Identification of Gene Transcription Start Sites and Enhancers

Identification of Gene Transcription Start Sites and Enhancers Responding to Pulmonary Carbon Nanotube Exposure in Vivo Jette Bornholdt,*,∞,†,‡ Anne Thoustrup Saber,∞,§ Berit Lilje,†,‡,¶ Mette Boyd,†,‡ Mette Jørgensen,†,‡ Yun Chen,†,‡ Morana Vitezic,†,‡ Nicklas Raun Jacobsen,§ Sarah Søs Poulsen,§ Trine Berthing,§ Simon Bressendorff,† Kristoffer Vitting-Seerup,†,‡ Robin Andersson,† Karin Sørig Hougaard,§ Carole L. Yauk,∥ Sabina Halappanavar,∥ Håkan Wallin,§,⊥ Ulla Vogel,§,# and Albin Sandelin*,†,‡ †

The Bioinformatics Centre, Department of Biology University of Copenhagen, 2200 Copenhagen, Denmark Biotech Research and Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark § National Research Centre for the Working Environment, 2100 Copenhagen, Denmark ∥ Environmental and Radiation Health Sciences Directorate, Health Canada, Ottawa, Ontario K1A 0K9, Canada ⊥ Department of Public Health, University of Copenhagen, 2200 Copenhagen, Denmark # Department of Micro and Nanotechnology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark ‡

S Supporting Information *

ABSTRACT: Increased use of nanomaterials in industry, medicine, and consumer products has raised concerns over their toxicity. To ensure safe use of nanomaterials, understanding their biological effects at the molecular level is crucial. In particular, the regulatory mechanisms responsible for the cascade of genes activated by nanomaterial exposure are not well-characterized. To this end, we profiled the genome-wide usage of gene transcription start sites and linked active enhancer regions in lungs of C57BL/6 mice 24 h after intratracheal instillation of a single dose of the multiwalled carbon nanotube (MWCNT) Mitsui-7. Our results revealed a massive gene regulatory response, where expression of key inflammatory genes (e.g., Csf 3, Il24, and Fgf 23) was increased >100-fold 24 h after Mitsui-7 exposure. Many of the Mitsui-7-responsive transcription start sites were alternative transcription start sites for known genes, and the number of alternative transcription start sites used in a given gene was correlated with overall Mitsui-7 response. Strikingly, genes that were up-regulated after Mitsui-7 exposure only through their main annotated transcription start site were linked to inflammatory and defense responses, while genes up-regulated only through alternative transcription start sites were functionally heterogeneous and not inflammation-associated. Furthermore, we identified almost 12 000 active enhancers, many of which were Mitsui-7-responsive, and we identified similarly responding putative target genes. Overall, our study provides the location and activity of Mitsui-7-induced enhancers and transcription start sites, providing a useful resource for targeted experiments elucidating the biological effects of nanomaterials and the identification of biomarkers for early detection of MWCNT-induced inflammation. KEYWORDS: multiwalled carbon nanotubes, CAGE, TSS, enhancer, inflammation, Mitsui-7, MWCNT-7 potential negative impact on human health.3,4 Therefore, improved understanding of the biological effects of nanomaterial exposure is important for proper hazard assessment. In this context, recent reports have shown that a commonly used engineered nanomaterial, multiwalled carbon nanotubes

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key area within nanotechnology is the development and application of engineered nanomaterials, characterized as having one or more external dimensions between 1 and 100 nm for at least 50% of the particles.1 Such materials often have distinct and desirable properties and are increasingly used in consumer products and medical applications, such as composite material enforcement, in vivo imaging, and drug delivery.2 Despite the many benefits of using engineered nanomaterials, their physicochemical properties have led to concerns as to their © 2017 American Chemical Society

Received: November 8, 2016 Accepted: March 27, 2017 Published: March 27, 2017 3597

DOI: 10.1021/acsnano.6b07533 ACS Nano 2017, 11, 3597−3613

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Figure 1. Mitsui-7 characteristics and its distribution in mouse lung. (A) Transmission electron microscopy image of Mitsui-7 fibers. (B) Mitsui-7 distribution in mouse lung 24 h after pulmonary exposure, assessed by optical microscopy. Mitsui-7 particles (black) are found in bronchioles (arrow heads; also see panels C,D for zoomed-in views), alveolar walls (see panels E−G for zoomed-in views), and macrophages in the alveolar lumen (see panels H,I for zoomed-in views). (C) Zoomed-in view of Mitsui-7 in bronchioles. (D) Same as in panel C, but using dark-field microscopy. Mitsui-7 appears as white agglomerate structures. (E) Zoomed-in view of Mitsui-7 in alveolar walls. (F) Extended zoomed-in view of Mitsui-7 in alveolar walls from panel E. (G) Same as in panel F, but using dark-field microscopy. Mitsui-7 appears as white dispersed fibers. (H) Zoomed-in view of Mitsui-7 alveolar lumen. (I) Extended zoomed-in view of Mitsui-7 alveolar lumen from panel H.

