Surface PEGylation suppresses pulmonary effects of CuO in allergen-induced lung inflammation

RESEARCH Open Access Surface PEGylation suppresses pulmonary effects of CuO in allergen-induced lung inflammation Marit Ilves 1 , Pia Anneli Sofia Kinaret 2,3 , Joseph Ndika 1 , Piia Karisola 1 , Veer Marwah 2,3 , Vittorio Fortino 2,4 , Yuri Fedutik 5 , Manuel Correia 6 , Nicky Ehrlich 7 , Katrin Loeschner 6 , Alexandros Besinis 8,9 , Joanne Vassallo 8 , Richard D. Handy 8 , Henrik Wolff 10,11 , Kai Savolainen 10 , Dario Greco 2,3 and Harri Alenius 1,12* Abstract Background: Copper oxide (CuO) nanomaterials are used in a wide range of industrial and commercial applications. These materials can be hazardous, especially if they are inhaled. As a result, the pulmonary effects of CuO nanomaterials have been studied in healthy subjects but limited knowledge exists today about their effects on lungs with allergic airway inflammation (AAI). The objective of this study was to investigate how pristine CuO modulates allergic lung inflammation and whether surface modifications can influence its reactivity. CuO and its carboxylated (CuO COOH), methylaminated (CuO NH 3 ) and PEGylated (CuO PEG) derivatives were administered here on four consecutive days via oropharyngeal aspiration in a mouse model of AAI. Standard genome-wide gene expression profiling as well as conventional histopathological and immunological methods were used to investigate the modulatory effects of the nanomaterials on both healthy and compromised immune system. Results: Our data demonstrates that although CuO materials did not considerably influence hallmarks of allergic airway inflammation, the materials exacerbated the existing lung inflammation by eliciting dramatic pulmonary neutrophilia. Transcriptomic analysis showed that CuO, CuO COOH and CuO NH 3 commonly enriched neutrophil-related biological processes, especially in healthy mice. In sharp contrast, CuO PEG had a significantly lower potential in triggering changes in lungs of healthy and allergic mice revealing that surface PEGylation suppresses the effects triggered by the pristine material. Conclusions: CuO as well as its functionalized forms worsen allergic airway inflammation by causing neutrophilia in the lungs, however, our results also show that surface PEGylation can be a promising approach for inhibiting the effects of pristine CuO. Our study provides information for health and safety assessment of modified CuO materials, and it can be useful in the development of nanomedical applications. Keywords: CuO, Engineered nanomaterial, Health effects, Inflammation, Asthma, Allergic airway inflammation, Risk assessment © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. * Correspondence: [email protected];[email protected] 1 Human Microbiome Research Program, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland 12 Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden Full list of author information is available at the end of the article Ilves et al. Particle and Fibre Toxicology (2019) 16:28 https://doi.org/10.1186/s12989-019-0309-1 Background Nanotechnology is a rapidly expanding field of material manipulation. It focuses on improving physicochemical characteristics of different materials and thereby offers numerous possibilities for product development in several industrial sectors. Metal oxides are one of the most abundantly produced types of engineered nanomaterials (ENM) with production volumes of up to thousands of tons every year [1]. CuO nanomaterials are appealing due to their electrical, optical, magnetic and biocidal features, thus they are produced for variety of industrial and commercial applications, such as electronic chips, solar cells, lithium batteries, paints, processed wood and plastics [2–4]. Because of their antimicrobial properties, CuO nanomaterials are used or could be utilized in food packaging, wound dressings, skin products and textiles [2–4]. An important innovation in health care is CuOcontaining bed sheets that reduce the occurrence of hospital-acquired infections and thus decrease health care costs in medical facilities [4]. In addition, CuO ENM can be potentially used in nanomedicine as anticancer and bioimaging agents [5,6]. Asthma is a pulmonary disease that affects over 330 million people worldwide and its prevalence is rising rapidly [7]. It is characterized by chronic airway inflammation, reoccurring episodes of airway obstruction, airway hyperresponsiveness (AHR), mucus overproduction and airway remodeling [8,9]. Asthma has multiple forms and it can be caused by several environmental factors in combination with over 100 susceptibility genes [8]. The extrinsic phenotype (also known as allergic or atopic asthma) starts early on in life and is triggered by an allergen, such as pollen or pet dander. Another pathway of asthma (intrinsic, non-allergic) is independent of Th2 cells and adaptive immunity but relies on the presence of neutrophils and IL-17, which is a neutrophilic chemotactic factor. This “neutrophilic asthma”is associated with nonallergic stimuli, such as cigarette smoke, cold air or pollutants. Pulmonary neutrophilic inflammation is also a major characteristic of patients with severe, steroid non-responding asthma [8–10]. The most common phenotype of asthma found in children and half of adults is atopic in nature [11]. The classical asthmatic response can be divided into early and late phase reactions. During the immediate, early phase response, antigen contact causes local mast cell activation and release of their mediators that ultimately culminates in an acute asthmatic attack. The late-phase response arrives several hours later and can last for prolonged periods of time [12]. Pathomechanistically, the allergens cause a complex immune response that starts with the activation and differentiation of allergenspecific CD4+ T helper (Th) 2 cells. Th2 lymphocytes mediate their functions by releasing Th2 cytokines IL-4, IL-5, and IL-13 [11]. IL-4 switches B-cell antibody production to IgE that subsequently activates the highaffinity IgE receptor FcεRI-carrying mast cells, eosinophils and basophils. IL-5 recruits eosinophils into the lungs and IL-13 triggers AHR and mucus hyperproduction which are classical hallmarks of allergic asthma [9]. As for many nanomaterials, inhalation is one of the most significant routes of CuO exposure. Pulmonary exposure to CuO nanomaterials has been investigated previously in healthy subjects [13,14], but there is only limited information about the effects of the material on asthma as well as on allergic airway inflammation (AAI) –a condition preceding the chronic disease [2]. Using a mouse model of ovalbumin (OVA)-induced AAI (Additional file 5: Figure S1), the aim of this study was to investigate the adverse effects of nano-sized core CuO (CuO) on the pulmonary condition, and to understand whether the carboxylated (CuO COOH), methylaminated (CuO NH 3 ) and PEGylated (CuO PEG) derivates of CuO influence its reactivity. Results Particle characterisation and the total measured copper content in the test materials The total measured Cu concentration in the original powders of the different test materials is presented in Table 1, along with the details of purity, primary particle size, surface area and zeta potential of the materials investigated. On a mass basis of each material, the Cu content of each material varied according to the proportion of mass attributed to the coating. Consequently, less total Cu was measured in the coated ENMs relative to the uncoated form (Table 1). For the coated CuO NPs, the NH 4+ -coated NPs were found to contain the highest measured fraction of Cu (0.52), followed by the COOHcoated NPs (0.43) and least Cu in the PEG-coated NPs (0.29). Example images of the nanomaterials with particle size distributions of dispersions measured by nanoparticle tracking analysis are shown in Additional file 6: Figure S2 and Additional file 7: Figure S3. The primary particle sizes of the powders, as measured by transmission electron microscopy (TEM), did not exceed the manufacturer’s reported size range (10–20 nm). Following dispersion of the test materials in ultrapure water, the mean hydrodynamic diameters of the aggregates were measured by nanoparticle tracking analysis, and