-NTA Phosphopeptide Enrichment kit [9] following manufacturer’s instructions. After column equilibration, sample binding, and washing, the phosphopeptides were eluted into a new microcentrifuge tube containing 100 µL of 10% formic acid and dried in a vacuum centrifuge. The unbound fraction was retained and fractionated using basic pH reversed-phase (BPRP) HPLC. Ninety-six fractions were collected and then consolidated into 24 which were analyzed by LC-MS3.

Mass spectrometry and data analysis.

All data were collected on an Orbitrap Fusion Lumos mass spectrometer coupled to a Proxeon NanoLC-1200 UHPLC. The peptides were separated using a 100 μm capillary column packed with ≈35 cm of Accucore 150 resin (2.6 μm, 150 Å; ThermoFisher Scientific). The mobile phase was 5% acetonitrile, 0.125% formic acid (A) and 95% acetonitrile, 0.125% formic acid (B). For BPRP fractions, the data were collected using a DDA-SPS-MS3 method with online real-time database searching (RTS) [18]. Each fraction was eluted using a 90 min method over a gradient from 6% to 30% B. Peptides were ionized with a spray voltage of 2,600 kV. The instrument method included Orbitrap MS1 scans (resolution of 120,000; mass range 400−1400 m/z; automatic gain control (AGC) target 2x10⁵, max injection time of 50 ms and ion trap MS2 scans (CID collision energy of 35%; AGC target 1x10⁴; rapid scan mode; max injection time of 120 ms). RTS was enabled and quantitative SPS-MS3 scans (resolution of 50,000; AGC target 2.5x10⁵; max injection time of 250 ms) were processed through Orbiter with a real-time false discovery rate filter implementing a modified linear discriminant analysis.

Phosphopeptides were processed with FAIMS/hrMS2 using our optimized workflow for multiplexed phosphorylation analysis [14, 15]. Briefly, the Thermo FAIMS Pro device was operated with default parameters (inner and outer electrode were set at 100°C, yielding a FWHM between 10 V to 15 V and dispersion voltage (DV) was set at −5000 V). No additional gas was used for desolvation. The DV circuitry was auto-tuned, which independently tunes each of the sine waves and phase shifts one of the waveforms by π/2 to assemble a bisinusoidal waveform with a high amplitude of −5000 V at a 3MHz frequency. Each “mini-phos” was analyzed twice by the mass spectrometer, once with a method incorporating two CVs (CV= −45 and −70V) and again with three CVs (CV= −40V,−60V and −80V) using a 2.5h method having a gradient of 6% to 30% B.

Data analysis.

Raw files were first converted to mzXML. Database searching included all human entries from UniProt (downloaded March 2020). The database was concatenated with one composed of all protein sequences in the reversed order. Sequences of common contaminant proteins were also included. Searches were performed using a 50ppm precursor ion tolerance and 0.9 Da (low-resolution MS2) or 0.03 Da (high-resolution MS2) product ion tolerance. TMTpro on lysine residues and peptide N termini (+304.2071 Da for TMTpro) and carbamidomethylation of cysteine residues (+57.0215 Da) were set as static modifications (except when testing for labeling efficiency, when the TMTpro modifications are set to variable), while oxidation of methionine residues (+15.9949 Da) was set as a variable modification. For phosphopeptide analysis, +79.9663 Da was set as a variable modification on serine, threonine, and tyrosine residues.

PSMs (peptide-spectrum matches) were adjusted to a 1% false discovery rate (FDR) [16]. PSM filtering was performed using linear discriminant analysis (LDA) as described previously [17], while considering the following parameters: XCorr, ΔCn, missed cleavages, peptide length, charge state, and precursor mass accuracy. Protein-level FDR was subsequently estimated. Phosphorylation site localization was determined using the AScore algorithm [18]. A threshold of 13 corresponded to 95% confidence that a given phosphorylation site was localized.

For reporter ion quantification, a 0.003 Da window around the theoretical m/z of each reporter ion was scanned, and the most intense m/z was used. Peptides were filtered to include only those with a summed signal-to-noise ratio ≥100 across all channels. An isolation purity of at least 70% in the MS1 isolation window was used for samples analyzed with FAIMS/hrMS2. For each protein, the filtered signal-to-noise values were summed to generate protein quantification values. To control for different total protein loading within an experiment, the summed protein quantities of each channel were adjusted to be equal in the experiment. For each protein in a TMTpro experiment, the signal-to-noise was scaled to sum to 100 to facilitate comparisons across experiments. Further data analysis was performed using a combination of Microsoft Excel, Perseus [19], and GraphPad Prism™ v. 9.0. Gene ontology enrichment was performed using the PantherGO server [20], phosphorylation motifs were analyzed using pLogo [21].

