Network Analysis Reveals Similar Transcriptomic Responses to

Network Analysis Reveals Similar Transcriptomic Responses to Intrinsic Properties of Carbon Nanomaterials in Vitro and in Vivo Pia Kinaret,†,‡ Veer Marwah,† Vittorio Fortino,† Marit Ilves,‡ Henrik Wolff,§ Lasse Ruokolainen,∥ Petri Auvinen,† Kai Savolainen,§ Harri Alenius,‡,⊥ and Dario Greco*,† †

Institute of Biotechnology, ‡Department of Bacteriology and Immunology, and ∥Department of Biosciences, University of Helsinki, Helsinki, Finland 00014 § Finnish Institute of Occupational Health, Helsinki, Finland 00251 ⊥ Institute of Environmental Medicine (IMM), Karolinska Institutet, 171 77 Stockholm, Sweden S Supporting Information *

ABSTRACT: Understanding the complex molecular alterations related to engineered nanomaterial (ENM) exposure is essential for carrying out toxicity assessment. Current experimental paradigms rely on both in vitro and in vivo exposure setups that often are difficult to compare, resulting in questioning the real efficacy of cell models to mimic more complex exposure scenarios at the organism level. Here, we have systematically investigated transcriptomic responses of the THP-1 macrophage cell line and lung tissues of mice, after exposure to several carbon nanomaterials (CNMs). Under the assumption that the CNM exposure related molecular alterations are mixtures of signals related to their intrinsic properties, we inferred networks of responding genes, whose expression levels are coordinately altered in response to specific CNM intrinsic properties. We observed only a minute overlap between the sets of intrinsic property-correlated genes at different exposure scenarios, suggesting specific transcriptional programs working in different exposure scenarios. However, when the effects of the CNM were investigated at the level of significantly altered molecular functions, a broader picture of substantial commonality emerged. Our results imply that in vitro exposures can efficiently recapitulate the complex molecular functions altered in vivo. In this study, altered molecular pathways in response to specific CNM intrinsic properties have been systematically characterized from transcriptomic data generated from multiple exposure setups. Our computational approach to the analysis of network response modules further revealed similarities between in vitro and in vivo exposures that could not be detected by traditional analysis of transcriptomics data. Our analytical strategy also opens a possibility to look for pathways of toxicity and understanding the molecular and cellular responses identified across predefined biological themes. KEYWORDS: carbon nanomaterials, transcriptomics, pathways of toxicity, gene networks, network response modules, toxicogenomics, mechanism of action

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signaling pathways and altered gene expression levels, related to ENM morphology, still remain largely unknown. Moreover, while many studies have been focusing on cellular phenotypic responses at the morphological and biochemical levels, less information is available on the effect of particle size and

ngineered nanomaterials (ENMs) are entering the market with fast growing speed, and the need to assess their safety is widely recognized, for a variety of distinct ENM characteristics might affect the biological systems and their functions. To date, however, the association between ENM effects and their toxicity-driving physicochemical properties is still poorly understood. Several intrinsic properties of ENMs, such as shape, size, and surface area, are known to affect their recognition and uptake by specific cell types and ultimately the downstream cellular responses.1−5 The molecular © 2017 American Chemical Society

Received: December 26, 2016 Accepted: April 5, 2017 Published: April 5, 2017 3786

DOI: 10.1021/acsnano.6b08650 ACS Nano 2017, 11, 3786−3796

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ACS Nano Table 1. CNMs Tested in the Studya description long rigid multiwalled carbon nanotube long tangled multiwalled carbon nanotube short tangled multiwalled carbon nanotube long rigid carbon fiber hollow carbon sphere short rigid multiwalled carbon nanotube

material acronym rCNT tCNT Baytube graphite fullerene SES

product code MWCNT-7 mitsui Cheaptubes Baytubes C150 HP 636398 MTS60 900−1260

provider

shape

aspect ratio

avg. length (nm)

avg. diameter (nm)

avg. surface area (m2/g)

references for characterization data

Mitsui & Co.

tube

2.6

13000

50

22

24 and 39

Cheaptubes Inc. Bayer Material Science Sigma-Aldrich MTR Ltd. SES Research

tube

26.09

30000

11.5

180

24

tube

0.69

1000

14.5

204

38

fiber sphere tube

0.71 0.01 0.75

10000 100 1500

140 100 20

32 20 60

38 38 38

a

Names and producers of the CNMs studied, along with the values of their investigated properties. Material description, acronyms, product code, provider, shape, and CNM intrinsic properties of aspect ratio (calculated as average length divided by average diameter), length (average), diameter (average), and surface area (average) are provided.