were: 41 nm in the uncoated CuO NPs, 46 nm in the ammonium-coated CuO NPs, 121 nm in the COOHcoated CuO NPs and 100 nm in the PEG-coated CuO NPs (Table 1). Similar sizes of aggregates were formed for the respective particles in Krebs physiological saline (Table 1). The dialysis experiments revealed some Cu dissolution from the different CuO NPs in ultrapure water. The dialysis curves are shown in Additional file 8:FigureS4.The Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 2 of 21 Table 1 Characterization of the CuO-containing ENMs from the original powders, adapted from Vassallo et al. [15] Test material (Supplier) Manufacturer’s information c Measured primary particle size (nm) d Measured hydrodynamic diameter in ultrapure water or Krebs saline (nm) e Total measured copper concentration (mg l -1 ) f Percentage of nominal concentration (%) g Measured copper fraction in coated CuO NPs h Metal dissolution rate in ultrapure water or Krebs saline (μgCuh -1 ) i Zeta potential in ultrapure water (mV) a CuO NPs uncoated, CAS 1317-38-0 (PlasmaChem GmbH, Lot YF1309121) 99% purity; diameter, 10 - 20 nm; b surface area 42 ± 2 m 2 g -1 12.00 ± 0.37 41 ± 28 (water) 62 ± 50 (Krebs) 287.1 ± 14.4 89.7 ± 4.5 -- 1.68 (water) 1.21 (Krebs) 14.0 ± 1.2 a CuO NPs COOH-coated, CAS 1317-38-0 (PlasmaChem GmbH, Lot YF140114) 99% purity; diameter, 10 - 20 nm; b surface area, 7.4 ± 0.5 m 2 g -1 6.45 ± 0.16 121 ± 91 (water) 128 ± 58 (Krebs) 154.3 ± 6.9 - 0.43 ± 0.02 69.12 (water) 18.15 (Krebs) - 7.3 ± 0.5 a CuO NPs NH 4+ -coated, CAS 1317-38-0 (PlasmaChem GmbH, Lot YF140114) 99% purity; diameter, 10 - 20 nm; b surface area, 6.1 ± 0.5 m 2 g -1 9.53 ± 0.22 46 ± 36 (water) 96 ± 75 (Krebs) 185.9 ± 7.2 - 0.52 ± 0.02 18.6 (water) 12.22 (Krebs) 27.7 ± 0.5 a CuO NPs PEG-coated, CAS 1317-38-0 (PlasmaChem GmbH, Lot YF140114) 99% purity; diameter, 10 -20nm 7.46 ± 0.42 100 ± 36 (water) 189 ± 113(Krebs) 105.0 ± 3.5 - 0.29 ± 0.01 52.02 (water) 17.44 (Krebs) - 16.8 ± 0.4 a Supplied as dry powders, bespoke design and production of spherical particles for the NANOSOLUTIONS project via Alexei Antipov, PlasmaChem GmbH b Brunauer–Emmett–Teller (BET) surface area values (mean ± one standard deviation, n= 3) from NANOSOLUTIONS project conducted by A. Besinis c Based on transmission electron microscopy (TEM) images of CuO ENMs from a 100 mg l -1 Cu stocks in Milli-Q water where data are mean ± standard error of the mean (S.E.M) with n= 60 measurements d Particle size distribution measurements (mean ± one standard deviation, n= 3) by Nanoparticle tracking analysis (NTA) on 100 mg l -1 Cu ENM stocks in Milli-Q water or Krebs physiological saline at pH 7.4 e Data are means ± S.E.M (n= 3 replicates) of total measured copper concentration by ICP-OES following aqua regia acid digestion of the dry powders, and after normalisation to an initial 0.02 g weight of material; Cupric oxide nanoparticles (CuO NPs) f With a 0.8 fraction of copper by weight in uncoated CuO NPs g Relative to the measured copper content in the uncoated CuO NPs h Maximum slope from rectangular hyperbola function of curve fitting used to estimate the maximum rate of dissolution of copper from the dialysis experiments, in triplicate i Zeta potential of CuO dispersions in ultrapure water (pH 5) as an average of five separate measurements.- Not possible to calculate from the manufacturer’s information on material composition; -- Data not applicable to the test material. The Krebs physiological saline data are from Besinis and Handy, unpublished Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 3 of 21 Fig. 1 (See legend on next page.) Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 4 of 21 dissolution rate of the uncoated CuO NPs was low (1.68 μgCuh −1 , Table 1), but in comparison, all the coated CuO NPs had higher dissolution rates; greater than 18 μgCuh −1 in ultrapure water (Table 1). However, the rates were still micromolar, and even the highest rates would only equate to maximally around 6–9% of the total metal being released every hour in water. The dissolution rates for the ENM in Krebs physiological saline were much lower than the equivalent material in ultrapure water (Table 1), and represented less than 3% of metal being released in an hour. Thus, it is expected that the materials will remain in particulate form in physiological saline over 24 h. Exposure to CuO nanomaterials induced significant neutrophil migration into the lungs of phosphatebuffered saline (PBS)- and OVA-challenged mice To characterize the pulmonary effects of CuO materials, inflammatory cell influx in bronchoalveolar lavage (BAL) and pathological changes in lung tissue were evaluated along with mRNA expression analysis of cytokines in tissue samples. Development of AAI in our model was evidenced by increased number of eosinophils and lymphocytes in BAL, activation of goblet cells in lung tissue and up-regulation of pro-inflammatory TNF, pro-allergic IL-33 and Th2 type IL-13 in the lungs of OVAchallenged compared with PBS-challenged mice (OVA versus PBS group in Fig. 1). Furthermore, OVAchallenged mice exhibited eosinophils as well as elevated number of CD4+ T cells in the lung tissue (OVA versus PBS in Fig. 2a-b, e). CuO materials tested in our study induced a significant neutrophil influx into the airways of PBSas well as OVAchallenged mice (Fig. 1a). The materials did not influence the number of eosinophils in BAL nor mucus-producing goblet cells in the lung tissue of OVA-challenged mice, except in case of CuO NH 3 whose exposure triggered additional migration of eosinophils into the airways (Fig. 1a-b). Co-exposure to OVA and CuO materials had an additive (mostly dose-dependent) effect on the number of macrophages and lymphocytes (Fig. 1a). Total number of BAL cells and relative proportion of differential immune cells is presented in Additional file 1: Table S1. CuO nanomaterials increased the expression of proinflammatory TNF and pro-allergic IL-33 but did not up-regulate IL-13 expression in PBS-challenged mice, as compared to specific controls (Fig. 1c). TNF and IL-33 levels were increased especially in response to CuO in both PBS-challenged as well as OVA-challenged in mice. In OVA-challenged mice, CuO nanomaterials rather suppressed the expression of IL-13 compared with OVA controls (Fig. 1c). IL-13 was measured also at the protein level in BAL supernatants and no significant changes were seen in OVA-challenged groups, except in a case of CuO NH 3 that had increased IL-13 protein release at doses 2.5 and 10 μg/mouse (Fig. 1d). Histological assessment of mice treated with CuO nanomaterials, revealed the presence of inflammatory areas and nuclear dust (Fig. 2a-d; Additional file 9:FigureS5). These changes were seen in both PBSand OVAchallenged mice treated with 10 and 40 μg/mouse of CuO, CuO COOH and CuO NH 3 . Inflammation was milder at 10 μg/mouse in CuO PEG-exposed mice (Additional file 9: Figure S5A) whereas the effects were comparable to the ones of other CuO materials at 40 μg/mouse. Nuclear dust was especially evident in the lung tissue at the highest dose of 40 μg/mouse, at which it was observed in response to all ENM (Fig. 2c-d). Since all materials exhibited similar changes at the highest dose, quantification of the inflammatory areas and nuclear dust was performed at dose 10 μg/mouse. The core material and CuO PEG only were selected for evaluation because the other surfacefunctionalized materials had equivalent effects on the lung tissue at this dose as CuO. It was found that exposure to CuO triggered or enhanced inflammation and caused the formation of nuclear dust in both OVAand PBSchallenged mice as compared with their respective controls (Additional file 9: Figure S5B). CuO PEG-induced inflammation in PBS-challenged mice was minor compared with PBS controls and significantly milder compared to the inflammation caused by CuO. Similar result appeared also in OVA-challenged mice but the difference between the groups of CuO and CuO PEG was statistically insignificant. Nuclear dust was observed only in CuObut not in CuO PEG-treated mice (Additional file 9: Figure S5C). (See figure on previous page.) Fig. 1 Cell counts, expression and release of (pro-)inflammatory markers following exposure to CuO materials.BALB/c mice were sensitized ip to OVA/Alum on day 1 and 10, and exposed repeatedly to 2.5, 10 or 40 μg/mouse of CuO nanomaterials dispersed in PBS with or without OVA by oropharyngeal aspiration after a 10-day recovery period. aBAL cell counts showed the ability of CuO materials to trigger an increase in the number of macrophages, lymphocytes and especially neutrophils into the airways of both PBSand OVA-challenged mice whereas eosinophils were detected only in OVA-challenged groups. bPAS staining revealed that CuO materials did not activate mucin-production in goblet cells. c Pro-inflammatory cytokines TNF and IL-33 were expressed in PBS-challenged as well as OVA-challenged mice while Th2 type cytokine IL-13 was expressed only in OVA-challenged mice. dIL-13 protein was detected in BAL supernatants by ELISA also only in OVA-challenged mice. Results in a-b are shown with the highest number of cells marked on top of each plot. Columns and error bars represent mean values ± standard error of mean (SEM). Statistically significant differences between experimental groups and PBS-challenged control mice are marked with “*”whereas the ones between experimental groups and OVA-challenged control mice are marked with “•”.