Data access.

We have included protein lists with TMT relative abundance measurements in supplementary tables: Supplemental Table 1 for the SH-SY5Y cell line and Supplemental Table 2 for the A549 cell line. In addition, we provided peptide lists with TMT relative abundance measurements in supplementary tables: Supplemental Table 3 for the SH-SY5Y cell line and Supplemental Table 4 for the A549 cell line. Likewise, the phosphopeptide data are in Supplemental Table 5 for the SH-SY5Y cell line and Supplemental Table 6 for the A549 cell line. RAW files will be made available upon request. In addition, the data have been deposited ProteomeXchange with identifier PXD023108. Username: reviewer_pxd023108@ebi.ac.uk, Password: wcpZP0Wc

3. Results and Discussion

3.1. A temporal proteomics map of nicotine-induced perturbations in two human cell lines.

The aim of our study was to explore the temporal (phospho-)proteomics changes in two commonly used cell lines (SH-SY5Y and A549) in response to nicotine (Figure 1A). We examined the changes occurring after subjecting the cells to acute (15 min, 1h and 4h) or chronic (24h, 48h) nicotine exposure. We selected the SH-SY5Y cell line because it was a well-accepted model to study the biological and neurological effects of nicotine in mammals [22–35]. Also, A549 was a lung adenocarcinoma cell line that had been well-documented to be responsive to nicotine [26–27], and which expressed α7 and α9 nicotinic receptors [28, 29]. All sample preparation for mass spectrometry analysis was performed using our optimized SL-TMT protocol [9] in combination with TMTpro16-plex reagents [8]. In addition, we used Real-Time Search (RTS) to profile the global proteomic alterations, while FAIMS/hrMS2 was used to explore the phosphoproteomics changes. This combination of state-of-the-art instrumentation and optimized methods enabled us to quantify over 9,000 proteins per cell line and more than 7,000 phosphorylation events.

Figure 1. Temporal effects of nicotine on protein expression in SH-SY5Y and A549 cells.

A) Experimental design: SH-SY5Y and A549 cells were treated with nicotine for acute (15 min, 1h, 4h) or chronic (24h, 48h) exposure. Samples were prepared following the SL-TMT protocol using TMTpro16 reagents and one TMT16-plex was performed per cell line. B) Temporal expression of known nicotine-responsive proteins. C) Early phosphorylation events in response to nicotine. Selected abundance profiles of known nicotine-responsive and iron metabolism-related proteins in D) SH-SY5Y and E) A549 cells. APP, Amyloid-Precursor Protein; NCOA4, Nuclear receptor coactivator 4; FTL, ferritin light chain; FTH1, ferritin heavy chain; TFRC, transferrin receptor.

We first assessed the alterations in protein abundance after 48h of nicotine exposure for both cell lines. Our data showed that 572 proteins have significantly altered abundance levels (change of ±50% and corrected p-value<0.05) in SH-SY5Y cells. A similar number of changes were observed in A549 cells (n=472); however, only 23 proteins were significantly altered in both cell lines. This modest overlap suggested that the cellular context - with regard to the cell-specific basal proteome - strongly influenced the proteomic alterations in response to nicotine exposure. This observation corroborated previous data reported by our group related to a mouse tissue study in which mice were subjected to orally administered nicotine [25].

We next explored the protein abundance profiles of specific proteins that were shown previously to be responsive to nicotine treatment [13, 25]. Those proteins behaved similarly in both cell lines; however, the effects were greater and occurred earlier in A549 cells (Figure 1B). As an example, we examined the expression of Amyloid-Precursor Protein (APP) which had been demonstrated to increase in abundance in response to nicotine [25]. Our data showed that the expression of APP increased in SH-SY5Y cells after 24h of treatment (Figure 1D). In contrast, the expression of APP in A549 cells increased as early as 4h (Figure 1E). In addition, nicotine had been known to increase the expression of Nuclear receptor coactivator 4 (NCOA4) [25]. NCOA4 is an iron-regulated selective cargo receptor that mediates the degradation of ferritin (the main cellular iron storage protein) [30]. NCOA4-dependent ferritin degradation occurs when cellular iron requirements increase, for instance, during erythropoiesis [30]. In our dataset, NCOA4 increased significantly in both cell lines (Figure 1D and 1E), but interestingly, both ferritin light (FTL) and heavy (FTH1) chains also increased (Figure 1D and 1E). Moreover, another key player in iron-uptake, the transferrin receptor (TFRC) also showed a slight increase in the abundance over the course of nicotine exposure in both cell lines (Figure 1D and 1E). We expected these proteins (FTL and FTH1) to decrease if ferritin degradation was being promoted by NCOA4 expression [30]. As such, this result raised the possibility that the increased expression of NCOA4 in response to nicotine was not associated with selective ferritin degradation. Nonetheless, the link between nicotine and iron merits further study, particularly in the context that nicotine had been shown previously to directly chelate iron, potentially implicating it with antioxidant properties [31, 32].