exposed cells in individual response modules. In prospective, the systematic collection of such information can also be useful for computationally predicting the effects of less experimentally characterized ENMs. In this study, we investigated the TMOA of a variety of CNMs by integrating the transcriptomic data from in vivo and in vitro studies in mouse lungs and human cells, respectively. We could highlight transcriptional responses to specific CNM properties across the exposure setups. Particularly, we focused on the molecular effects of the aspect ratio and its individual components, the length, and the diameter, as well as the surface area of the CNM. We also inferred network response modules of gene expression patterns correlated with CNM properties, representing sets of evocative biological functions.

geometry on the gene expression at the global transcriptomic level.6,7 Among other classes of ENMs, carbon nanomaterials (CNMs) have been increasingly utilized in several industrial applications.8 One of the main exposure routes of CNM is via the lungs, and thus efforts in studying the pulmonary responses have mainly focused on in vivo exposures to murine models as well as in vitro exposures to cell types representing those physiologically resident in the lung.9 With the growing number of ENMs entering the market, it is impossible to study the toxicity effects of all the available materials by expensive and time-consuming in vivo exposures. Substitution of whole organism exposures by cell culture models would also diminish the number of animal experiments in toxicity studies, as suggested by the principles of replacement, reduction, and refinement (3R) of animal testing.10 Yet, systematic comparisons of the exposure models across multiple organisms to elucidate common patterns of molecular alterations are still missing. The systemic physio-pathological consequences of an exposure at the organism level are usually referred to as the mode of action (MoA). Instead, the combination of all the molecular effects of a chemical on the exposed cells or tissues can be referred to as the mechanism of action (MOA). Transcriptomics experiments, in which the transcriptional modulation of virtually all the genes of a given genome are assessed, can help characterizing the transcriptional mechanism of action (TMOA) of distinct ENMs, facilitating the replacement of some in vivo assays with in vitro tests, in which specific molecular markers or pathways could be investigated in a cost- and time-effective manner.11−15 Although a number of key studies have already highlighted important aspects of the TMOA of multiple ENMs,16−20 there is currently a lack of screenings where (i) the effects of geometrical properties of ENMs on the gene expression are systematically assessed; (ii) the read-across of different species and exposure set ups (in vitro/in vivo) is performed at the transcriptome level; and (iii) the complex networks formed by the genes altered by ENM exposure are taken into account. Despite the high heterogeneity of ENMs3 and the complexity of the molecular responses of exposed cells and tissues in different organisms, a certain degree of generalization can be achieved by the integrated analysis of transcriptome experiments carried out in multiple species. Consequently, the TMOA of specific ENM intrinsic properties can be highlighted by decomposing the complex transcriptomic alterations of

RESULTS AND DISCUSSION The macrophages are the most abundant phagocytic cells resident in the lungs, representing the first sentinels encountering foreign intruders.21 Because of this reason, we exposed macrophage-like cells, derived from the human monocytic THP-1 cell line, to a set of six CNMs of different sizes and geometries (Table 1) for 6 and 24 h, mimicking proinflammatory responses mediated by innate immunity cells. We complemented the above-described experiments with a 4 day in vivo exposure in a murine model, simulating a prolonged inflammation involving also initiation of the adaptive immunity (Figure S1). We highlighted specific response modules extrapolated from coexpression networks of genes specifically altered by relevant CNM intrinsic properties, such as the aspect ratio, its components the length and the diameter considered separately, and the surface area (Table 1). Aspiration of Rigid, Rod-like Multiwalled Carbon Nanotubes (rCNTs) Causes Lung Inflammation. First, we characterized the immunological phenotypes of the exposed mice. To this end, the bronchoalveolar lavage (BAL) and the lung tissues were collected. BAL cell counts evidenced a clear eosinophilic response associated with long and rigid multiwalled carbon nanotubes (rCNTs) after 4 days of aspiration exposure (Mann−Whitney test p-value ≤0.001) and highlighted mild but significant increase in neutrophil and lymphocyte counts (Mann−Whitney test p-value ≤0.001, Figure S2). rCNT is known to cause pulmonary inflammation, granulomas, and fibrosis in mice lungs.11,22,23 In particular, Th2-type eosinophilic response to rCNT has also earlier been observed in vivo24,25 as have cytotoxic effects and inflammasome activation in vitro.26,27 3787


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and mouse transcriptome data on the basis of a set of 3868 highly homologous and orthologous genes between the two genomes. Unsupervised clustering analysis highlighted substantially homogeneous expression patterns across the samples of the in vitro and in vivo experiments, confirming the robustness of our data integration strategy (Figure S5). We could appreciate differences in the impact of CNM intrinsic properties in the two systems (Figure 1). In fact, while the