*/•P< 0.05; **/••P< 0.01; ***/•••P< 0.001. HPF, high power field; PAS, periodic acid–Schiff Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 5 of 21 Fig. 2 (See legend on next page.) Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 6 of 21 Immunohistochemical staining of lung tissue revealed no increased influx of T cell subtypes in PBS-challenged mice. However, increase of CD3+ T cells in OVA-challenged mice was seen after treatment with all CuO materials, especially at the dose of 10 μg/mouse, except with CuO PEG (Fig. 2e). Further characterization showed that this increase was derived from the elevated presence of CD4+ T cells since the numbers of CD8+ T cells remained generally at similar levels as in OVA-challenged mice. Transcriptome analysis revealed that CuO PEG has a lower potential in triggering changes at gene expression level than other tested materials Hierarchical clustering of top 500 differentially expressed genes (DEGs) showed that lung tissue of PBS-challenged mice exposed to the two lower doses (2.5 and 10 μg/ mouse) of CuO PEG shared similar expression patterns with their controls (cluster e in Fig. 3a). The same trend was also seen among OVA-challenged mice (cluster a in Fig. 3a). Samples from mice treated with lower doses of all other materials and the highest dose of CuO PEG clustered together within both, PBS-challenged and OVA-challenged mice (clusters b-c in Fig. 3a). Regardless of the allergen challenge, mice exposed to higher doses of all materials except CuO PEG shared a similar expression pattern (cluster d in Fig. 3a). Number of DEGs in each experimental group compared with their respective controls revealed that more changes took place in PBS-challenged than in OVAchallenged mice (Fig. 3b, Additional file 2: Table S2). Every material triggered a dose-dependent response as the number of DEGs was increasing across the doses. CuO PEG induced notably less changes compared with other CuO materials which supports the finding seen on the heatmap. Venn comparisons of DEG sets of experimental groups revealed that the highest number of DEGs were exclusive or shared between higher doses of CuO, CuO COOH and CuO NH 3 in both PBSchallenged and OVA-challenged mice (Fig. 3c). To validate the microarray data, monocyte chemoattractants CCL2 (MCP-1) and CCL7 (MCP-3), and eosinophil-recruiting CCL11 (eotaxin-1) were chosen for reanalysis by real-time quantitative polymerase chain reaction (PCR). The PCR-based expression levels were highly correlating (Pearson’sr≥0.95) with the transcriptomics data (Additional file 10: Figure S6). Furthermore, CCL2 was measured at protein level in BAL supernatants and its levels were highly similar to those obtained by microarray and PCR. In addition, several biomarkers linked to AAI were also identified in the microarray-based transcriptomics data to present the differences between PBSand OVA-challenged control mice (Additional file 11: Figure S7). Irrespective of differentiating transcriptome profiles, CuO, CuO COOH and CuO NH 3 displayed commonalities at pathway level Excluding dose as a variable, we combined experimental groups of PBS-challenged or OVA-challenged mice that were treated with the same CuO material and compared their transcriptomic profiles against the ones of their corresponding controls to explore general differences between the materials at gene expression level (Fig. 4, Additional file 12: Figure S8, Additional file 3: Table S3). CuO PEG did not produce any DEGs in mice challenged with PBS or OVA, while the other three materials elicited specific transcriptomic changes in both phenotypes. In PBS-challenged mice, 7.9, 13.2 and 39.9% of the DEGs were identified as unique to CuO, CuO COOH and CuO NH 3 respectively, while 14.2% of the DEGs were shared between all three materials (Fig. 4a). A similar trend –where the most unique transcriptomic responses originated from CuO NH 3 –was also observed in transcriptome of OVA-challenged mice (Additional file 12: Figure S8), but no functionally (in)activated biological processes could be identified based on Ingenuity™pathway analysis (IPA) [16]. On the other hand, in PBS-challenged mice, the material-type-specific DEGs were predicted to activate/inactivate several pathways where the extent of activation/deactivation varied according to the material surface chemistry (Fig. 4b). All three materials suppressed Cell Cycle at G2/M DNA Damage Checkpoint Regulation and Antioxidant Action of Vitamin C pathway, for which the lowest degree of inhibition was predicted for core CuO DEGs. Similar surface-chemistry-based bioreactivity was also observed (See figure on previous page.) Fig. 2 Histological and immunohistochemical evaluation of the lung tissue after exposure to CuO nanomaterials.BALB/c mice were sensitized ip to OVA/Alum on day 1 and 10, and exposed repeatedly to 2.5, 10 or 40 μg/mouse of CuO nanomaterials dispersed in PBS with or without OVA by oropharyngeal aspiration after a 10-day recovery period. H&E-stained lung tissue of aa PBS-challenged control, bOVA-challenged, cPBSchallenged and 40 μg of core CuO-exposed, and dOVA-challenged and 40 μg of core CuO-exposed mouse. The presence of eosinophils in OVAchallenged mice and neutrophils accompanied with nuclear dust in CuO-treated mice were found. E, The number of CD3+, CD4+ and CD8+ T cells in the lung tissue. Images a-d are shown at × 400 magnification with a 50-μm scale bar. Arrows in the insets indicate the location of nuclear dust. Counts of T cell subtypes in eare shown as positive cells per high power field (HPF) with the highest number of cells marked on top of each plot. Columns and error bars represent mean values ± standard error of mean (SEM). Statistically significant differences between experimental groups and PBS-challenged control mice are marked with “*”whereas the ones between experimental groups and OVA-challenged control mice are marked with “•”.