Once we determined that our dataset recapitulated known nicotine-induced changes at the protein level, we explored further the phosphoproteomic changes in both cell lines (Figure 1C). We quantified over 8,300 phosphorylation events for SH-SY5Y cells and more than 8,700 for A549 cells. Early signaling events, however, appeared cell-line specific. Of the 350 sites that were significantly (p value<0.01) altered after 1h of nicotine administration, only 30 were significantly changed in both cell lines. As in the protein level data, these results suggested again that the effects of nicotine were strongly dependent on the cellular context.

3.2. SH-SY5Y and A549 (phospho)-proteomic response to nicotine.

Once we confirmed the expression of known nicotine-responsive proteins, we explored the global proteomic response to nicotine exposure in SH-SY5Y and A549 cell lines. We found that SH-SY5Y cells were less sensitive to nicotine, compared with A549 cells. While most of the proteomic changes in SH-SY5Y cells occurred at later time points (24h-48h), a clear response to nicotine happened as early as 4h in A549 cells (Figure 2A). This result was evidenced further by principal components analysis (Figure 2B), where only the 48h group was clearly separated from the rest of the time points in SH-SY5Y cells. For comparison, A549 cells showed clearly defined groups as soon as 4h after treatment. Interestingly, after 48h of nicotine treatment, we found that several proteins in SH-SY5Y cells were strongly down-regulated, while proteins seemed to be mostly upregulated in A549 cells (Figure 2C and 2D). Moreover, gene ontology enrichment showed that about 30% of the significantly changing proteins in SH-SY5Y cells were associated with the mitochondria. Consequently, several metabolism-related pathways were affected by nicotine after 48h of treatment. For example, glutamate metabolism and transport-related proteins were downregulated, as were tricarboxylic cycle and fatty acid oxidation (Figure 2C). Coincidently, as the mitochondria has been considered a target of nicotine, some of the changes associated with nicotine exposure were related to the respiratory chain, oxidative stress, calcium homeostasis, biogenesis, and mitophagy [33].

Figure 2. Biological consequences of nicotine administration in two human cell lines.

A) Violin plots illustrating the fold change distribution in both cell lines. B) PCA showing grouping of replicates and time points for each cell line. C) Bar plots (right) displaying the temporal protein abundance of several affected pathways in SH-SY5Y and D) A549 cells.

As we noted previously, most of the proteins from SH-SY5Y that were significantly altered in abundance by nicotine treatment were not shared with A549 cells. As such, we proposed that nicotine exposure will have a different outcome in tissues and cells depending in their efficiency to transport and internalize nicotine. For example, in a mouse model of oral nicotine administration, the lung and spleen were shown to be the most susceptible tissues to nicotine administration, yet few significantly altered proteins were shared across many of the tissues investigated [34]. As for the A549 cells, we found that the foremost affected pathways were related to immune function (Figure 2D). These pathways included the interferon, TGF-β, TNF-α and complement pathways. The association of nicotine and TGF-β has been extensively documented. Moreover, the beneficial effects of nicotine treatment for allergic asthma has been linked to an increase in TGF-β/IL-4 ratio [35]. In addition, the epithelial-to-mesenchymal transition in response to nicotine has been associated with TGF- β [36, 37] and expression of α7 nicotinic receptors [29]. As such, the relationship between nicotine and immune response in the background of A549 cells merits further investigation.