Exposure to short tangled multiwalled carbon nanotubes (Baytube), instead, caused mildly elevated levels of neutrophils (Mann−Whitney test p-value ≤0.001), whereas exposure to long tangled multiwalled carbon nanotubes (tCNTs) was associated with minute accumulation of neutrophils (p-value ≤0.001) and lymphocytes (Mann−Whitney test p-value ≤0.05, Figure S2). Histological assessment of the lung tissues confirmed clear eosinophilia, granulomatous inflammation, and morphological alteration after exposure to rCNTs, as well as accumulation of macrophages in tCNT-exposed lungs. Baytube caused minor activation of macrophages and a minute amount of eosinophil infiltration, whereas short rigid multiwalled carbon nanotubes (SES), graphite, and fullerene C60 did not cause any visible histological changes in lung tissue (data not shown). Moreover, periodic acid-Shiff (PAS) staining of lung tissues revealed no significant alterations in goblet cell activation nor in the mucus production, yet a clear trend of increased mucus secretion by rCNT exposures could be appreciated (Figure S2). Moreover, we performed histo- and cytopathological examination in order to gain insight into the nano-cell interactions. Our results confirmed that all CNMs were internalized by macrophages in both BAL and tissue (Figure S3a,b). CNM Exposures Trigger Specific Overall Changes in the Transcriptome Both in Vitro and in Vivo. Despite a lack of a clear immune responses observed in the lungs exposed with the majority of the CNMs, we hypothesized that distinct patterns of gene expression alteration are likely to occur in response to CNM exposure both in vitro and in vivo, as already reported.20,28−30 Our observations indeed confirmed the above-mentioned hypothesis, as we could observe general trends of gene modulation, which are, in the in vitro experiment, mainly related to the time of exposure (Figure S3). In addition, tCNT at 24 h and SES at 6 h induce similar expression profiles by clustering together with rCNT after 6 h exposure. Surprisingly SES, a short CNM, generates similar landscapes of gene expression in vitro, together with rCNT and tCNT (Figure S3). These similar patterns could be due to the dynamics of the material uptake, in other words, how the macrophages sense and engulf the particles, as already known.5,31,32 In this respect, rCNT and SES might be capable of causing more rapid changes in signal transduction and, consequently, expression patterns, which become visible already after 6 h of exposure, whereas gene expression patterns after exposure to tCNT show a higher degree of similarity at the later time point (24 h). It should be also noted that the agglomeration state and the dispersions might have an impact on how rapidly the particles sediment and finally come into contact with the cells.33,34 Overall, patterns of gene expression regulation are also distinguishable in vivo, probably driven by size and geometry of the particles. Materials characterized by high surface area, tCNT, and Baytube cluster together. SES and fullerene, characterized by small aspect ratio, exacerbated similar transcriptome outlooks. Interestingly, the transcriptional landscape caused by rCNT exposure is remarkably different from all the other CNM (Figure S3), mirroring our observations of immunological responses at the cellular level (Figure S2). Distinct Gene Sets Are Associated with CNM Intrinsic Properties in Vitro and in Vivo Exposure Scenarios. Next, we systematically studied the association of the gene expression patterns to relevant CNM intrinsic properties both in the in vitro and in vivo data sets. To this end, we integrated the human

Figure 1. Distributions of the correlation scores of gene expression with CNM intrinsic properties. Correlation scores between the expression of human (x-axis) and mouse (y-axis) orthologous genes and CNM intrinsic properties of aspect ratio (a), diameter (b), length (c), and surface area (d). CNM properties of aspect ratio (a) and length (c) have a more pronounced impact on the gene expression in human macrophage-like cells (wider distribution on the x-axis), whereas diameter (b) and surface area (d) are more associated with gene expression in mouse lung tissue (wider distribution on the y-axis).

range of the correlation distributions of the gene expression with the aspect ratio (Figure 1a) and length (Figure 1c) was wider in exposed THP-1 cells, suggesting a stronger impact of these properties in vitro than in vivo, the transcriptome of exposed murine lungs seemed more profoundly affected by the diameter (Figure 1b) and surface area (Figure 1d). We then selected specific sets of genes whose expression patterns correlated to CNM intrinsic properties of interest in each sub-data sets (THP-1 6 h, THP-1 24 h, and murine lung 96 h, Table 2). Additionally, we also analyzed the combined transcriptome data set (comprising THP-1 6 h, THP-1 24 h, and murine lung 96 h normalized together) in search of robust patterns of correlations recapitulating the exposure setups considered separately (Table 2). We observed only marginal overlap between the sets of genes significantly correlated to Table 2. Number of Genes Correlating with CNM properties