(MWCNTs), share many properties with asbestos fibers,5 a known human lung carcinogen. Indeed, lung exposure to MWCNTs in rodents is associated with acute and long-lasting pulmonary inflammation, fibrosis and formation of granulomas, and lung cancer risk, similar to the effects of asbestos fiber exposure.4,6−11 Humans are typically exposed to MWCNTs through the respiratory tract, by inhalation of aerosolized nanotubes. Inhalation studies have shown that MWCNTs reach the alveolar regions of the lungs, where 84% of the total lung burden is deposited.12 In addition, half-lives of MWCNT clearance have been reported to be up to a year.9 Long-term exposure of rodents to the MWCNT Mitsui-7 caused mesotheliomas in a dose-dependent manner after intraperitoneal injections13,14 and lung cancer after inhalation exposure.15 Mitsui-7 was recently classified as possibly carcinogenic to humans (Group 2B) by the International Agency for Research on Cancer.16 To understand the mechanisms underlying the toxic effects of inhaled carbon nanotubes at the molecular level, it is necessary to go beyond conventional toxicity assays to investigate genome-

wide responses. To this end, several in vitro studies have investigated the transcriptomic response following MWCNT exposure using DNA microarray and qPCR analysis after both acute11,17−22 and (sub)chronic exposures.17,23 These studies demonstrate that genes involved in cell proliferation, DNA repair, and apoptosis are dys-regulated after MWCNT exposure. Recent in vivo MWCNT exposure studies show dose-dependent effects on the expression of genes associated with inflammation, acute phase response, immune cell trafficking, decreased mitochondrial membrane potential, lung hypersensitivity, lung fibrosis, and the hematological system.10,11,19,24−28 DNA microarrays can only measure changes in expression of genes by predefined probes present on the array and can therefore not detect non-annotated transcripts or distinguish between isoforms of genes. In humans, most genes use at least two alternative isoforms: ∼95% of genes with at least three exons are alternatively spliced.29 Isoforms from the same gene may result in transcripts or proteins with substantial functional differences because a functional protein-coding sequence may be absent from the isoform.30,31 Even though a few RNA3598


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Figure 2. Inflammatory gene response following Mitsui-7 exposure. (A) Overview of highest changing genes following Mitsui-7 exposure. The Xaxis shows the average Mitsui-7 vs control fold change or vice versa (after pseudocount addition). The Y-axis ranks the TSSs (indicated by their mm9 locations) based on fold change. If the TSS was linked to a gene, its name is indicated. The left panel shows the top 20 most up-regulated TSSs following Mitsui-7 exposure, sorted by fold change. The right panel shows the top 20 most down-regulated TSSs. Green and pink colors indicate up- and down-regulation, respectively. (B) Overview of the 10 most significantly over-represented “Biological Process” gene ontology terms for genes up-regulated after Mitsui-7 exposure. Gene ontology over-representation FDR values are shown in parentheses. Gene ontology terms marked in black are among the top 10 most significant gene ontology terms identified. Gene ontology terms marked in gray are shown to infer the relationship between the top 10 gene ontology terms. (C) Overview of the most significantly over-represented KEGG pathways in the up-regulated gene set vs differentially expressed genes associated with each pathway. KEGG pathways are listed along the X-axis, and genes that were differentially expressed are on the Y-axis. Black color denotes the presence of the gene in a given pathway. (D) Subset of genes in the KEGG pathway “cytokine−cytokine receptor interaction” (see Supplementary Figure 2 for the whole pathway). Green boxes indicate genes significantly up-regulated after Mitsui-7 exposure.

