Comparative Ligandomics Analysis of Human Lung Epithelial Cells Exposed to PM2.5

Abstract

Objective.

To investigate whether PM2.5 exposure alters lung epithelial cell response to extrinsic regulation by globally profiling cell surface ligands and quantifying their binding activity.

Methods.

Human A549 lung epithelial cells (LECs) were treated with or without PM2.5. Ligandomics profiling was applied to these cells for global identification of LEC-binding ligands with simultaneous binding activity quantification. Quantitative comparison of the entire ligandome profiles systematically identified ligands with increased or decreased binding to PM2.5-treated LECs.

Results.

We found 143 ligands with increased binding to PM2.5-treated LECs and 404 ligands with decreased binding. Many other ligands showed unchanged binding activity. For example, apolipoprotein E (ApoE), Notch2 and growth arrest-specific 6 (Gas6) represent ligands with increased, decreased or unchanged binding activity, respectively. Both ApoE and Gas6 are phagocytosis ligands, suggesting that phagocytic receptors on LECs with stimulation of PM2.5 are differentially upregulated by PM2.5.

Conclusion.

These results suggest that newly-developed ligandomics is a valuable approach to globally profile the response of LECs to PM2.5 in terms of regulating their cell surface receptor expression, as quantified by ligand binding activity. This quantitative ligandome profiling will provide in-depth understanding of LEC molecular response on the cell surface to particulate matter air pollution.

INTRODUCTION

Particulate matter with diameters equal to or less than 2.5 μm (PM2.5) in polluted air tends to penetrate deep into alveoli of the lung and deposit onto the surface of lung epithelial cells (LECs). Alveolar epithelium and pulmonary marcrophages are able to ingest inhaled PM2.5 (1). Exposure to PM2.5 increases the risk of respiratory diseases, including nasal obstruction, cough, asthma, respiratory infection, chronic obstructive pulmonary disease (COPD) and lung cancer (2–4).

LECs are the first cells to contact inhaled PM2.5, which may induce cellular responses, including oxidative stress and alternation of protein expression profile, through complicated molecular mechanisms. Previous studies have shown that cytotoxicity produced by PM2.5 is frequently attributed to the oxidative stress due to intracellular reactive oxygen species (ROS), thereby inducing DNA and mitochondrial damages (5–7). PM2.5 may also alter gene expression profile (8), such as upregulation of tumor growth factor-β (TGF-β) and ligands for epidermal growth factor receptor (EGFR) (9, 10).

Inhaled PM2.5 deposited on the surface of lung epithelium can be phagocytosed by LECs through pattern recognition receptors (PRRs) or other phagocytic receptors to exert cytotoxicity or inflammation (11). Soluble PM2.5 components as well as the particles themselves may interact with epithelial cell surface receptors to trigger pro-inflammatory responses. Over the time, LECs may adapt to the air pollution by up- or down-regulating cell surface receptors. Systematic profiling of ligand-receptor interactions on LEC surface will provide in-depth understanding of how PM2.5 induces lung toxicity and LECs adaptively respond to the air pollution.

Ligand-receptor interactions are traditionally characterized for one ligand at a time. An innovative technology of quantitative ligandomics was recently developed to globally identify cell-wide ligands and simultaneously quantify their binding activities (12). This approach successfully identified several novel ligands with different functions (12–14). However, this new omics approach has never been applied to environmental biology. In this study, we applied quantitative ligandomics to globally map cellular ligands for human LECs with or without exposure to PM2.5 and simultaneously quantify their binding activities. Quantitative comparison of their entire ligandome profiles systematically identified ligands with increased or decreased binding to PM2.5-exposed LECs. The results of this study suggest that comparative ligandomics is a valuable approach to environmental medicine research.

MATERIALS AND METHODS

Materials.

Open reading frame phage display (OPD) cDNA library prepared from mouse embryos at Day 18 (E18) was previously described (15). Human A549 lung alveolar epithelial cell line (carcinoma) was purchased from ATCC (Cat. # CCL-185). F12/K culture medium and fetal bovine serum were from Thermo Fisher. Human rhinovirus (HRV) 3C protease and BLT5616 bacteria were from Sigma.

PM2.5 sample collection.