THP-1 6 h THP-1 24 h Mm 96 h combined 3788

aspect ratio

diameter

length

surface area

659 564 127 494

95 314 662 337

584 618 168 530

210 52 589 187


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Figure 2. Number of genes significantly correlated with CNM properties and their overlap across the exposure setups. Venn diagrams showing the number of genes correlating to distinct CNM properties aspect ratio (a), diameter (c), length (e), and surface area (g), in each exposure setup (THP-1 6 h, THP-1 24 h, C57BL/6 lung), and in the integrated data set (combined). Pie charts representing the percentage of overlapping genes within the four exposure scenarios for aspect ratio (b), diameter (d), length (f), and surface area (h). Gray area (1) corresponds to genes significantly correlated in one exposure setup; yellow area (2) corresponds to genes significantly correlated in two exposure setups; orange area (3) corresponds to genes significantly correlated in three exposure setups, and red area (4) corresponds to genes significantly correlated in four exposure setups. 3789


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Figure 3. Schematic representation of the computational analysis. Gene expression data are preprocessed to compile matrices with genes in rows (3868 human−mouse orthologue genes) and samples in column (a). The correlation of each gene expression profile with each CNM property is computed in each data matrix, and significantly correlating genes are selected (b). The gene−gene coexpression networks for each analysis are inferred (c). The genes in the network are ranked based on their correlation with the CNM properties and their patterns of connection, and three network response modules containing 5, 10, and 20 top-ranked genes are extrapolated (d). Gene ontology (GO) terms progressively enriched across the response modules are chosen (e).

CNM properties across the subdata sets: 27.2% for aspect ratio (Figure 2a,b), 22.1% for diameter (Figure 2c,d), 28.6% for length (Figure 2e,f), and 17.4% for surface area (Figure 2g,h) shared between at least two data sets. These results suggest that different transcriptional regulatory programs may be active in cells in vitro and tissues in vivo and pose reasonable questions concerning the translatability of in vitro assays into in vivo exposure scenarios. Similar Molecular Functions Are Altered in Response to CNM Intrinsic Properties in Vitro and in Vivo. We did not observe significant commonalities at the level of the individual genes whose expression was significantly altered in response to CNM properties. However, we hypothesized that, when focusing on the biological functions affected by CNM properties in vitro and in vivo, we could highlight shared patterns of gene regulation. In cells, genes do not function independently from each other, but rather, their activities are coordinately regulated in complex molecular networks. For this reason, we systematically inferred the coexpression networks of each set of genes significantly correlated with CNM intrinsic properties, under the guilt-by-association assumption that coexpression underlies co-regulation in the context of relevant cellular and molecular functions. We used a rigorous computational strategy to identify relevant response modules from each inferred network, which are communities of coexpressed genes whose expression is also significantly affected by the intrinsic properties (Figure 3). We hypothesized that these response modules represent relevant aspects of the CNM TMOA mediated by their properties. Next, we studied the patterns of connections of the selected genes and their subsequent topological position within each network. For this, we computed several centrality measures for each gene in each network and combined them with their degree of correlation with each intrinsic property in order to compile a rank of prioritized genes (Table S1). This, in turn, was used to identify key responding genes and their close interactors in the

network, which were together considered as the relevant response modules. Three response modules with different degrees of stringency were selected from each network associated with a specific CNM intrinsic property, considering the top 5, 10, and 20 candidate key responders from each final ranked gene list. When the sets of candidate genes associated with CNM properties were studied at the functional level, many of them were found to be involved in relevant biological processes, corroborating our initial hypothesis that response modules could be exploited in order to determine the molecular responses related to specific CNM properties (Table S2). Strikingly, the number of significantly represented gene ontology (GO) terms between the in vitro and in vivo exposure scenarios remarkably increased as compared to what we observed at the individual gene level (Figure 4a,c,e,g). In fact, we detected 55.2% common GO terms associated with aspect ratio (Figure 4b), 44% associated with diameter (Figure 4d), 58.7% associated with length (Figure 4f), and 32.1% associated with surface area (Figure 4h). These observations suggest that, despite different genomic regulatory programs, cells in vitro and tissues in vivo respond to CNM exposure by altering similar biological functions. Our results also imply that in vitro exposure models are able to recapitulate important events taking place also in vivo, upon appropriate computational analysis aimed at disentangling complex TMOA taking place in different biological systems. Response Modules from THP-1-Exposed Cells Highlight Activation of DNA Repair and Innate Immunity Pathways in Response to Aspect Ratio and Length in a Time-Dependent Manner. Next, we focused on specific patterns of genomic alterations in different exposure setups. Thereafter, we analyzed the genes significantly correlated with CNM properties in all three data sets separately, THP-1 6 h (Figures S6−S9), THP-1 24 h (Figures S10−S13), and mice 96 3790