In addition to alternative splicing, most genes can initiate transcription from different regions. Such regions are typically referred to as alternative transcription start sites (TSSs). Around 60% of genes have at least two alternative TSSs,35 producing partially different transcripts. Compared to alternative splicing,

sequencing studies have been conducted in the context of nanotube exposure,32−34 none have analyzed isoform usage through differential splicing, even though this is possible with this technique. 3599


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Mitsui-7 Distribution in Lungs 24 h After Exposure. Mitsui-7 nanotubes were visualized in lungs 24 h after exposure using optical microscopy. Mitsui-7 nanotubes were observed in the bronchioles (arrow heads in Figure 1B; also see zoomed-in images in C,D), at the alveolar walls (Figure 1E−G), and in macrophages in the alveolar lumen (Figure 1H,I). Enhanced dark-field microscopy showed Mitsui-7 as agglomerate structures and dispersed fibers (Figure 1D,G). Lung Gene Expression Response to Mitsui-7 Exposure. Female mice were exposed to 162 μg of Mitsui-7 or vehicle (referred to as “control”) by intratracheal installation and sacrificed after 24 h. The dose reflects the pulmonary deposition in mice corresponding to 9 working days (40 h/week) at the current Danish occupational exposure limit for Printex90 carbon black particles (3.5 mg/m3) assuming a ventilation rate of 1.8 L/ h and 33% pulmonary deposition.50 Carbon black exposure is used as a reference model for MWCNTs as no regulatory or legal binding occupational exposure limit exists for CNTs. Our dose level is comparable to that of several other installation/aspiration studies25,51−53 although at the high end. To compare our Mitsui-7 dose with the ones used in in vitro experiments, we calculated the surface area of both exposures. The in vivo 162 μg dose corresponded to a surface area dose of 1.98 μg/cm2. Concentrations of 0.25−2 μg/cm2 in a recent in vitro study were considered a biological relevant dose.22 Thus, our selected dose is in the biologically relevant range for the study of Mitsui-7 response in vitro although at the high end. From the isolated lung tissue, we prepared CAGE libraries, averaging ∼17 × 106 sequencing reads per library. Sequencing reads, corresponding to the first 30 nucleotides of cDNAs, were mapped to the mouse genome (the mm9 reference assembly), and proximal sequencing reads on the same strand were grouped into 554.351 tag clusters as described previously.38 For simplicity, we will refer to these tag clusters as “TSSs”. To limit our set to strong TSSs, we required a mean expression of ≥3 tags per million mapped reads (TPM) across at least one condition, reducing the data set to 17 241 TSSs. Differential expression analysis identified 613 TSSs with significant expression change between conditions (FDR < 0.01 and ≥2-fold expression change in either direction). As we wanted to focus on the major TSSs in a given gene, we discarded 115 TSSs that were minor alternative TSSs (contributing to less than 10% of total CAGE signal for the gene in question). This resulted in 334 up-regulated and 164 down-regulated TSSs after Mitsui-7 exposure. Overlapping these TSSs with annotated gene models identified 257 up- and 112 down-regulated genes (Supplementary Table 1). We first focused on understanding the effects of Mitsui-7 at the gene expression level. In general, the magnitude of response was much higher for up-regulated genes than for down-regulated genes (Figure 2A shows the top responding TSSs in either direction, and Supplementary Table 1 shows all differentially expressed TSSs): the six most up-regulated TSSs had fold changes >100, whereas only one gene was down-regulated by >30-fold. For example, the TSS of Saa3, a major acute phase reactant,54 increased expression by 105-fold from 11 to 1125 TPM. A closer look at the differentially expressed genes revealed several transcription factors: five up-regulated (Cebpd, Fosl1, Nf il3, Tead3, and Hif 3a) and five down-regulated (Ank1, Smyd1, Fhl2, Tcf12, and Asb2). Changes in expression levels of these transcription factors may explain part of the massive changes in regulation following Mitsui-7 exposure. Overall, the up-regulated genes were highly enriched in gene ontology terms associated with immune response, agreeing with