*/•P< 0.05; ***/•••P< 0.001 Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 7 of 21 for the predicted activation of several innate immunity and pro-inflammatory pathways (Fig. 4b; IPA comparison analysis). Among others, Aryl Hydrocarbon Receptor Signaling (least activated by core CuO) and Dendritic Cell Maturation pathways (least activated by CuO NH 3 ) were activated. To study similarities of the biological processes, EnrichR tool was used [17,18]. In PBS-challenged mice, 17 processes were commonly enriched in response to CuO, CuO COOH and CuO NH 3 (Fig. 4c). Majority of these were related to cytokine/chemokine signaling, but other inflammatory processes, such as macrophage and Fig. 3 Differential gene expression analysis.aHeat map of top 500 differentially expressed genes (linear FC > |1.5|, adjusted Pvalue < 0.05) in each analyzed lung tissue sample of PBSand OVA-challenged mice exposed to CuO nanomaterials by oropharyngeal aspiration (2.5, 10 and 40 μg/mouse). Z-score normalized log2 intensity values were used as input for hierarchical clustering. Red color indicates a higher expression while green refers to a lower expression. bNumbers of total, up-regulated, down-regulated and exclusively differentially expressed genes (DEGs; linear FC > |1.5|, adjusted Pvalue < 0.05) in lung tissue after exposure to CuO nanomaterials of PBSand OVA-challenged mice versus the corresponding control mice. cUpSet plots showing 20 largest intersections of DEGs that are either specific to a treatment or shared between experimental groups among PBS and OVA-challenged mice. The upper bar chart indicates the number of DEGs in each intersection Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 8 of 21 neutrophil chemotaxis, and cell division overlapped between the materials as well (Fig. 4d). In OVA-challenged mice, only few DEGs were obtained (Additional file 12: Figure S8A), however, enrichment analysis revealed one common biological process, i.e. neutrophil degranulation (Additional file 12: Figure S8B) that was seen also in PBS-challenged mice. PEGylation modulates bioreactivity of pristine CuO via predicted suppression of inflammatory pathways To investigate how surface functionalization affects the effects of core CuO, each dose of every modified material was compared against the corresponding dose of core CuO (Fig. 5, Additional file 4: Table S4). Numbers of DEGs showed that CuO COOH and CuO NH 3 triggered notably less changes than CuO PEG at all doses. Enrichment analysis by PANTHER [19] revealed that DEGs of CuO COOH and CuO NH 3 groups resulted in none or few significantly enriched biological processes (Fig. 5a). In contrast, a large number of biological processes were enriched by the set of DEGs in treatment groups of CuO PEG. Therefore, CuO PEG was chosen for further analysis to understand how the surface modification alters the effect of the core material. Upand down-regulated genes derived from CuO PEG groups were analyzed separately. It was found that Fig. 4 General profiling of canonical pathways and biological processes in PBS-challenged mice after CuO, CuO COOH and CuO NH 3 exposure. Experimental groups of exposed PBS-challenged mice were merged based on the test material, and their transcriptomic profiles were compared against the one of PBS-challenged controls. aA Venn distribution of the number of DEGs (linear FC > |1.5|, adjusted Pvalue < 0.05) in lungs of PBS-challenged mice exposed to CuO nanomaterials by oropharyngeal aspiration (2.5, 10 and 40 μg/mouse). bA heat map showing the activation z-scores of IPA canonical pathways filtered with z-score > 2 and -log(Pvalue) > 2 across the materials. cVenn comparison of significantly enriched biological processes (adjusted Pvalue < 0.05) obtained from analyses of DEG sets shown in (a). d17 biological processes were commonly enriched by DEGs from the CuO, CuO COOH and CuO NH 3 exposures Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 9 of 21 sonicated for 2 min. Further dilutions of 50, 200 and 800 μg/ml (2.5, 10 and 40 μg/mouse, respectively) were prepared in Dulbecco’s PBS (Gibco, Life Technologies, Carlsbad, CA) with or without ovalbumin (OVA; SigmaAldrich Co, St. Luis, MO) shortly after sonication and used immediately for orophagyngeal aspirations. Mice and sensitization Female BALB/c mice (aged 6–8 weeks) were obtained from Scanbur A/S (Karlslunde, Denmark) and quarantined for 1 week. The mice were housed in groups of four in transparent plastic cages bedded with aspen chip and were provided standard mouse chow diet (Altromin no. 1314 FORTI, Altromin Spezialfutter GmbH & Co., Germany) and tap water ad libitum when not being treated. The environment of the animal room was carefully controlled, with a 12 h dark-light cycle, temperature of 20–21 °C, and relative humidity of 40–45%. The experiments were performed in agreement with the European Convention for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes (Strasbourg March 18, 1986, adopted in Finland May 31, 1990). All experiments were approved by the State Provincial Office of Southern Finland (ESAVI-3241-04.10.07-2013, permission number PH701A). During the sensitization period, each mouse (eight per group) received ip a mixture of 50 μg of OVA and 2 mg of aluminum/magnesium hydroxide (Alum; Imject® Alum, Pierce Biotechnology, Rockford, IL) in 100 μlof Dulbecco’s PBS on day 1 and 10. After 10-day recovery, mice were exposed to 50 μl of Dulbecco’s PBS or 50 μg of OVA in 50 μl of Dulbecco’s PBS with or without dispersed CuO materials via oropharyngeal aspiration in isoflurane anaesthesia (Univentor 400 Anaesthesia Unit, Abbott Laboratories, IL) on four consecutive days. CuO materials were tested at three doses, 2.5, 10 and 40 μg/ mouse/administration. Mice were sacrificed using an overdose of inhaled isoflurane 24 h after the last administration (Additional file 5: Figure S1). Sample collection Blood was collected from the vena cava (hepatic vein), and the lungs were lavaged with Dulbecco’s PBS (800 μl for 10 s) via the tracheal tube. The mouse chest was opened, and half of the left pulmonary lobe was removed, divided in two, stabilized in RNAlater® solution (Life Technologies Ltd., Paisley, UK) and stored at − 70 °C for total RNA isolation and microarray analysis. A part of the left lobe was embedded in Tissue-Tek® O.C.T.™compound (Sakura Finetek, Alphen aan Den Rijn, The Netherlands), quick-frozen and stored at − 70 °C for immunohistochemical stainings. The rest of the lungs were formalin-fixed, embedded in paraffin, cut, affixed on slides, and stained with hematoxylin and eosin (H&E) and periodic acid-Schiff (PAS) solutions. BAL cell counts 100 μl of BAL samples were cytocentrifuged on slides at 1000 rpm for 10 min (Miles Scientific Cyto-Tek centrifuge, Sakura Finetek). The slides were air-dried and stained with May Grünwald-Giemsa (MGG). BAL cell differentials (number of macrophages, neutrophils, eosinophils and lymphocytes) were obtained as average counts from three high-power fields (HPF). The cells were counted at 50x under light microscopy (Leica DM 4000B; Leica, Wetzlar, Germany). Lung histology Recruitment of inflammatory cells into the lungs and morphological alterations of the tissue were assessed after H&E staining. The number of mucus-producing goblet cells was determined after PAS-staining from three bronchi per mouse in control group and six bronchi per mouse in treatment groups by counting PAS+ cells from 200 μm of bronchus surface under light microscope. Quantification of histological alterations was performed by measuring all inflammatory areas and areas including nuclear dust of one lung section per slide at a total magnification of 100x with AxioVision V4.8.2 software (Zeiss, Oberkochen, Germany). The results were expressed as averages of the measured areas. Immunohistochemistry Immunoperoxidase staining was used to detect CD3+, CD4+ and CD8+ lymphocytes in the lung tissue. Briefly, 4-μm frozen sections were fixed with cold acetone and stained with anti-mouse CD3 antibody (Ab; clone 17A2), anti-mouse CD4 Ab (clone RM4–5) and anti-mouse CD8 Ab (clone 53–6.7). All primary Abs were purchased from BD Biosciences Pharmingen (San Diego, California). Biotin-conjugated secondary Ab anti-rat IgG (H + L) was purchased from Vector Laboratories. Number of immunohistochemically stained positive cells was counted under light microscopy at × 400 magnification from slides of five samples/group as an average of three HPF/slide. mRNA expression of cytokines in lung tissue Lung samples were homogenized in 1 ml of TRIsure reagent (Bioline Reagents Ltd., London, UK) in Lysing matrix D tubes (MP Biomedicals, Illkirch, France) with a FastPrep FP120 machine (BIO 101, Thermo Savant, Waltham, MA, USA). The RNA extraction was performed following instructions provided by Bioline Reagents. The quantity and purity of isolated RNA was determined by NanoDrop spectrophotometer (ND-1000, Thermo Fisher Scientific Inc., Wilmington, NC, USA). Complementary DNA (cDNA) was synthesized from 500 ng of total RNA in a 25 μl reaction using MultiScribe Reverse Transcriptase and random primers (The Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 16 of 21 High-Capacity cDNA Archive Kit, Applied Biosystems, Foster City, CA, USA) according to the manufacturer’s protocol. The synthesis was performed in a 2720 Thermal Cycler (Applied Biosystems, Carlsbad, CA, USA) starting at 25 °C for 10 min and continuing at 37 °C for 120 min. Primers and probes (18S ribosomal RNA, TNF, IL-33, IL-13, CCL2, CCL7, CCL11) for