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Figure 4. Number of gene ontology terms significantly over-represented in network response modules and their overlap across the exposure setups. Venn diagrams indicating the number of significantly over-represented GO terms in network response modules associated with aspect ratio (a), diameter (c), length (e), and surface area (g), in each exposure setup (THP-1 6 h, THP-1 24 h, C57BL/6 lung) and in the integrated data set (combined). Pie charts representing the percentage of overlapping GO terms within the four exposure scenarios for aspect ratio (b), diameter (d), length (f), and surface area (h). Gray area (1) corresponds to GO terms significantly over-represented in one exposure setup; yellow area (2) corresponds to GO terms significantly over-represented in two exposure setups; orange area (3) corresponds to GO terms significantly over-represented in three exposure setups, and red area (4) corresponds to GO terms significantly over-represented in four exposure setups: aspect ratio (a), diameter (b), length (c), and surface area (d). 3791


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ACS Nano h (Figures S14−S17), in search of specific over-represented cellular functions (Table S2). Genes belonging to the “cytokine mediated signaling” and “DNA repair” pathways were represented in the aspect ratio and length-related response modules of THP-1 cells after 6 h exposure (Figures S6 and S8) and were more enriched after 24 h exposure (Figures S10 and S12). The cumulative difference within the two time points might be caused by distinct mechanisms of cellular responses. After 6 h exposure, the immune cells start to sense and engulf the foreign material and consequently signal for recruiting other immune cells. After 24 h exposure, however, material uptake, possible frustrated phagocytosis, cellular translocation, and destabilization of the phagolysosomal process induced by long and high aspect ratio materials may lead to more damage, repair, and inflammation.35 We could observe a similar but reversed pattern with the “innate immunity” related pathways, which were highly activated at 6 h in the aspect ratio and length response modules, but only mildly represented after 24 h exposure. Innate immunity-related signaling is rapidly activated after the cells encounter foreign intruders but attenuates in time, as long and high aspect ratio CNMs exert more stress on macrophages, leading to enhanced damage- and stress-response-associated pathways. These events may thus possibly result in a partial masking of the activation of innate immunity pathways. The genes included in the response modules associated with diameter were strongly representative of the “cellular response to DNA damage stimulus” and “apoptotic signalling pathways” also in a time-dependent manner, by being induced only at 24 h (Figure S11) but not at 6 h. The GO term “signal transduction”, on the other hand, was associated with diameter at 6 h but could not be detected anymore at 24 h in the THP-1 cell response modules. The surface-area-related response module in THP-1 cells exposed for 6 h highlighted functions related to “cell differentiation”, “cell shape regulation”, and “transmembrane transport” (Figure S9). Likewise, after 24 h exposure, several GO terms related to general cell metabolism, such “DNAtemplated transcription” and “signal transduction”, emerge significantly associated with surface area (Figure S13). Similar pathways found associated with all properties in vitro, such as “DNA-templated transcription” and “signal transduction”, were also represented in the response modules in vivo. Moreover, “cell differentiation” was clearly enriched in relation to aspect ratio (Figure S14) but less remarkably also with all the other CNM intrinsic properties. Similarly, the “nervous system development” pathway was affected by length (Figure S16) but not by other properties. As observed in the correlation analysis (Figure 1), the orthologous genes associated with distinct CNM properties in an organism-specific manner. At the pathway level, diameter and surface-area-related genes activated more versatile sets of pathways in vivo, whereas length and aspect ratio evocated stronger transcriptomics responses and pathway alterations in vitro. “Innate immune response” and “DNA repair” pathways, highly activated in THP-1 cells after 24 h in respect to aspect ratio (Figure S10) and length (Figure S12), revealed different trend in mice, being highlighted in response modules associated with diameter (Figure S15) and surface area (Figure S17). This is in concordance with the observations by Kim et al., who appreciated significant inflammatory effects on mouse lungs only after exposure to smaller sized CNMs.20