the usage of alternative TSSs as a means to generate isoforms is underappreciated,35−37 even though shifts in TSS usage have been observed between different cell types, during differentiation, and development as well as in specific contexts (e.g., during inflammation).35,38,39 As with splicing, alternative isoforms caused by usage of alternative TSSs can result in loss of a functionally important protein sequence, such as exons encoding protein domains.30,36 The identification of TSSs is also highly useful for the study of regulatory mechanisms of genes, as the DNA region around it binds regulatory proteins and interacts with distal enhancer elements. State of the art methods for determining TSSs include cap analysis of gene expression (CAGE)40 and oligo-capping.41 These methods are based on the generation of full-length cDNAs, followed by high-throughput sequencing of their 5′ ends. CAGE has recently been used to produce an atlas of TSS locations and their usage across the whole body in humans and mice.39,42 Because of the high sensitivity of the CAGE method, it has also been used to characterize non-mRNA transcription initiation events, including transcription of noncoding RNAs.42−46 As discovered by Kim et al.,47 active enhancer regions have the ability to initiate transcription of a class of noncoding RNAs called enhancer RNAs, at both sides of the enhancer. We have previously shown that CAGE can be used for detection of the initiation of enhancer RNAs and thereby enhancers.39,43 Enhancers predicted by CAGE are 2−3 times more likely to be validated in in vitro assays than nontranscribed enhancer regions predicted by chromatin methods.43 Confirming this, enhancer RNA expression correctly predicted tissue-specific in vivo enhancer activity for murine developmental enhancers48 and similarly primary blood cell enhancer usage.43 Thus, enhancer RNAs are powerful proxies for enhancer activity, which can be detected by the CAGE technique. To our knowledge, no study has analyzed the effects of nanomaterials in vivo on TSSs or how distal enhancers regulate these TSSs. Here, we used the CAGE technique to characterize TSSs and enhancer changes in mouse lungs following intratracheal instillation of the MWCNT Mitsui-7. We found that Mitsui-7 exposure resulted in a dramatic transcriptional response 24 h after exposure, with more than 600 differentially expressed TSSs, including known alternative TSSs and TSSs of non-annotated transcripts. We also found a large number of differentially expressed enhancers, several of which were correlated with similarly responding TSSs. We showed that the Mitsui-7 upregulated genes can be functionally divided depending on the type of TSSs that was up-regulated: genes up-regulated only by annotated main TSSs were typically part of the inflammatory cascade, whereas genes up-regulated exclusively by alternative TSSs were not.

RESULTS AND DISCUSSION Characterization of the Mitsui-7 Multiwalled Carbon Nanotubes. We used the nanomaterial MWCNT-XNRI-7 (also called Mitsui-7 or MWCNT-7) obtained as a kind gift from the Mitsui Company. Mitsui-7 appears as long and straight fibers with some agglomeration (Figure 1A). For intratracheal instillation, Mitsui-7 was dispersed in 10% bronchoalveolar fluid and 0.9% NaCl. The peak size was approximately 5000 nm with a slightly skewed distribution. We previously characterized Mitsui-7 using multiple methods:49 the results are summarized in Methods. 3600


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Figure 3. TSS localization in respect to annotated genes. (A) Schematic representation of the four different types of TSS annotations used, based on RefSeq gene models: canonical, alternative, non-annotated intragenic, and non-annotated intergenic, exemplified by TSSs at the Ned4 locus. Classification rules are shown for each respective case. (B) Histogram showing the number of CAGE-defined TSSs passing the expression and isoform sorting thresholds that map to respective annotation classes shown in panel A. (C) Same as in panel B, but TSSs are split by type of differential expression (X-axis). The percentage of TSSs falling within each annotated group is shown on the Y-axis. TSS counts are shown at the top of each bar.

a previous microarray study19 (Supplementary Figure 1). Specifically, biological process gene ontology terms linked to stress, cell migration, and immune system processes were overrepresented, as were cytokine activity and extracellular space (Figure 2B). In agreement with this finding, genes within KEGG55 immune response associated pathways were overrepresented, in particular, the cytokine−cytokine receptor interaction, chemokine signaling, TNF signaling, and the Jak STAT signaling pathways (Figure 2C). Notably, several chemokines in the same subfamily were up-regulated, and these were often targeting the same receptor, which in turn was often up-regulated (Figure 2D; the whole pathway is shown in Supplementary Figure 2A). The up-regulation of selected cytokines was also measured at the protein level (Supplementary Figures 3 and 4). Airway exposure to Mitsui-7 is accompanied by an influx of immune cells into lung tissue.19 This influx could contribute to the measured expression response. Although such events would not invalidate our findings, we investigated if this was a likely explanation for our observations. To this end, we compared control and Mitsui-7-treated lung sample CAGE expression