PCR analysis were ordered as pre-developed assay reagents from Applied Biosystems. The PCR assays were performed in 96well optical reaction plates with Relative Quantification 7500 Fast System (7500 Fast Real-Time PCR system, Applied Biosystems) by the manufacturer’s instructions. Amplifications were done in 11 μl reaction volume containing TaqMan universal PCR master mix and primers provided by Applied Biosystems and 1 μl of cDNA sample. Ribosomal 18S was used as an endogenous control. Cytokine levels in BAL supernatants Protein secretion of IL-13 and CCL2 was measured in BAL supernatants by commercial mouse uncoated ELISA kits (Invitrogen, San Diego, CA). The assays were performed according to the manufacturer’s instructions and ELISA plate reader (Multiscan MS, Labsystems, Finland) was used to record the absorbances. DNA microarrays and statistical analysis of the data Total RNA samples isolated from lung tissue as described above were quantified and quality checked by NanoDrop and Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA), respectively. Samples with RNA integrity number (RIN) > 8 were selected for the analysis. Independent pools of two RNA samples (total of 100 ng) were used to synthesize cDNA which was transcribed to cRNA using T7 RNA polymerase amplification method (Low Input Quick Amp Labeling Kit, Agilent Technologies), according to the instructions of the manufacturer. cRNAs were labeled with Cy3 and Cy5 dyes (Agilent Technologies) and thereafter cleaned up by using Qiagen’s RNeasy mini spin columns (Qiagen, GmbH, Hilden, Germany). 300 ng of a Cy3-labeled sample and 300 ng of corresponding Cy5-labeled sample were combined (total 600 ng). cRNA samples were fragmented and hybridized to the Agilent 2-color 60-mer oligo arrays (Agilent SurePrint G3 Mouse Gene Expression v2 GE 8x60K). The slides were washed and scanned with Agilent Microarray Scanner G2505C (Agilent Technologies) and the raw intensity values were obtained with the Feature Extraction software, version 11.0.1.1 (Agilent Technologies). Raw data was quality checked according to the Agilent standard procedures. The median foreground intensities were imported into the R software [62] and analyzed with limma package within the BioConductor. Briefly, log2 transformation and quantile normalization was performed. Subsequently, batch effects derived from the labeling and array-specific variance were removed using the ComBat method [63] implemented in the sva package [64,65]. The same batches were taken also into account when limma package [66] was used. The values of the probes recognizing the same NCBI Entrez Gene identifiers were further averaged into the final expression matrix. The microarray data is deposited in NCBI Gene Expression Omnibus (GEO) database [67] and are accessible through GEO Series accession number GSE122197 (https://www.ncbi. nlm.nih.gov/geo/query/acc.cgi?acc=GSE122197). Differentially expressed genes were identified by using linear models and empirical Bayes pairwise comparisons (post hoc adjusted P< 0.05 and linear fold change (FC) > |1.5|). The resulting gene sets after Benjamini and Hochberg post hoc correction [63] were considered to be significant and were further studied. Analysis tools Enrichment analyses of biological processes were performed using Enrichr [17,18]andPANTHER[19]tools. REVIGO [68] was used to summarize lists of gene ontology (GO) terms. Canonical Pathway Analyses were carried out through the use of IPA [16]. Unless otherwise specified, the cut-off for a biological process or pathway to be considered significantly enriched was set at adjusted pvalue of 0.05. Bar and line plots were constructed using GraphPad Prism 7 (GraphPad Software Inc., San Diego, CA, USA) or IPA software. Heat maps and 2D scatter plots were created with Perseus [69,70], Venn diagrams with Venny 2.1.0 [71]. Tile plots were prepared by R software [62]usingthe codes obtained from REVIGO tool [68]. Statistical analysis of cell counts and mRNA expression levels For statistical analysis of cell counts and PCR results, the Analysis of Variance (ANOVA) was performed. Tukey honest significant differences post-hoc testing was carried out after ANOVA for selected pair-wise comparisons. A P-value of < 0.05 was considered to be statistically significant. Validation of microarray results by PCR was evaluated with Pearson’s correlation test. The results were considered accurate when Pearson’s r value was ~ 1. Additional files Additional file 1: Table S1. Total number of BAL cells and relative portion of differential immune cells. The numbers are presented as mean values ± standard error of mean (SEM). Statistically significant differences of total cell numbers between experimental groups and PBS-challenged control mice are marked with “*”whereas the ones between experimental groups and OVA-challenged control mice are marked with “•”. ***/•••P< 0.001. HPF, high power field; OVA, ovalbumin; PBS, phosphate buffered saline. (TIF 1108 kb) Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 17 of 21 Additional file 2: Table S2. Lists of the differentially expressed genes from the microarray experiment. The tables with the differentially expressed genes in each of the relevant pairwise comparisons (each experimental group compared against corresponding PBS-challenged or OVA-challenged controls) are reported in sheets of the Microsoft Excel archive, named accordingly. In each table, the significantly differentially expressed genes (Benjamini and Hochberg post hoc corrected Pvalue < 0.05 and absolute log2 fold change > 0.58) are ordered according to the decreasing log2 fold change. The Agilent probe IDs and gene symbols are shown. Additionally, for each significant gene, the average expression across the microarray samples, the t-test value, the nominal Pvalue, the adjusted Pvalue and the B value are also reported. (XLSX 1842 kb) Additional file 3: Table S3 Lists of the differentially expressed genes from the microarray experiment. Experimental groups of PBS-challenged or OVA-challenged mice that were treated with the same CuO material were combined and compared against their corresponding controls. The tables with the differentially expressed genes of each pairwise comparison are reported in sheets of the Microsoft Excel archive, named accordingly. In each table, the significantly differentially expressed genes (Benjamini and Hochberg post hoc corrected p-value < 0.05 and absolute log2 fold change > 0.58) are ordered according to the decreasing log2 fold change. The Agilent probe IDs and gene symbols are shown. Additionally, for each significant gene, the average expression across the microarray samples, the t-test value, the nominal Pvalue, the adjusted P value and the B value are also reported. (XLSX 256 kb) Additional file 4: Table S4. Lists of the differentially expressed genes from the microarray experiment. Experimental groups treated with modified CuO materials were compared against the groups treated with the corresponding dose of core CuO in PBS-challenged or OVAchallenged mice. The tables with the differentially expressed genes of each pairwise comparison are reported in sheets of the Microsoft Excel archive, named accordingly. In each table, the significantly differentially expressed genes (Benjamini and Hochberg post hoc corrected p-value < 0.05 and absolute log2 fold change > 0.58) are ordered according to the decreasing log2 fold change. The Agilent probe IDs and gene symbols are shown. Additionally, for each significant gene, the average expression across the microarray samples, the t-test value, the nominal Pvalue, the adjusted Pvalue and the B value are also reported. (XLSX 338 kb) Additional file 5: Figure S1. Murine model of allergic airway inflammation. BALB/c mice were sensitized intraperitoneally (ip) with a mixture of ovalbumin and aluminium/magnesium hydroxide (OVA/Alum) on day 1 and 10. After a 10-day recovery period, 2.5, 10 or 40 μg/mouse of CuO nanomaterials dispersed in PBS with or without OVA were administered repeatedly to mice by oropharyngeal aspiration on days 20–23. Mice were sacrificed on day 24 for sample collection of bronchoalveolar lavage fluid (BAL) and lung tissue. ENM, engineered nanomaterial; PBS, phosphate buffered saline. (TIF 444 kb) Additional file 6: Figure S2. Example