PM2.5 samples were collected as reported previously by our group (16). Briefly, PM2.5 particles were collected onto quartz microfiber filters (20.3 × 25.4 cm, PALL, USA) using a PM2.5 high-volume air sampler (KC-1000, Laoshan Mountain Electronic Instrument Factory Company, Qingdao, China) for 14 days in October 2014 in Zhengzhou, the capital city of Henan Province located in central China. The sampling apparatus was located about 14 meters above the ground level. In the surrounding areas, a variety of small- and medium-size factories, including machineries, chemical manufacturing companies, boiler facilities and power plants, were present, and the samples represented typical and important ambient air quality situations in such a mixed pollutant area. Airborne PM2.5 was sampled daily (24-h samples) with an airflow of 1.13 m³/min on preconditioned (48 h at 20–25℃ and 40–45% of relative humidity) and preweighted TE-6070DX-2.5-HVS filters (Tisch Environmental) using a microbalance (Mettler Toledo XS205, Switzerland). After particle collection, filters were reconditioned for another 48 h. Particles were extracted from the filter by vortexing in 2 ml sterile distilled water for 10 min followed by 30-s sonication. The PM2.5 suspension was then stored in −80°C freezer, and then put in vacuum freeze-drying instrument for 15 h to remove water. Before and after extraction, each preconditioned filter was weighted twice, and the difference of the values was less than or equal to 0.03 mg. Extracted PM2.5 mass was calculated based on the difference in filter weight before and after extraction. The quartz filters have structural stability, and it was minimally removed during the PM2.5 extraction. Although the use of quartz filter may sometimes result in the loss of particle mass, the choices of filters are unable to influence the responses of lung epithelial cells (17–19). Dried PM2.5 was suspended in sterile nanopure water to make PM2.5 stock solution for the following cell exposure studies.

Cell culture.

A549 cells, a human lung alveolar epithelial cell line, were cultured in F12/K medium supplemented with 10% fetal bovine serum (FBS), 1 X GlutaMax and 1 X pen/strep at 37°C with 5% CO₂. We chose A549 cell line for this study because it was one of the most studied cells for adverse effects of PM2.5, including particle uptake/phagocytosis by the cells and signaling pathways (20, 21).

Cell-based binding selection.

PM2.5 was diluted into phosphate-buffered saline (PBS). A549 cells were treated with PM2.5 (50 μg/ml) or PBS for 24 h. The OPD cDNA library was amplified in BLT5616 bacteria, precipitated, resuspended and incubated with A549 cells (~90% confluence) for 1 h at 4°C in 6-well plates (~10¹² plaque forming units(pfu)/well) (15, 22, 23). After washing, cell-bound phages were eluted with HRV 3C protease (10 units in 500 μl) at 4°C (overnight for the 1st round; 4 h for the 2nd and 3rd rounds). After centrifugation (2,000 × g for 5 min), eluted phages in the supernatant were quantified by plaque assay, amplified in BLT5615 bacteria and used as input for the next round of selection (22, 23). Three rounds of cell-based binding selection were performed.

Next generation DNA sequencing analysis.

The cDNA inserts of all enriched clones were amplified by PCR using upstream and downstream primers annealing to the phage backbone sequences flanking the cDNA library inserts, as described (23). After resolving by agarose gel, PCR products between 400 – 1,500 bp were purified and subjected to next generation DNA sequencing (NGS) analysis by DNA Sequencing Core Facility at Baylor College of Medicine (12).

Comparative ligandomics data analysis.

All sequence reads were aligned against the NCBI CCDS database. All identified ligands are listed in an Excel spreadsheet with their copy numbers. Ligandome data are normalized against the total number of their sequence reads identified by NGS (24). Quantitative comparison of the entire ligand profiles for PM2.5-treated and untreated cells were performed using Chi-square (χ²) test to systematically identify PM2.5-regulated epithelial ligands (24). Only those with statistical significant difference (P<0.001) were defined as PM2.5-related epithelial ligands. Additional criteria to filter out false positives include PM2.5/control binding activity ratio ≥ 10 or ≤ 0.1, and copy number in treated or untreated LECs ≥ 10 (24). Binding activity ratio was calculated as (PM2.5 counts +1)/(control counts +1). A binding activity plot (PM2.5-treated vs. untreated) was generated to assess the global changes of ligand binding activity to PM2.5-treated cells. Pearson correlation coefficient was calculated (24).