values to 13 relevant tissue and cell types including adult mouse lung, macrophages (unstimulated and stimulated by various agents), granulocytes, T-and B-cells, and tracheal epithelial cells from the FANTOM5 project39,42,56 (Supplementary Figure 5A and Table 2). Interestingly, our control and Mitsui-7-treated samples correlated more strongly with each other than with any FANTOM5 tissues and cell types including activated immune cells. The only exception to this was the FANTOM5 adult mouse lung CAGE library. We also tried to model the expression of control or Mitsui-7-treated samples based on FANTOM5 samples using linear models to see which FANTOM5 samples would explain most of the signal (see Methods and Supplementary Figure 5B). As expected, normal lung tissue from FANTOM5 contributed the most (a relative contribution of ∼50−70%), followed by tracheal epithelial cells (∼10−20%). More importantly, the relative contribution from primary immune cells was considerably smaller (typically around 5%) and did not change between controls and Mitsui-7 treatment. Thus, it seems likely that the majority of observed changes cannot be explained by the influx of immune cells. 3601


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Figure 4. Examples of differentially expressed TSSs. Each panel describes a genome locus and consists of three subpanels. Top: genome browser overview of the gene landscape around the TSS(s) of interest including gene annotation (if present) and positioning of qPCR primers. Middle: average CAGE TPM intensity on each genomic strand in Mitsui-7-treated and control mice. Numbers on Y scale axes indicate TPM scales. Bottom: dot plots showing gene expression measured by CAGE (TPM) for the highlighted regions and qPCR normalized to β-actin (Actb) expression for individual mice. Horizontal lines in the dot plots show the mean. See main text for details on respective locus. (A) Canonical TSS for Csf 3. qPCR validations for this TSS are shown. (B) Non-annotated intergenic TSS not associated with known genes. qPCR validations for this TSS are shown. (C) Non-annotated alternative TSS for Sbno2. qPCR validations for main and alternative TSSs are shown. (D) Known alternative TSS for Usp2. qPCR validations for main and alternative TSSs are shown. (E) Non-annotated intragenic TSS for Med24. Mitsui-7 dose−response qPCR validations for main and alternative TSSs are shown.

TSS-Specific Mitsui-7 Response in Lungs. An advantage of the CAGE technique is that the location of individual TSSs can be identified with high precision, thereby facilitating categorization of the TSSs. TSSs exceeding the expression threshold (but not necessarily differentially expressed) was separated into four categories based on their overlap with annotated gene models: canonical, known alternative, non-annotated intragenic,

and non-annotated intergenic CAGE-defined TSSs (see Figure 3A for an illustration of the classification definitions). Specifically, for a given gene, a CAGE-defined TSS was labeled “canonical” if it was located within 100 bp of the most upstream-annotated TSS from the RefSeq gene model database.57 CAGE-defined TSSs that overlapped other annotated TSS within a given gene were labeled “alternative known TSSs”, whereas CAGE-defined TSSs 3602


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Figure 5. Genes using solely alternative or canonical TSSs contribute in distinct ways to Mitsui-7 response. (A) Box plots showing the distribution of CAGE expression fold change (Mitsui-7 vs control) for genes with one TSS, two TSSs, or at least three TSSs. For genes with multiple TSSs, the mean log fold change was used. Only TSSs with a positive fold changes were considered in the analysis. P values refer to two-tailed Mann− Whitney U tests between indicated distributions. (B) Top panel: Significantly over-represented gene ontology terms for up-regulated genes where only the canonical TSS is up-regulated. X-axis shows over-representation FDR values, log-scaled. Y-axis show top five gene ontology terms, sorted by significance. Bottom panel: String database83 protein interaction plots between these genes. (C) Same as in panel A, but analyzing upregulated genes where only annotated or non-annotated alternative TSSs are up-regulated. Only two gene ontology terms are significant. (D) Transcription factor binding site over-representation analysis on promoters for respective TSS set, focused on the −300 to +100 bp around CAGE summits.