After determining the differential proteomic responses in both cell lines, we explored the phosphoproteomic changes in response to nicotine. Similar to the protein level data, phosphorylation site alterations seemed to be generally cell line specific, as most of the changing sites were not shared between both cell lines. Nonetheless, we highlighted four kinases that were phosphorylated in response to nicotine in both cell lines, specifically, NEK9, RAF1, ABL2, and MAPK7 (Supplementary Figure 1A). Of these kinases, only MAPK7 was phosphorylated in a known activation site (Y221). Those phosphorylation changes occurred as early as 15 min after nicotine treatment. This finding suggested that these kinases could, at least partially, coordinate some signaling events in response to nicotine. We performed a motif enrichment analysis on a subset of phosphorylation sites that changed significantly after 48h of treatment, (p-value<0.01). We found that other than the SP motif, no clearly enriched motif was evident in either the SH-SY5Y (Supplementary Figure 1B) or A549 (Supplementary Figure 1C) cell line. We also uncovered a subset of up- or down-regulated phosphorylation events for each cell line (Supplementary Figure 2A and 2B). Again, those significantly changing sites have poor overlap between cell lines, which once again highlights the cell line-specific nicotine response.

3.3. Biological relevance of the nicotine-induced proteome changes in the context of COVID19.

Here, we have generated a dataset that can be mined further by the community to provide insights into the biological consequences of nicotine administration in human cells. As an example, we will interrogate our dataset in the context of COVID19. As we mentioned previously, nicotine had been associated with multiple positive and negative biological impacts. The effects of nicotine exposure had been presented in pathologies related to the lung, including cancer, chronic obstructive pulmonary disorder, allergic asthma, or infection susceptibility, among others [1, 5, 38].

Presently, we are in the midst of the COVID19 global pandemic. This viral infection is caused by the SARS-CoV2 virus and its manifestation can range from asymptomatic to fatal clinical presentations [39]. Currently, several vaccines have been developed and others are under different stages of clinical trials. For example, the mRNA-based vaccines of Pfizer/BioNTech and Moderna were the first to have emergency use authorization in the United States. Other vaccines from Johnson&Johnson/Janssen, Novavax, Sanofi/GlaxoSmith-Kline, Merck, among others are at the time of this writing at various stage of clinical trials. However, a need persists for scientific literature that sheds light onto the risk factors associated with the development of a severe clinical presentation [40]. Recently, smoking and COVID19 susceptibility have produced some contradictory observations. For example, in a meta-analysis [10–11, 41–42], current smokers were shown to be statistically less likely to be hospitalized due to COVID19, raising the possibility that some of the compounds in tobacco smoke could be associated with a certain degree of protection. In contrast, a UK study released shortly thereafter suggested that being a current smoker was associated with a higher probability of COVID19 infection [43]. Thus, a link between nicotine and COVID19 had been proposed, but the effect, either positive or negative, remained undefined.

We mined our dataset to explore proteins related to viral infection. Recently, the SARS-CoV2 viral-host proteins interactome was reported [44]. In that study, each viral protein was overexpressed in a mammalian cell system followed by immuno-affinity enrichment and mass spectrometry analysis. This study showed that viral proteins physically interacted with more than 300 host proteins. Using these data, we interrogated our own dataset for known interacting host proteins (“interactors”) in the SH-SY5Y and A549 cell lines. First, we observed greater alterations of interactors in A549 cells compared with SH-SY5Y cells upon nicotine treatment (Figure 3A). Interestingly, we found that while interactors showed either an increase or decrease in abundance in A549 cells, a considerable fraction of the interactors were downregulated in SH-SY5Y cells with increased time of nicotine exposure (Figure 3B). We highlighted in this volcano plot several interactors with large changes that were shown previously to interact with the M subunit, ORF8 and ORF9 of the SARS-CoV2 virus. In total, approximately 80 interactors were significantly affected by nicotine in either cell line (86 for SH-SY5Y cells and 87 for A549 cells), while only about 20% of those proteins were shared in both cell lines (Figure 3C). Of these proteins, some were either up- or down-regulated in SH-SY5Y cells, while all were upregulated in A549 cells (Figure 3D). Again, A549 cells seems to be more susceptible to the effects of nicotine than SH-SY5Y cells, as most of the changes in interactors were noticeable as early as 4h after nicotine treatment. As expected from our previous data showing differences in protein alterations between cell lines, some interactors were only affected by nicotine treatment in A549 cells, but not in SH-SY5Y cells. These data highlighted the complexity of elucidating the role of nicotine in COVID19 infection, as those effects varied between the cell lines investigated.

Figure 3. Temporal expression of host proteins known to interact with SARS-CoV proteins.