Since in mice the immune response to CNM is not limited to phagocytosis but involves a wide range of different cells, it is anticipated that differences are found between the in vitro and in vivo setups. We hypothesize that the complex cell repertoire in mice lungs might sense and respond to the short and spherical particles more rapidly and tensely and thus trigger immune responses and damage signaling pathways much more rapidly in comparison to the responses to longer CNMs. Cultured macrophages, on the other hand, could maybe sense more promptly bigger materials, which eventually sediment more quickly in the cell culture dish as compared to smaller floating particles. It should be also noted that different cell types may have different dynamics of activation to different types of materials. For example, Sweeney et al. reported stronger activation of alveolar macrophages to long multiwalled carbon nanotubes (MWCNTs), whereas epithelial cells were more responsive to short MWCNTs.2 Innate Immune Response and Response to Drug Pathways Are Altered in Response to Length and Diameter, Respectively, in the Combined Transcriptome Data Set. Next, we identified the network-derived response modules in the combined transcriptome data set (Figures S18− S21). When we systematically looked for over-representation of biological themes, we could observe a clear enrichment of “DNA repair”, “cell differentiation”, “organelle organization”, and “apoptotic process” pathways in the response module associated with aspect ratio (Figure S18). However, when the two components of the aspect ratio, length and diameter, were considered separately, we found suggestive associations. The length-responding genes, in fact, were found to be involved in pathways related to the “axon guidance” (a subcategory of “cell differentiation”) and “innate immune response” (Figure S20), whereas “response to drug” (a subcategory of “immune response”) and “apoptotic process” were found significantly over-represented in the diameter-associated response module (Figure S19). Surface area, on the other hand, correlated with genes of “small molecule metabolic processes” and “signal transduction” pathways in much greater magnitude than other properties (Figure S21). Candidate key genes with a topological prominent role in the networks were also found in the integrated data set (Table S1). Traf2- and Nck-interacting kinase (TNIK) and neuropilin 1 (NRP1) are both downregulated with aspect ratio, whereas TNIK, linked in protecting cells from outside stimuli,36 is found positively correlated with diameter but not with length. NRP1, on the other hand, is downregulated by length but is not correlated with diameter. Interestingly, the NRP1 cell surface receptor is involved in multiple fundamental cellular signaling cascades.37

CONCLUSIONS We have characterized the transcriptional mechanism of action of several types of CNMs by dissecting the gene expression regulatory circuits altered by their shape properties. We observed little concordance at the level of the individual genes between in vitro and in vivo exposure scenarios. However, the overlap between the transcriptomic responses of cultured cells and animal tissue samples was greatly improved when the data were analyzed on the level of the molecular and cellular functions affected by CNM exposure. We observed significant similarities in the pathways altered both in vitro and in vivo by CNM geometrical properties. Genes involved in innate immune response were correlated with 3792


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800 μL of PBS for 15 s by cannulating the trachea with a blunt syringe immediately after sacrificing and blood collection. BAL fluid was cytocentrifuged onto slides and May Grunwald Giemsa (MGG)stained alveolar macrophages; granulocytes and lymphocytes were counted under a light microscope (Leica DM 400B; Leica, Wetzlar, Germany) with 40× magnification by counting the mean from three high-power fields (HPF) per sample. Nonparametric Mann−Whitney U tests were used for BAL cell counts between two-group analyses. Results were conducted by mean with standard error of the mean. A pvalue 9 were then processed for microarray analysis. The extracted total RNA samples were stored at −70 °C. Transcriptome Assay. After totRNA isolation and purification, T7 amplification method (amino allyl MessageAmp II aRNA amplification kit, Ambion, Carlsbad, CA, USA) was performed to synthesize antisense RNA copies (aRNA) and indirect labeling. aRNA samples were labeled either with monoreactive Cy3, Cy5 (GE Healthcare, Buckinghamshire, UK), or Alexa 488 (Invitrogen, Eugene, Oregon, USA). After the second purification step, labeled aRNA samples were hybridized onto Agilent human GE 4×44K V2 microarray slides (Agilent Technologies, USA). Slides were washed (Agilent gene expression wash buffers 1 and 2) and scanned with GenePix 4200 AL (Molecular Devices, Sunnyvale, CA, USA). Image segmentation was done using GenePixPro 6.1 software (Molecular Devices). Spot intensities were calculated as the median intensity. DNA Microarray Computational Analysis. Data Preprocessing. Data were imported into R software v. 2.340 and analyzed with the BioConductor package limma.41 Briefly, raw data were quality checked, log2 transformed, and quantile normalized. Batch effect removal was performed to eliminate the labeling and slide-specific variance with the Combat algorithm,42 implemented in the sva package.43 Linear model was fitted to the data, and empirical Bayes was used for analyzing the pairwise comparisons of interest. Genes with p < 0.01 and linear FC > |

CNM aspect ratio and length. Likewise, the pathways of DNA damage and repair were found to be significantly altered in association with the diameter, whereas genes involved in signal transduction and cell differentiation were specifically responding to different levels of surface area. Given the widespread use of transcriptomic signatures to characterize the effects of nanomaterials and the effort to reduce the animal experimentation in the toxicology field (3R guidelines), the importance of this study lays in the demonstration that in vitro experiments can significantly anticipate relevant aspects of the ENM−bio interactions in vivo, upon utilization of proper data analysis strategy. Moreover, our analytical strategy, aimed at the identification of network response modules that are not restricted to individual pathways, opens a possibility to pinpoint adverse pathways representing complex biological functions.