transmission electron microscopy images of engineered CuO nanoparticles in 100 mg l −1 Milli-Q water showing, (A) uncoated CuO core, (B) CuO-COOH, (C) CuO-NH 4+ and, (D) CuO-PEG NPs. The respective Nanosight graphs show the particle distribution (bin sizes are hydrodynamic diameter) of the nanomaterials in 100 mg l −1 Milli-Q water solutions. From Besinis and Handy, unpublished. (TIF 2186 kb) Additional file 7: Figure S3. Example transmission electron microscopy images of engineered CuO nanoparticles in 100 mg l −1 Krebs physiological saline showing, (A) uncoated CuO core, (B) CuO-COOH, (C) CuO-NH 4+ and, (D) CuO-PEG NPs. The respective Nanosight graphs show the particle distribution (bin sizes are hydrodynamic diameter) of the nanomaterials in 100 mg l −1 Milli-Q water solutions. From Besinis and Handy, unpublished. (TIF 2625 kb) Additional file 8: Figure S4. Dialysis curves showing the release of total dissolved copper from uncoated CuO NPs core, CuO-COOH, CuO-NH 4+ and CuO-PEG NPs over a 24 h period when suspended in (A) Ultrapure Milli-Q water, or (B) Krebs physiological saline. Controls are the respective water or saline without nanomaterials. Data are means ± S.D., n= 3 replicates. Curves were fitted using SigmaPlot 13.0 (Systat Software, Inc.) applying the single rectangular two parameter hyperbola equation on the raw data. From Besinis and Handy, unpublished. (TIF 672 kb) Additional file 9: Figure S5. Histological assessment of the lung tissue after exposure to 10 μg/mouse of CuO nanomaterials. BALB/c mice were sensitized ip to OVA/Alum on day 1 and 10, and exposed repeatedly to 10 μg/mouse of CuO nanomaterials dispersed in PBS with or without OVA by oropharyngeal aspiration after a 10-day recovery period. A, H&Estained lung tissue of PBSand OVA-challenged mice that were treated with or without CuO nanomaterials. Histological changes were quantified in selected experimental groups and the results were expressed as an average size of inflammatory areas/section (B) and as an average size of areas containing nuclear dust/section (C). Images (A) are shown at × 200 magnification with a 100-μm scale bar. Columns and error bars represent mean values ± standard error of mean (SEM). *P< 0.05; **P< 0.01; ***P< 0.001. (TIF 6184 kb) Additional file 10: Figure S6. Validation of microarray data by PCR and ELISA. On the basis of the DNA microarray results, three genes were selected for validation by PCR taking into account their biological relevance in recruitment of leucocytes or in allergic airway inflammation. PCR results confirmed an upregulation of monocyte-recruiting chemokines CCL2 and CCL7 in lungs of ENM-treated mice compared with respective control mice at both time points, and induction of and eosinophil-attracting chemokine CCL11 in OVA-challenged mice. Microarray measurements were considered valid as the expression measured by PCR was concordant with microarray data and the correlation between the results was significant. In addition, CCL2 was measured at protein level in BAL supernatants by ELISA and the results were highly similar to those obtained by microarray and PCR. Left-sided plots present microarray results, right-sided plots in the top panel and the middle plot in the bottom panel express PCR results. Protein concentrations of CCL2 are presented in the bottom right. Data points and error bars represent mean values ± standard error of mean (SEM). AU, arbitrary unit of fluorescence intensity; RU, relative expression unit. (TIF 1265 kb) Additional file 11: Figure S7. Biomarkers associated to allergic airway inflammation were found up-regulated in ovalbumin (OVA)-challenged mice. Several markers known to play a role in asthma were chosen (proallergic IL-33, Th2 type cytokine IL-4, eosinophil-attracting chemokines CCL11 and CCL24, Th2 lymphocyte-attracting chemokine CCL17, and mucins MUC5AC and MUC5B), and their microarray gene expression data were compared in OVA-challenged and PBS-challenged mice. All selected markers were significantly up-regulated in mice challenged with OVA indicating that allergic airway inflammation developed in OVA-induced mice. AU, arbitrary unit of fluorescence intensity; PBS, phosphate buffered saline. (TIF 455 kb) Additional file 12: Figure S8. General profiling of canonical pathways and biological processes in OVA-challenged mice after CuO, CuO COOH and CuO NH 3 exposure. Experimental groups of exposed OVA-challenged mice were merged based on the test material, and their transcriptomic profiles were compared against the one of OVA-challenged controls. A, Number of differentially expressed genes (DEGs; linear fold change (FC) > |1.5|, adjusted Pvalue < 0.05) in lungs of OVA-challenged mice exposed to CuO nanomaterials by oropharyngeal aspiration (2.5, 10 and 40 μg/ mouse). B, Number of significantly enriched biological processes (adjusted Pvalue < 0.05) obtained from analyses of DEG sets shown in (A), and biological process enriched commonly by DEGs of CuO, CuO COOH and CuO NH 3 exposure with adjusted Pvalues. (TIF 848 kb) Additional file 13: Figure S9. DEGs with negative fold change in PBS mice exposed to 2.5 μg of CuO PEG versus core CuO at the same dose belong to processes related to inflammatory response. Treemap of GO biological processes significantly enriched by down-regulated genes in PBS-challenged mice exposed to 2.5 μg of CuO PEG compared against core CuO at the same dose. The size of each rectangle reflects the false discovery rate (FDR) of the corresponding GO term. GO terms are grouped by semantic similarity (SimRel, similarity = 0.5) into the same upper hierarchy term, visualized with different colours. (TIF 1139 kb) Additional file 14: Figure S10. GO biological processes enriched by DEGs in PBS mice exposed to 40 μg of CuO PEG versus core CuO at the same dose belong to processes related to inflammatory response. Treemap of GO biological processes significantly enriched by DEGs with A) a positive fold change and B) a negative fold change. The size of each Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 18 of 21 rectangle reflects the FDR of the corresponding GO term. GO terms are grouped by semantic similarity (SimRel, similarity = 0.5) into the same upper hierarchy term, visualized with different colours. (TIF 1880 kb) Additional file 15: Figure S11. DEGs with a positive fold change in OVA-challenged mice exposed to 40 μg of CuO PEG versus core CuO at the same dose belong to processes related to inflammatory response. Treemap of GO biological processes significantly enriched by upregulated genes in OVA-challenged mice exposed to 40 μg of CuO PEG compared against core CuO at the same dose. The size of each rectangle reflects FDR of the corresponding GO term. GO terms are grouped by semantic similarity (SimRel, similarity = 0.5) into the same upper hierarchy term, visualized with different colours. (TIF 732 kb) Additional file 16: Figure S12. Number of DEGs and canonical pathways involved in the inflammatory response to CuO materials in PBSchallenged and OVA-challenged mice. A differential expression analysis was performed in which each experimental group was compared against their corresponding negative controls. Thereafter, the activation z-scores of the pathways obtained from IPA were compared in PBS-challenged and OVAchallenged mice. A, A table of DEG numbers specific to PBSand OVAchallenged groups, and an overlap between them (number of common DEGs). B, A heatmap of (in)activation of predicted canonical pathways with IPA cut-offs of absolute z-score > 2 and -log(Pvalue) > 2. (TIF 1300 kb) Additional file 17: Figure S13. Canonical pathways involved in the inflammatory response to CuO materials. A differential expression analysis was performed in which each experimental group was compared against their respective PBSor OVA-challenged control group. Activation z-scores of the predicted pathways with cut-offs of absolute z-score > 2 and -log(P value) > 2 were obtained from IPA and compared in PBS-challenged and OVA-challenged mice. A-D, 2D-scatterplots of canonical pathways affected by A) CuO, B) CuO COOH, C) CuO NH 3 and D) CuO PEG exposure (40 μg/mouse) in PBSand OVA-challenged mice. Top 5 pathways of each section with the highest z-scores have been denoted above or below the plot, and marked on the plot with a filled