(A) Violin plot showing the fold change distribution in both cell lines. B) Volcano plot illustrating the significantly changing interactors in both cell lines. C) Venn diagram showing the exclusive and shared interactors that significantly changed in both cell lines. D) Heatmap showing the temporal protein abundance of significantly changing interactors in both cell lines. E) Interactors exclusively affected in A549 cells and not in SH-SY5Y cells. For the heat maps, the human gene name is followed by the name of the SARS-CoV2 protein.

As an example, we highlighted factor H of the complement system (Figure 2D). The complement system is a critical component of the innate immune system with pathogen clearance being one of its many functions. However, hyper-activation of the complement system can cause deleterious effects for the host. As such, tightly regulated mechanisms have evolved to protect it, with one being factor H. This molecule protects self surfaces from complement activation/deposition in healthy cells/tissues [45]. We observed that factor H was upregulated in A549 cells, potentially preventing complement deposition on healthy lung tissues, and helping to preserve tissue homeostasis during COVID19 infection. Recent data suggested that the SARS-CoV2 spike protein promoted complement activation [46], implying that complement modulation could be a potential therapy for COVID19 infection [47]. Our data showed an increase in factor H with increased nicotine exposure (Figure 2D), which suggests some preliminary evidence for the potential relationship among COVID19, nicotine, and the complement system/factor H. However, further studies are needed to define the consequences of nicotine exposure during COVID19 infection, for this and other proteins highlighted here.

Conclusion.

In summary, we generated a dataset that can be mined by the community to study the biological consequences of nicotine exposure in human cells. This dataset encompasses valuable information about the early and late proteomic changes in two human cell lines. In addition, we amassed phosphorylation data that can be used to explore signaling events associated with nicotine treatment. Together, this dataset can serve to develop hypothesis-based studies on a wide-array of topics related to the effects of nicotine exposure on human cells.

Supplementary Material

1. Supplemental Table 1: Proteins quantified in the SH-SY5Y cell line dataset.

Columns include: Uniprot protein identification number (proteinID), gene symbol (Gene Symbol), protein description/name (Description), number of peptides quantified per protein (peptides), and the normalized summed signal-to-noise for each of the 16 channels (126 to 134).

2. Supplemental Table 2: Proteins quantified in the A549 cell line dataset.

Columns are identical to those in Supplemental Table 1.

3. Supplemental Table 3: Peptides quantified in the SH-SY5Y cell line dataset.

Columns include: Uniprot protein identification number (proteinID), gene symbol (Gene Symbol), description/name (Description), redundancy, peptide sequence (peptide sequence), number of quantified peptides (num_quant), and the summed signal to noise for each of the 16 channels (126 to 134).

4. Supplemental Table 4: Peptides quantified in the A549 cell line dataset.

Columns are identical to those in Supplemental Table 3.

5. Supplemental Table 5: Phosphorylation sites quantified in the SH-SY5Y cell line dataset.

Columns include: Uniprot protein identification number (proteinID), gene symbol (Gene Symbol), description/name (Description), phosphorylation site position, phosphorylation site motif, redundancy, peptide sequence (peptide sequence), number of spectral counts, number of quantified peptides (num_quant), and the summed signal to noise for each of the 16 channels (126 to 134).

6. Supplemental Table 6: Phosphorylation sites quantified in the A549 cell line dataset.

Columns are identical to those in Supplemental Table 3.

7

Supplemental Figure 1: Phosphoproteomic changes in response to nicotine. A) Phosphorylation sites on kinases that are altered in both cell lines. Motif analysis of a subset of sites that change significantly 1h after nicotine administration in the B) SH-SY5Y or C) A549 cell lines.

Supplemental Figure 2: Example phosphorylation sites that change in response to nicotine. Bar plots showing up- and down-regulated phosphorylation events in A-B) SH-SY5Y cells and in C-D) A549 cells.

Significance.

Smoking is a major public health issue that is associated with several serious chronic, yet preventable diseases, including stroke, heart disease, type 2 diabetes, cancer, and susceptibility to infection. Tobacco smoke is a complex mixture of thousands of different compounds, among which nicotine is the main addictive compound. The biological effects of nicotine have been reported in several models, however very little data are available concerning the temporal proteomic and phosphoproteomic changes in response to nicotine. Here, we provide a dataset exploring the potential role of nicotine on different biological processes over time, including implications in the study of SARS-CoV2.

Highlights.

• SH-SY5Y and A549 cells were treated with 1 mM nicotine for acute or chronic exposures

• The proteome changes in SH-SY5Y cells were mostly related to mitochondria

• The proteome changes in A549 cells were associated with immune-related pathways

• A549 cells are more sensitive to nicotine, compare to SH-SY5Y