MATERIALS AND METHODS Carbon Nanomaterials. The CNMs used in this study were characterized as previously described by Vippola et al. (NANOATLAS)38 and by Palomäki et al.26 CNM providers and characteristics are described in Table 1. Briefly, rod-like multiwalled carbon nanotubes (rCNT; MWCNT-7) were received from Mitsui & Co., Ltd. (Tokyo, Japan); long tangled MWCNTs (tCNT) were purchased from CheapTubes Inc. (Cambridgeport, VT); short purified MWCNTs (SES) were received from SES Research (Texas, USA); short tangled multiwalled carbon nanotubes (Baytubes C150 HP) were obtained from Bayer Material Science (Germany), and fullerene C60 was from Sigma-Aldrich Co. (Missouri, USA). In Vivo Study. Exposure. Female C57BL/6 (7−8 weeks old) purchased from Scanbur A/S (Karslunde, Denmark) were quarantined for 5 days and housed in stainless steel cages in groups of four. Animal facility had a temperature (20−21 °C) and humidity (40−45%) control with 12 h dark/light cycles. Mice were bedded with aspen chip and received food and water ad libitum. Nanomaterial suspensions were prepared in glass tubes, by weighing 1 mg of CNM to 1 mL of PBS (with 0.6 mg of BSA/mL). The stock solutions were vortexed for 1 min and sonicated for 20 min in a bath sonicator (Elmasonic, USA). Dilution of 200 μg of CNM/mL for aspiration exposures was pipetted from the stock solution and further sonicated an additional 20 min before the exposure. Mice were exposed by oropharyngeal aspiration under isoflurane anesthesia to 10 μg of CNM per day in 50 μL of PBS for 4 consecutive days, mimicking a 1 week exposure scenario (Figure S1). BAL and tissue samples were collected 24 h after the final exposure. Dosage was selected according to the previous studies with relatively high and low concentrations (data not shown), resulting in moderate doses of 10 μg per day.39 The study was approved by National Animal Experiment Board from Regional State Administrative Agency of Southern Finland (ESAVI 3241-04.10.07-2013 permission number PH701A). RNA Extraction. After sacrificing and collection of the bronchoalveolar lavage, the lungs were cut, transferred to RNALater (Ambion, Life Technologies, CA, USA), and stored at −80 °C. Total RNA (totRNA) was isolated from the lung samples by phenol/chloroform isolation method. Tissue samples in RNALater were thawed and moved to the lysing matrix tubes (MP Biomedicals, Illkirch, France), containing 1 mL of TRIsure reagent (Bioline Reagents, Ltd., London, UK). Samples were homogenized in a FastPrep FP120 homogenizer (BIO 101, Thermo savant, Waltham, MA, USA), and mRNA was extracted and purified according to the instructions by Bioline Reagents. Quantity and quality of the mRNA were measured with NanoDrop spectrophotometer (ND-1000, Thermo Fisher Scientific Inc., Wilmington, NC, USA) and bioanalyzer (Agilent Technologies, USA). Two to three samples from the same experimental group were pooled, and independent pools of samples with RIN > 8.3 were used for DNA microarray analysis. BAL Cells. Influx of leukocytes into mice lungs was determined by analysis of the bronchoalveolar lavage (BAL). Lungs were flushed with 3793