circle. Blue circles refer to correlated and red to anticorrelated pathways. Black circles indicate the pathways that had a z-score of zero in one of the data sets. (TIF 1108 kb) Acknowledgements Kind thanks to Dr. A. Antipov for providing the coated ENM and their associated core material as part of the NANOSOLUTIONS project. The authors wish to thank also M. Lehto, L. Pylkkänen, S. Savukoski, P. Alander, S. Hirvikorpi, S. Tillander and M-L. Majuri for their excellent technical assistance. Authors’contributions MI and HA designed and MI performed the experiments. MI, PASK and PK collected the samples. MI performed the quantitative analysis of BAL cells, histology and T cell subtypes, carried out PCR and ELISA analyses, and with the assistance of PASK and DG, conducted the microarray experiment. VM, VF and DG preprocessed the microarray data, MI analyzed the preprocessed data with the assistance of HA, JN and DG. HW performed the qualitative evaluation of histopathological changes with MI. YF was involved in material production. AB, JV and RDH measured the primary particle sizes, characterized the dispersions of the particles, and performed the dialysis experiments. MC, NE and KL carried out the zeta potential measurements. MI and HA interpreted the results of the study. MI wrote the manuscript. JN, PK, VF, MC, KL, RDH, KS and HA contributed to the manuscript by writing and/or commenting. All co-authors approved the final manuscript. Funding The work leading to these results received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 309329 (NANOSOLUTIONS), and from the Academy of Finland. Availability of data and materials The data supporting the conclusions of this article are included within the article and its additional files. The microarray data are available in the NCBI GEO database and are accessible through GEO Series accession number GSE122197 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE122197). Ethics approval Animal experiments conducted in this study were performed in agreement with the European Convention for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes (Strasbourg March 18, 1986, adopted in Finland May 31, 1990). All experiments were approved by the State Provincial Office of Southern Finland (ESAVI-3241-04.10.07-2013, permission number PH701A). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Human Microbiome Research Program, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland. 2 Institute of Biotechnology, University of Helsinki, 00790 Helsinki, Finland. 3 Faculty of Medicine and Life Sciences, University of Tampere, 33100 Tampere, Finland. 4 Biomedicine Institute, University of Eastern Finland, 70211 Kuopio, Finland. 5 PlasmaChem GmbH, 12489 Berlin, Germany. 6 National Food Institute, Technical University of Denmark, 2800 Lyngby, Denmark. 7 Department of Chemical and Biochemical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark. 8 School of Biological and Marine Sciences, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK. 9 Plymouth University Peninsula Schools of Medicine and Dentistry, University of Plymouth, John Bull Building, Tamar Science Park, Plymouth PL6 8BU, UK. 10 Finnish Institute of Occupational Health, 00250 Helsinki, Finland. 11 Department of Pathology, University of Helsinki, 00014 Helsinki, Finland. 12 Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden. Received: 20 July 2018 Accepted: 4 June 2019 References 1. Landsiedel R, Ma-Hock L, Kroll A, Hahn D, Schnekenburger J, Wiench K, Wohlleben W. Testing metal-oxide nanomaterials for human safety. Adv Mater. 2010;22(24):2601–27. 2. Park JW, Lee IC, Shin NR, Jeon CM, Kwon OK, Ko JW, Kim JC, Oh SR, Shin IS, Ahn KS. Copper oxide nanoparticles aggravate airway inflammation and mucus production in asthmatic mice via MAPK signaling. Nanotoxicology. 2016;10(4):445–52. 3. Ahamed M, Akhtar MJ, Alhadlaq HA, Alrokayan SA. Assessment of the lung toxicity of copper oxide nanoparticles: current status. Nanomedicine (Lond). 2015;10(15):2365–77. 4. Grigore ME, Biscu ER, Holban AM, Gestal MC, Grumezescu AM. Methods of synthesis, properties and biomedical applications of CuO nanoparticles. Pharmaceuticals (Basel). 2016;9(4):75. 5. Gnanavel V, Palanichamy V, Roopan SM. Biosynthesis and characterization of copper oxide nanoparticles and its anticancer activity on human colon cancer cell lines (HCT-116). J Photochem Photobiol B. 2017;171:133–8. 6. Perlman O, Weitz IS, Azhari H. Copper oxide nanoparticles as contrast agents for MRI and ultrasound dual-modality imaging. Phys Med Biol. 2015; 60(15):5767–83. 7. The Global Asthma Report 2014. http://www.globalasthmareport.org (Accessed 18 June 2018). 8. Kim HY, DeKruyff RH, Umetsu DT. The many paths to asthma: phenotype shaped by innate and adaptive immunity. Nat Immunol. 2010;11(7):577–84. 9. Deckers J, De Bosscher K, Lambrecht BN, Hammad H. Interplay between barrier epithelial cells and dendritic cells in allergic sensitization through the lung and the skin. Immunol Rev. 2017;278(1):131–44. 10. Jaligama S, Patel VS, Wang P, Sallam A, Harding J, Kelley M, Mancuso SR, Dugas TR, Cormier SA. Radical containing combustion derived particulate matter enhance pulmonary Th17 inflammation via the aryl hydrocarbon receptor. Part Fibre Toxicol. 2018;15(1):20. 11. Meldrum K, Guo C, Marczylo EL, Gant TW, Smith R, Leonard MO. Mechanistic insight into the impact of nanomaterials on asthma and allergic airway disease. Part Fibre Toxicol. 2017;14(1):45. 12. Lukacs NW. Role of chemokines in the pathogenesis of asthma. Nat Rev Immunol. 2001;1(2):108–16. Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 19 of 21 13. Gosens I, Cassee FR, Zanella M, Manodori L, Brunelli A, Costa AL, Bokkers BG, de Jong WH, Brown D, Hristozov D, Stone V. Organ burden and pulmonary toxicity of nano-sized copper (II) oxide particles after short-term inhalation exposure. Nanotoxicology. 2016;10(8):1084–95. 14. Costa PM, Gosens I, Williams A, Farcal L, Pantano D, Brown DM, Stone V, Cassee FR, Halappanavar S, Fadeel B. Transcriptional profiling reveals gene expression changes associated with inflammation and cell proliferation following short-term inhalation exposure to copper oxide nanoparticles. J Appl Toxicol. 2018;38(3):385–97. 15. Vassallo J, Tatsi K, Bodenab R, Handy RD. Determination of the bioaccessible fraction of cupric oxide nanoparticles in soils using an in vitro human digestibility simulation. Environ Sci: Nano. 2019;6:432–443. 16. Kramer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics. 2014;30(4):523–30. 17. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128. 18. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma'ayan A. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90–7. 19. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, Thomas PD. PANTHER version 11: expanded annotation data from gene ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 2017;45(D1):D183–9. 20. Su Y, Zheng X, Chen Y, Li M, Liu K. Alteration of intracellular protein expressions as a key mechanism of the deterioration of bacterial denitrification caused by copper oxide nanoparticles. Sci Rep. 2015;5:15824. 21. Tarasova NK, Gallud A, Ytterberg AJ, Chernobrovkin A, Aranzaes JR, Astruc D, Antipov A, Fedutik Y, Fadeel B, Zubarev RA. Cytotoxic and Proinflammatory effects of metal-based nanoparticles on THP-1 monocytes characterized by combined proteomics approaches. J Proteome Res. 2017;16(2):689–97. 22. Hanagata N, Zhuang F, Connolly S, Li J, Ogawa N, Xu M. Molecular responses of human lung epithelial cells to the toxicity of copper oxide nanoparticles inferred from whole genome expression analysis. ACS Nano. 2011;5(12):9326–38. 23. Wang Z, Li N, Zhao J, White JC, Qu P, Xing B. CuO nanoparticle interaction with human epithelial cells: cellular uptake, location, export, and genotoxicity. Chem Res Toxicol. 2012;25(7):1512–21. 24. Akhtar MJ, Kumar S, Alhadlaq HA, Alrokayan SA, Abu-Salah KM, Ahamed M. Dose-dependent genotoxicity of copper oxide nanoparticles stimulated by reactive oxygen species in human lung epithelial cells. Toxicol Ind Health. 2016;32(5):809–21. 25. Strauch BM, Niemand RK, Winkelbeiner NL, Hartwig A. Comparison between microand nanosized copper oxide and water soluble copper chloride: interrelationship between intracellular copper concentrations, oxidative stress and DNA damage response in human lung cells. Part Fibre Toxicol. 