RESULTS

Comparative ligandomics profiling

Ligandomics was recently developed as the only technology to globally map cell-wide ligands with simultaneous binding or functional activity quantification in the absence of receptor information. Here we applied this new approach to globally profile LEC ligands by performing three rounds of cell-based binding selection in PM2.5-treated or control cells (Figure 1A). After three rounds of selection, enriched phages showed ~30-fold increase in cell-binding activity (Figure 2).

Figure 1.

Schematics of comparative ligandomics to globally identify PM2.5-selective cellular ligands. (A) Open reading frame phage display (OPD) selection. OPD cDNA library was incubated with human lung epithelial cells (LECs) pretreated with or without PM2.5 for multi-rounds of binding selection to enrich clones displaying cellular ligands. (B) Global identification of all enriched ligands by NGS. The cDNA inserts of enriched clones were amplified by PCR and identified by NGS. The copy numbers of the cDNA inserts are the equivalent of their clone numbers or the binding activity of the displayed ligands. (C) Comparative ligandomics data analysis. Quantitative comparison of the entire ligandome profiles for PM2.5-treated vs. untreated LECs systematically identified PM2.5-selective ligands.

Figure 2.

Enrichment of LEC-binding phages by cell-based phage binding selection. The library was incubated with A549 cells with or without PM2.5 treatment in 6-well plates. After washing, bound phages were eluted by 3C protease cleavage. Eluted phages were quantified by plaque assay.

Instead of performing labor-intensive manual screening of enriched clones individually, we analyzed the cDNA inserts of all enriched clones by NGS and identified a total of 975,632 and 1,132,564 valid sequence reads for PM2.5-treated and control cells, respectively. Identified sequences were aligned to 4,140 (PM2.5-treated cells) and 4,091 (control cells) proteins in the NCBI CCDS database (Figures 3A and 3B).

Figure 3.

Ligandome profile. (A) Binding activity profile of the entire ligandome for control A549 human LECs. (B) Binding activity profile of the entire ligandome for PM2.5-treated A549 cells. (C) Binding activity ratio of the entire ligandomes for PM2.5-treated vs. control cells.

The copy numbers of cDNA inserts quantified by NGS represent the relative binding activity of the identified ligands (see Discussion), most of which had background binding activity with less than 10 copies of cDNAs detected. Only 1,034 and 686 ligands have ≥ 10 copies bound to control and PM2.5-treated LECs, respectively (Figures 3A and 3B). Quantitative comparison of the entire ligandome profiles for PM2.5-treated vs. control A549 LECs by Chi-square test systematically identified 143 ligands with increased binding (PM2.5-high) to PM2.5-exposed A549 LECs and 404 ligands with decreased binding (PM2.5-low) (Figure 3C, Table 1).

Table 1.

PM2.5-related LEC ligands identified by comparative ligandomics

		Binding activity		Activity
CCDS_ID	Protein	Control	PM2.5	ratio
CCDS40232	Gas6	4,904	3,364	1.5X ↓
CCDS16777	Notch2*	1,020	55	18.5X ↓
CCDS20912	ApoE*	79	1,824	23.1X ↑
CCDS28578	Tulp1	20,012	27,450	1.3X ↑
CCDS28713	Abcf1	3,658	3,957	1.1X ↑
Total identified sequences		1,132,564	975,632
Total identified ligandsa		1,034	686
PM-2.5-related ligands*		404 ↓	143 ↑

^*P<0.001, control vs. PM2.5, χ² test.

^a,bound ligand count ≥ 10.

The binding activity of the entire ligandome profile for PM2.5-treated vs. untreated A549 LECs is plotted in Figure 4A. Ligands of the green dots within the bottom-right circle have decreased binding activity to PM2.5-treated cells, whereas ligands of the red dots within the up-left circle have increased binding activity. Ligands in the blue dots around the diagonal line have minimal change in binding activity. Proteins in the bottom-left circles have background low binding activity to A549 cells and are considered non-specific binding. Pearson correlation coefficient for the entire ligandomes of PM2.5-treated and control LECs in the binding activity plot was calculated as r = 0.9942 (Figure 4A).

Figure 4.

Global mapping of PM2.5-specific cellular ligands by comparative ligandomics. (A) Binding activity plot for PM2.5-treated vs. untreated A549 cells. Ligands with increased or decreased binding to PM2.5-treated cells are classified as PM2.5-high or PM2.5-low, respectively. PM2.5-unchanged ligands and background binding showed similar binding activities in both conditions. PM2.5-high ApoE, PM2.5-low Notch2 and unchanged Gas6 are indicated. Pearson correlation coefficient r=0.9942. (B) PM2.5 treatment altered ligand binding activity. ApoE preferentially bound to PM2.5-treated LECs (1,824:79 for PM2.5-treated vs. control cells). Notch2 binding to LECs was reduced in PM2.5-treated cells (55:1,020). Gas6 had minimal binding activity changes to PM2.5-treated cells (3,364:4,904).