TSSs, and non-intergenic TSSs (5267/15 625) (Figure 3B). A subset of each category was differentially expressed after Mitsui-7 exposure (Figure 3C). We discuss each category below together with specific examples, which were validated by qPCR analysis. Canonical TSSs. Of the detected canonical TSSs, 2.2% were differentially expressed (up- or down-regulated). These included some of the largest changes observed, exemplified by the Csf 3

overlapping the gene model that were not annotated TSS were labeled “non-annotated alternative TSSs”. CAGE-defined TSSs outside of gene boundaries were labeled non-annotated intergenic TSSs. Two-thirds of CAGE-defined TSSs (10 358/15 625 or 66%) were annotated as canonical, whereas the remaining third included known alternative TSSs, non-annotated intragenic 3603


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ACS Nano (colony stimulating factor 3) gene, encoding a key cytokine.58 Transcription from the canonical TSS of Csf 3 increased 479-fold after Mitsui-7 exposure. The high expression change was confirmed by qPCR analysis (342-fold) (Figure 4A). Additional examples of up-regulated canonical TSSs for Cxcl2, Cxcl3, Il24, and Atp6v1b1 genes, together with qPCR validation, are shown in Supplementary Figure 6. Non-annotated Intergenic TSSs. Among the differentially expressed TSSs, 100 were classified as non-annotated intergenic TSSs (Figure 3C), corresponding to 6% of all non-annotated intergenic TSSs. For example, we identified a TSS likely corresponding to the TSS of a spliced, uncharacterized long noncoding RNA (ensemble transcript ENSMUST0000014649), whose closest annotated RefSeq gene, Ptpn1, was >100 kb distal. This TSS increased 7-fold in expression following Mitsui-7 exposure. The increased expression was verified by qPCR (6.5fold change increase) (Figure 4B). Non-annotated intragenic TSSs. A total of 2884 nonannotated intragenic TSS candidates were identified of which 152 (5%) were differentially expressed. One such example was located in the Sbno2 gene, which is reported to repress NFKBactivated transcriptional activity and thereby participate in the anti-inflammatory pathway of IL-10 response.59 The nonannotated TSS within the Sbno2 loci was located in the fourth intron leading to exclusion of the upstream coding exons. The expression of this variant was 4-fold increased after Mitsui-7 exposure (Figure 4C), as also confirmed by qPCR (3.7-fold). Annotated Alternative TSSs. We detected 727 known alternative TSSs. Of these, 19 (3%) were up-regulated and 5 (0.7%) were down-regulated. The Usp2 and Med24 genes exemplified up-regulated alternative TSSs after Mitsui-7 exposure. In Usp2, an alternative TSS was up-regulated 3-fold by CAGE and 5-fold by qPCR (Figure 4D). Usp2 is a wellstudied ubiquitin-specific protease required for tumor necrosis factor alpha (TNF)-induced NFKB signaling as well as deubiquitination of target proteins like fatty acid synthase (Fasn), murine double minute 2 and 4 (Mdm2, Mdm4), and cyclin D1 (Ccnd1).60,61 Mdm2 and Mdm4 are negative regulators of p53 (Trp53), and Ccnd1 is required for cell cycle G1/S transition.62 Importantly, the alternative TSS likely corresponded to the previously described Usp2-45 isoform.61 This protein isoform lacked the first 217 amino acids including a region binding Mdm4 but maintained a second Mdm4 binding site and the catalytic site downstream of the TSS.63 The Mitsui-7induced truncated version of Usp2 with potentially lower binding affinity for Mdm4 could thus potentially function as a dominant negative isoform causing a decrease in cell cycle progression. Such Mitsui-7-responding TSSs were observed multiple times: 75 RefSeq genes in our data set used RefSeq-annotated alternative TSSs that were both differentially expressed and localized downstream of protein-coding exons in respective genes (Supplementary Table S3). Similarly, we found a highly up-regulated (32-fold by CAGE, 31-fold by qPCR) alternative TSS upstream of exons 20 in the Med24 gene, a part of the Mediator complex that promotes initiation of RNA polymerase II to begin transcription. The subunit composition of the Mediator complex can differ between cells and can change its function (reviewed in ref 64). While no RefSeq database gene models matched this TSS, an ENSEMBL database65 transcript model (ENSMUST00000139849.1) was present. Through computational analysis, we predicted the transcript to be protein-coding, but it only included one nuclear receptor recognition motif (LXXLL), compared to six LXXLL