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ACS Nano 1.5| were considered to be significantly deregulated (Table S3). Microarray data have been deposited in NCBI Expression Omnibus (GEO) database44 and are accessible through GEO series accession number GSE92901 (http://www.ncbi.nlm.nih.gov/geo/). Data Clustering. Clustering of the expression data (Figure S3) for both in vitro and in vivo data sets was computed in R with hierarchical clustering method (hclust) using Euclidean distance measure and complete linkage method. Data Integration. A robust common set of 3868 highly homologous and orthologous genes between the human and mouse genomes was retrieved from the Ensemble database45 and used to merge the in vitro and in vivo data sets. Batch effects caused by species and time of exposure were removed as described above. Correlation with CNM Properties. Correlations between each gene expression profile and the CNM intrinsic properties aspect ratio, length, diameter, and surface area were computed by Pearson, Spearman, and Kendall correlations in each data set analyzed. A score comprising the three correlation estimators and their p-values was computed and used to select the 5% most associated genes to each CNM property (Figure 1). Network Inference and Response Modules. Gene coexpression networks were inferred for each set of genes significantly correlated with a CNM property in each data set separately by using the R package MiNET46 (Figure 3). MiNET allows one to choose distinct inference algorithms, entropy estimators, and methods for discretization, returning a network as weighted adjacency matrix. The higher the weight, the more likely the gene−gene interaction exists. Combined information from all available inference algorithms was used in order to obtain the most robust network inference.47 This combination happens in two steps: first step requires computing a group of inference networks for each inference algorithm by combining the selected inference algorithm with each entropy estimator and each discretization method. The group of inference networks are reduced to a single median network by taking a median of weights for gene pairs across all the networks in the group, so that at the end of step 1, four median networks are computed, one for each inference algorithm. In step 2, a list of edges ranked by their weights from each median network is created, and these ranked edge lists are then reduced to a single median ranked list by using the voting-based scheme Borda method as implemented in the R package TopKLists.48 The most significant edges are selected by progressively including the edges from the topmost ranked edge and moving down the rank to the next ranked edge. The selection cutoff is the last ranked edge allowing all the genes in the network to be connected by at least one edge. Gene Prioritization and Selection of Response Modules. Network topology-based centrality attributes were used to identify the key genes and their close interactors in each coexpression network. The centrality measures of betweenness, clustering-coefficient, degree, closeness, and eigenvector, along with a correlation score of each gene expression profile to the CNM intrinsic property (aspect ratio, length, diameter, and surface area), were used to create a ranked list of genes. Centrality measure ranking is highlighting the key genes in each transcriptional network, whereas the correlation score is an indicator of each gene having high correlation to the CNM intrinsic property ensuring the ability to retrieve the most important key components of the network, which are significantly affected by the CNM property. A list comprising the most central/relevant genes and their neighbors was compiled by the virtue of their connectivity patterns. The vertices and edges corresponding to the genes in this gene list were extracted as a subnetwork. This subnetwork shows the intrinsic central structure and cluster which grows out from the candidate genes extending to their connected genes, providing a stage for understanding the interplay of these genes in this focused zone. Representation of Biological Themes. Three subnetworks of different sizes were selected, which were seeded with three different sizes of the candidate gene list. The list of genes comprising the three subnetworks was used to perform GO enrichment by Fischer’s exact test. The enriched gene ontology (GO) categories were filtered by selecting those represented by at least three genes and progressively enriched across the subnetworks, from the smallest to the largest. This

ensures that only GO categories that are increasingly represented as the size of the input gene list increases are considered. Significant GO classes were clustered using their semantic similarity by using the R Package GOSemSim.49 The clusters are represented by the GO term with the highest enrichment score. GO biological processes (BP) were chosen for displaying the clustering results, and it shows a two-level hierarchy of GO termsthe cluster representatives are clustered further into a supercluster of related GO terms. The superclusters are displayed as a treemap with different colors, and their size reflects the enrichment score.50

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.6b08650. Figures of the experimental set up; BAL cells counts; microscopic images of the BAL cells and lung tissue samples from CNM-exposed mice; hierarchical clustering of the transcriptome data; heatmap from the expression patterns of the orthologue genes; tileplot of progressively enriched gene ontology biological process terms in aspect ratio THP-1 cells 6 h; tileplot of progressively enriched gene ontology biological process terms in diameter THP-1 cells 6 h; tileplot of progressively enriched gene ontology biological process terms in length THP-1 cells 6 h; tileplot of progressively enriched gene ontology biological process terms in surface area THP-1 cells 6 h; tileplot of progressively enriched gene ontology biological process terms in aspect ratio THP-1 cells 24 h; tileplot of progressively enriched gene ontology biological process terms in diameter THP-1 cells 24 h; tileplot of progressively enriched gene ontology biological process terms in length THP-1 cells 24 h; tileplot of progressively enriched gene ontology biological process terms in surface area THP-1 cells 24 h; tileplot of progressively enriched gene ontology biological process terms in aspect ratio C57BL/6 lung 96 h; tileplot of progressively enriched gene ontology biological process terms in diameter C57BL/6 lung 96 h; tileplot of progressively enriched gene ontology biological process terms in length C57BL/6 lung 96 h; tileplot of progressively enriched gene ontology biological process terms in surface area C57BL/6 lung 96 h; tileplot of progressively enriched gene ontology biological process terms in aspect ratio combined; tileplot of progressively enriched gene ontology biological process terms in diameter combined; tileplot of progressively enriched gene ontology biological process terms in length combined; tileplot of progressively enriched gene ontology biological process terms in surface area combined (PDF) Candidate genes of the networks (XLSX) GO terms (XLSX) Differentially expressed genes (XLSX)

AUTHOR INFORMATION Corresponding Author

*E-mail: dario.greco@helsinki.fi. ORCID

Harri Alenius: 0000-0003-0106-8923 Dario Greco: 0000-0001-9195-9003 Notes

The authors declare no competing financial interest. 3794


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