PM2.5-related cellular ligands

The up- and down-regulation of ligandome binding activity profile to PM2.5-treated cells implies that the particles can alter expression of cell surface receptors to modulate various cellular responses. One of the important biological responses of the lung epithelium is phagocytosis or engulfment of PM2.5 as a part of innate defense mechanisms for debris clearance. Phagocytosis is mediated through cell surface phagocytic receptors, whose altered expression can be detected by comparative ligandomics.

Apolipoprotein E (ApoE), a well-known phagocytosis ligand, was uncovered with up-regulated binding activity. Comparative ligandomics analysis revealed that ApoE binding to PM2.5-treated cells increased by 23.1-fold (P<0.001) (Figure 4B, Table 1). However, not all phagocytosis ligands showed increased binding activity. For example, growth arrest-specific 6 (Gas6), a well-characterized phagocytosis ligand, had a 1.5-fold decrease in binding to PM2.5-exposed cells (Figure 4B, Table 1). Other phagocytosis ligands, such as Tubby-like protein 1 (Tulp1) and ATP-binding cassette subfamily F member 1 (Abcf1), showed negligible binding activity increase to PM2.5-treated cells (1.3- and 1.1-fold increase, respectively) (Figure 4B, Table 1). On the other hand, neurogenic locus notch homolog protein 2 (Notch2) with a potential to regulate phagocytosis had an 18.5-fold increase in binding to the particle-exposed cells (Figure 4B, Table 1). These data suggest that PM2.5 differentially promoted the induction of some phagocytic receptors but not others on human LECs.

DISCUSSION

Plasma membrane receptors are the most valuable drug targets, as highlighted by one third of all FDA-approved drugs targeting G protein-coupled receptors (GPCRs) (25). Other receptors, such as receptor tyrosine kinases and integrins, are also important therapeutic targets. We may assume that cell surface ligands with the similar capacity to regulate diverse cellular functions would be equally valuable as drug targets, such as insulin to treat diabetes and inhibitors of vascular endothelial growth factor (VEGF) to ameliorate pathological angiogenesis (26). Surprisingly, however, only a limited number of drugs have been successfully developed by targeting cellular ligands (25). Part of the reason is technical difficulties to identify unknown cellular ligands, whereas all plasma membrane receptors can be readily identified based on their transmembrane domains and cell surface expression (27). Current technologies of functional proteomics are mainly developed to map intracellular protein-protein interactions but not cell surface ligand-receptor communications (28).

To tackle these challenges, we recently developed ligandomics technology to globally map cell-wide ligands and comparative ligandomics to systematically identify disease-selective ligands as drug targets (29). An example for comparative ligandomics to facilitate development of novel drug therapies is the discovery of secretogranin III (Scg3) for the treatment of diabetic retinopathy (24). We applied comparative ligandomics to diabetic and control retina in live mice for systematic profiling of disease-selective endothelial ligands (24). Scg3 was discovered as the first diabetes-selective or restricted angiogenic and vascular leakage factor. Among thousands of identified endothelial ligands, Scg3 had the highest binding activity ratio (1,731:0) to diabetic vs. control vessels and lowest background binding to control vasculature (24). We developed Scg3-neutralizing antibody and demonstrated its high efficacy to alleviate retinal vascular leakage in mice with diabetic retinopathy (24). We further proposed that therapies targeting disease-selective ligands have the safety advantage of minimal side effects on healthy cells or vessels with wide therapeutic windows (29). Indeed, compared to conventional anti-angiogenic therapy, disease-selective anti-Scg3 mAb showed an improved safety profile (30). These results suggest that comparative ligandomics is a powerful tool to discover disease-restricted extracellular ligands as high-quality drug targets for novel ligand-guided targeted therapies (24, 29).