2017;14(1):28. 26. Liu Y, Beyer A, Aebersold R. On the dependency of cellular protein levels on mRNA abundance. Cell. 2016;165(3):535–50. 27. Lambrecht BN, Hammad H. The immunology of asthma. Nat Immunol. 2015;16(1):45–56. 28. Ordonez CL, Khashayar R, Wong HH, Ferrando R, Wu R, Hyde DM, Hotchkiss JA, Zhang Y, Novikov A, Dolganov G, Fahy JV. Mild and moderate asthma is associated with airway goblet cell hyperplasia and abnormalities in mucin gene expression. Am J Respir Crit Care Med. 2001;163(2):517–23. 29. Barnes PJ. Immunology of asthma and chronic obstructive pulmonary disease. Nat Rev Immunol. 2008;8(3):183–92. 30. Inoue K, Koike E, Yanagisawa R, Hirano S, Nishikawa M, Takano H. Effects of multi-walled carbon nanotubes on a murine allergic airway inflammation model. Toxicol Appl Pharmacol. 2009;237(3):306–16. 31. Inoue K, Yanagisawa R, Koike E, Nishikawa M, Takano H. Repeated pulmonary exposure to single-walled carbon nanotubes exacerbates allergic inflammation of the airway: possible role of oxidative stress. Free Radic Biol Med. 2010;48(7):924–34. 32. Mizutani N, Nabe T, Yoshino S. Exposure to multiwalled carbon nanotubes and allergen promotes earlyand late-phase increases in airway resistance in mice. Biol Pharm Bull. 2012;35(12):2133–40. 33. Ronzani C, Casset A, Pons F. Exposure to multi-walled carbon nanotubes results in aggravation of airway inflammation and remodeling and in increased production of epithelium-derived innate cytokines in a mouse model of asthma. Arch Toxicol. 2014;88(2):489–99. 34. Brandenberger C, Rowley NL, Jackson-Humbles DN, Zhang Q, Bramble LA, Lewandowski RP, Wagner JG, Chen W, Kaplan BL, Kaminski NE, Baker GL, Worden RM, Harkema JR. Engineered silica nanoparticles act as adjuvants to enhance allergic airway disease in mice. Part Fibre Toxicol. 2013;10:26. 35. Park HJ, Sohn JH, Kim YJ, Park YH, Han H, Park KH, Lee K, Choi H, Um K, Choi IH, Park JW, Lee JH. Acute exposure to silica nanoparticles aggravate airway inflammation: different effects according to surface characteristics. Exp Mol Med. 2015;47:e173. 36. Han B, Guo J, Abrahaley T, Qin L, Wang L, Zheng Y, Li B, Liu D, Yao H, Yang J, Li C, Xi Z, Yang X. Adverse effect of nano-silicon dioxide on lung function of rats with or without ovalbumin immunization. PLoS One. 2011;6(2):e17236. 37. Rossi EM, Pylkkanen L, Koivisto AJ, Nykasenoja H, Wolff H, Savolainen K, Alenius H. Inhalation exposure to nanosized and fine TiO2 particles inhibits features of allergic asthma in a murine model. Part Fibre Toxicol. 2010;7:35. 38. Jonasson S, Gustafsson A, Koch B, Bucht A. Inhalation exposure of nanoscaled titanium dioxide (TiO2) particles alters the inflammatory responses in asthmatic mice. Inhal Toxicol. 2013;25(4):179–91. 39. LeBoit PE. Dust to dust. Am J Dermatopathol. 2005;27(3):277–8. 40. Newcomb DC, Peebles RS Jr. Th17-mediated inflammation in asthma. Curr Opin Immunol. 2013;25(6):755–60. 41. Alcorn JF, Crowe CR, Kolls JK. TH17 cells in asthma and COPD. Annu Rev Physiol. 2010;72:495–516. 42. Eash KJ, Greenbaum AM, Gopalan PK, Link DC. CXCR2 and CXCR4 antagonistically regulate neutrophil trafficking from murine bone marrow. J Clin Invest. 2010;120(7):2423–31. 43. Reichel CA, Rehberg M, Lerchenberger M, Berberich N, Bihari P, Khandoga AG, Zahler S, Krombach F. Ccl2 and Ccl3 mediate neutrophil recruitment via induction of protein synthesis and generation of lipid mediators. Arterioscler Thromb Vasc Biol. 2009;29(11):1787–93. 44. Lacy P. Mechanisms of degranulation in neutrophils. Allergy Asthma Clin Immunol. 2006;2(3):98–108. 45. Papayannopoulos V. Neutrophil extracellular traps in immunity and disease. Nat Rev Immunol. 2018;18(2):134–47. 46. Tatsi K, Shaw BJ, Hutchinson TH, Handy RD. Copper accumulation and toxicity in earthworms exposed to CuO nanomaterials: effects of particle coating and soil ageing. Ecotoxicol Environ Saf. 2018;166:462–73. 47. Studer AM, Limbach LK, Van Duc L, Krumeich F, Athanassiou EK, Gerber LC, Moch H, Stark WJ. Nanoparticle cytotoxicity depends on intracellular solubility: comparison of stabilized copper metal and degradable copper oxide nanoparticles. Toxicol Lett. 2010;197(3):169–74. 48. Limbach LK, Wick P, Manser P, Grass RN, Bruinink A, Stark WJ. Exposure of engineered nanoparticles to human lung epithelial cells: influence of chemical composition and catalytic activity on oxidative stress. Environ Sci Technol. 2007;41(11):4158–63. 49. Karlsson HL, Cronholm P, Gustafsson J, Moller L. Copper oxide nanoparticles are highly toxic: a comparison between metal oxide nanoparticles and carbon nanotubes. Chem Res Toxicol. 2008;21(9):1726–32. 50. Karlsson HL, Gustafsson J, Cronholm P, Moller L. Size-dependent toxicity of metal oxide particles--a comparison between nanoand micrometer size. Toxicol Lett. 2009;188(2):112–8. 51. Tong R, Kohane DS. New strategies in Cancer nanomedicine. Annu Rev Pharmacol Toxicol. 2016;56:41–57. 52. Suk JS, Xu Q, Kim N, Hanes J, Ensign LM. PEGylation as a strategy for improving nanoparticle-based drug and gene delivery. Adv Drug Deliv Rev. 2016;99 (Pt A:28–51. 53. Gref R, Minamitake Y, Peracchia MT, Trubetskoy V, Torchilin V, Langer R. Biodegradable long-circulating polymeric nanospheres. Science. 1994; 263(5153):1600–3. 54. Rehberg M, Praetner M, Leite CF, Reichel CA, Bihari P, Mildner K, Duhr S, Zeuschner D, Krombach F. Quantum dots modulate leukocyte adhesion and transmigration depending on their surface modification. Nano Lett. 2010;10(9):3656–64. 55. Wagner VE, Bryers JD. Monocyte/macrophage interactions with base and linearand star-like PEG-modified PEG-poly(acrylic acid) co-polymers. J Biomed Mater Res A. 2003;66(1):62–78. 56. Siddiqui MA, Alhadlaq HA, Ahmad J, Al-Khedhairy AA, Musarrat J, Ahamed M. Copper oxide nanoparticles induced mitochondria mediated apoptosis in human hepatocarcinoma cells. PLoS One. 2013;8(8):e69534. 57. Chibber S, Shanker R. Can CuO nanoparticles lead to epigenetic regulation of antioxidant enzyme system? J Appl Toxicol. 2017;37(1):84–91. Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 20 of 21 58. Gao F, Ma N, Zhou H, Wang Q, Zhang H, Wang P, Hou H, Wen H, Li L. Zinc oxide nanoparticles-induced epigenetic change and G2/M arrest are associated with apoptosis in human epidermal keratinocytes. Int J Nanomedicine. 2016;11:3859–74. 59. Yuan R, Xu H, Liu X, Tian Y, Li C, Chen X, Su S, Perelshtein I, Gedanken A, Lin X. Zinc-doped copper oxide nanocomposites inhibit the growth of human Cancer cells through reactive oxygen species-mediated NF-kappaB activations. ACS Appl Mater Interfaces. 2016;8(46):31806–12. 60. Vassallo J, Besinis A, Boden R, Handy RD. The minimum inhibitory concentration (MIC) assay with Escherichia coli: an early tier in the environmental hazard assessment of nanomaterials? Ecotoxicol Environ Saf. 2018;162:633–46. 61. Muoth C, Wichser A, Monopoli M, Correia M, Ehrlich N, Loeschner K, Gallud A, Kucki M, Diener L, Manser P, Jochum W, Wick P, Buerki-Thurnherr T. A 3D co-culture microtissue model of the human placenta for nanotoxicity assessment. Nanoscale. 2016;8(39):17322–32. 62. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for statistical computing. ISBN 3-90005107-0. http://www.R-project.org. Accessed 18 June 2018) 63. Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 1995;57(1):289–300. 64. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–27. 65. Leek JT, Johnson E, Parker HS, Jaffe AE, JD S. Sva: Surrogate variable analysis; 2013. 66. Smyth G. Limma: Linear models for microarray data; 2005. 67. EdgarR,DomrachevM,LashAE.Geneexpressionomnibus:NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10. 68. Supek F, Bosnjak M, Skunca N, Smuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6(7):e21800. 69. Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, Mann M, Cox J. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods. 2016;13(9):731–40. 70. Cox J, Mann M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics. 2012;13 Suppl 16:S12. 71. Oliveros, J. C. Venny. An interactive tool for comparing lists with Venn's diagrams. http://bioinfogp.cnb.csic.es/tools/venny/index.html. Accessed 18 June 2018). Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ilves et al. Particle and Fibre Toxicology (2019) 16:28 Page 21 of 21