motifs in the full-length transcript using the canonical TSS. It is thus likely that the shortened, Mitsui-7 up-regulated Med24 transcript will produce a protein than is functionally different from the protein encoded by the full-length transcript. This is interesting since Med24 is an important protein in the initiation of all mRNA transcription start sites. qPCR analysis showed that the alternative TSS expression followed the Mitsui-7 concentration and could be detected at concentrations lower than that used in the CAGE experiments (Figure 4E; Mitsui-7 dose− response experiments for all previously discussed alternative TSSs in Figure 4 are shown in Supplementary Figure 7). Low Similarity between the Expression Patterns in Vivo and in Vitro. Functional characterization can be greatly facilitated by in vitro cell models. To see if the alternative TSSs induced by Mitsui-7 in Figure 4 responded similarly in an in vitro model, we measured their expression by qPCR as a function of Mitsui-7 concentration in FE-1 murine lung epithelial cells. In agreement with our previous study,19 the in vitro response was not similar to the in vivo response (Supplementary Figure 7). Differential Use of TSS Types by Inflammatory and Noninflammatory Genes Induced by Mitsui-7. Changes in alternative TSS usage may lead to truncated mRNAs and proteins, which could potentially cause functional changes as exemplified by the Usp2 gene. However, alternative TSS usage may also have regulatory effects. As described above, a substantial fraction of genes used more than one TSS (14%, 1699/12 124 genes). By analyzing the TSSs for the differentially expressed genes with more than one active TSS, we made several observations. First, only a handful of genes (Hif 3a, Serpina3n, Retnla, Ampd3, Doc2b, Fst, Slc39a14, Syt12, Cd33, and Ccr1) had both up-regulated canonical and alternative TSSs. Second, if multiple differentially expressed TSSs occurred within the same gene, they responded in the same direction (Frmd5 was the only exception with two alternative TSSs responding in opposite directions). We therefore hypothesized that multiple TSSs in a gene could enable enhanced upregulation to Mitsui-7 stimuli. Indeed, the number of CAGEdetected TSSs within each gene was positively correlated with the increase in overall gene expression response to Mitsui-7 exposure (Figure 5A). Third, up-regulated genes using only a differentially expressed canonical TSS (141 genes) were linked to biological roles different than those of up-regulated genes, which did not have differentially expressed canonical TSS but had one or more upregulated alternative TSSs (106 genes). Up-regulated genes using exclusively canonical TSSs were highly over-represented in gene ontology terms related to bacterial defense and immunological response, including cytokine-mediated responses, and were generally highly interconnected based on data from the literature (Figure 5B). Conversely, genes where only noncanonical TSS(s) were up-regulated had few overrepresented gene ontology terms and limited interconnectivity, implying diverse but nonimmunological roles (Figure 5C). One of the few over-represented gene ontology terms for this group (FDR = 0.05) was “positive regulation of cell cycle”, linked to eight distinct genes (Sphk1, Usp2, Fntb, Rad51b, Lmnb1, Fosl1, Cdkn1a, and Camk2b). DNA motif over-representation analysis (Figure 5D) showed little similarity between promoters linked to the two classes of TSSs; in particular, the NFKB and CEBP motifs, associated with inflammatory response,66,67 were highly enriched in promoters of the canonical versus alternative TSS sets defined above. 3604


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Figure 6. Characteristics of predicted enhancer regions. (A) Average footprint plot showing typical enhancer profiles around identified enhancer midpoint. The X-axis shows genomic distance in base pairs centered on the CAGE-defined enhancer midpoints. The top panel shows the average DNA binding intensity of monomethylated lysine 4 in histone 3 (H3K4me1; top panel, light gray line), and RNA polymerase 2 (RNAPII; top panel, black line), measured by chromatin immunoprecipitation followed by sequencing in adult mouse lung.68 The accessibility of DNA, as measured by DNase sensitivity assays in adult mouse lung,68 is also shown (top panel, gray area, right Y-axis). Signal intensity was measured in non-overlapping 201 bp bins. Bottom panel is similar to the top panel but shows stranded CAGE signals (bottom panel, gray = forward and black = reverse strand). Dotted lines delineate the maxima in each CAGE distribution. (B) Similar to panel A, but showing the average evolutionary conservation across placental mammals using the PhyloP method88 in non-overlapping 601 bp wide bins, for significantly differentially expressed enhancers (gray area) and remaining enhancers (black line).