This study applied comparative ligandomics to A549 cells and identified ApoE with increased binding activity to PM2.5-treated cells, Notch2 with decreased binding, and Gas6, Tulp1 and Abcf1 with minimal binding activity changes (Figure 4, Table 1). ApoE and its low-density lipoprotein receptor (LDLR) have been extensively studied. ApoE is a well-known phagocytosis ligand (31, 32), and LDLR and LDLR-related proteins are cell surface phagocytic receptors (33–35). The increased binding activity of ApoE to PM2.5-treated A549 cells indicates the up-regulation of its phagocytic receptors. Gas6 is also a well-characterized phagocytosis ligand interacting with MerTK phagocytic receptor (36), and Tulp1 and Abcf1 were recently reported as phagocytosis ligands (13, 37). These data imply that PM2.5 exposure may selectively upregulate some but not all phagocytic receptors on the lung epithelium. Notch and its receptor Jag1 regulates M1 polarization and phagocytosis of macrophage (38). Notch2 also regulates cell growth and other cellular functions, such as marginal zone B-cell growth and immunological function, vascular smooth muscle cell differentiation and proliferation, and vascular endothelial cell survival (39). Furthermore, Notch2 functions as a tumor suppressor (40). Notch2 binds to DDL1(Delta-link protein 1), GSK3B, Jag1 and Jag2 (41). The precise role of Notch2 to regulate phagocytosis of lung epithelium is yet to be established.

The challenge is how to validate the therapeutic potential for a large number of disease-selective ligands identified by comparative ligandomics. We recently proposed function-first and therapy-first approaches to verify functional activity, disease selectivity, pathological role and therapeutic potentials of identified ligands (42). In these approaches, disease-selective ligands or cognate inhibitors will be independently characterized for their activity to exacerbate or alleviate disease pathogenesis in animal models, followed by drug development (24, 42). The barrier to verify the disease-selective ligands identified in this study is how to develop appropriate in vitro or in vivo models and independently assess their potential for PM2.5 intervention.

The binding activity of identified ligands is a relative value, which reflects not only ligand-receptor binding affinity but also expression level of the cognate receptors on the surface of A549 cells. Some ligands may bind to multiple receptors. Given that different ligands may have distinct functional roles, variable binding affinities, multiple receptors, and alterable receptor expression, it is inappropriate to compare different ligands for their binding activity. On the other hand, the same ligand has the same panel of receptors and binding affinities (24, 29). Thus, quantitative comparison of the same ligands for PM2.5-treated vs. untreated cells can reveal the air pollution-induced up- or down-regulation of their binding activity to A549 cell surface, which reflects their altered receptor expression.

The Pearson correlation coefficient of the binding activity plot for the entire ligandome unravels the global dysregulation of cell surface receptors. The correlation coefficient for PM2.5-exposed vs. control LECs was calculated as r = 0.9942 (Figure 4A). Our previous study of comparative ligandomics in streptozotocin-treated 4-month-diabetic and control mice calculated a Pearson correlation coefficient (r = 0.498) for all identified retinal endothelial ligands (24). These two coefficients suggest that global ligandome alternations on A549 cells pretreated with PM2.5 only for 24 h was not as severe as those on retinal endothelium of streptozotocin-induced 4-month-diabetic mice. It is possible that long-term exposure of the lung to PM2.5 induces more diverse global alternations of the entire ligandome profile.

Ligandomics is an OPD-based technology and may not be able to display some proteins appropriately due to protein misfolding or posttranslational modifications (24). We estimated that ligandomics may miss a small percentage of cellular ligands as false negatives due to these technical limitations (24). These technical problems could be solved by mammalian display systems, such as retrovirus display (43). Nonetheless, OPD is presently the most robust display system for ligandomics analysis. To our knowledge, our ligandomics is the only technology to globally map cell-wide ligands, and comparative ligandomics is the only approach to systematically profile disease-restricted cellular ligands as high-quality drug targets (29). Application of comparative ligandomics to environment medicine will globally map pollution-induced alternation of ligandome profiles, thereby unraveling molecular toxicological mechanisms and potential targets for novel therapies.

CONCLUSIONS

Quantitative ligandomics globally mapped cell-wide ligands for PM2.5-treated and control human LECs with simultaneous quantification of their binding activities. Comparative ligandomics analysis for these two cells systematically identified ligands with increased or decreased binding activity to PM2.5-exposed cells. Global mapping of PM2.5-restricted cellular ligands will delineate molecular mechanisms of cellular response to the pollution and identify potential targets to develop novel therapies. These findings suggest that comparative ligandomics is a valuable approach for environmental medicine research.