observed in the FANTOM5 set.39 The number of differentially expressed enhancers was a conservative estimate because enhancer transcription levels are substantially lower than gene transcription levels as measured by CAGE and RNA sequencing,43 making it harder to reach statistical significance. As with TSSs, the majority of the differentially expressed enhancers (238/292, 82%) were up-regulated, whereas 54 were down-regulated. We have previously shown that enhancer−TSS interactions can be predicted by CAGE coexpression across samples, assessed by pairwise correlation tests between the CAGE expression in TSSs versus enhancers.43 Using a conservative approach where we only analyzed TSSs and enhancers that were differentially expressed, we identified 99 pairs of enhancers and TSSs (involving 70 TSSs and 69 enhancers) where the TSS−enhancer distance was 0 per library. The data were produced in two different rounds, and a batch effect was observed (Supplementary Figure 8A). The effect was removed using ComBat from the sva Bioconductor package (sva version 2.12.0)79 (Supplementary Figure 8B). The limma package80 (version 3.22.1) was then used for calculating differential expression (Supplementary Figure 8C). P values were corrected for multiple testing using the Benjamini and Hochberg method. For significance calls, we required an FDR threshold of 1. For analyses focused on alternative TSSs, in order to analyze only strong transcriptional events, we discarded minor alternative TSSs that had a contribution of ≤10% of the total CAGE signal for the gene in question. Labeling of TSSs with Respect to Gene Annotation and Gene Ontology Analysis. CAGE-defined TSSs were overlapped with RefSeq genes57 (mm9, downloaded from the UCSC browser81 18.08.2014). RefSeq gene bodies were defined as the region from the start of the most upstream RefSeq transcript to the end of the most downstream RefSeq transcript with the same gene symbol. RefSeq transcripts with same gene symbol, but where the transcripts were not overlapping, were removed. To identify alternative upstream TSSs, an additional 500 bp was added upstream of each gene. For identification of transcription factors, we used the nonredundant mouse transcription factor list from RIKEN transcription factor database.82 Gene ontology analysis was done using the gProfilerR R package (version 0.5). All genes with at least one TSS that passed the three TPM thresholds after isoform sorting were used as background, mouse was chosen as the organism, and FDR-based correction of multiple testing was used. As in the ENSEMBL annotation pipeline,65 the canonical start site was defined as the most upstream annotated start site in the gene model. CAGEdefined TSSs within 100 bp from such annotated TSS were labeled as the canonical TSSs. CAGE-defined TSSs within 100 bp from all other annotated TSS were annotated as known alternative TSS. CAGEdefined TSSs within gene bodies, but >100 bp from an annotated TSS, were defined as non-annotated intragenic TSSs, and finally, all remaining CAGE-defined TSSs (not overlapping annotated genes) were annotated as non-annotated intergenic TSSs (Figure 3A). Protein interactions were visualized using the String database.83 The perl script findMotifsGenome.pl from the Homer suite84 was used for the motif searches. We analyzed the region 300 bp upstream of each TSS to 100 bp downstream. As the background, all nondifferentially expressed TSSs were used, and repeats were masked. Otherwise, default settings were used. Comparison of CAGE and Microarray Data. Microarray data were downloaded from Supplementary Table 1 from ref 19. Only microarray data for the 162 μg dose 24 h after instillation were used for comparison because this was the same dose level as tested by CAGE analysis in the present paper. Genes where at least one of the probes had an FDR < 0.01 and fold change >|1| (the same criteria as for the CAGE data) were classified as differentially expressed. For comparison of gene ontology terms between the microarray and CAGE, gene ontology terms for each group were found independently using mouse as the organism, FDR for multiple testing correction, and no background. The two gene ontology term sets were subsequently overlapped. Validation of TSS Usage by Quantitative PCR. Total RNA (extracted as described above) was reverse transcribed into cDNA using SuperScript II (Invitrogen) with an input of 1.2−1.5 μg total RNA per 20 μL of reaction volume. Quantitative real-time PCR (qPCR) was performed on an Applied Biosystems QuantStudio 6 Flex real-time PCR system (384-well plate) using SYBR Select Master Mix (Life Technologies). All samples were run in triplicates in 5 μL reaction volumes. All primers were designed to amplify regions downstream of the CAGE signal and overlapping an exon/exon junction if possible (Supplementary Table 6). Primers were purchased from Integrated

DNA Technologies (Leuven, Belgium) and used at a concentration of 0.5 μM. All PCRs were run under the following conditions: 50 °C for 2 min, 95 °C for 2 min, 45 cycles of 95 °C for 15 s, 58 °C for 1 min. The average standard deviation between triplicates was on average 0.13 with a maximum of 0.5. In the in vitro experiment, a standard variation of 1 was allowed for the CQ values between triplicates for the “med24_alt” target due its low expression. All results were normalized to the housekeeping genes β-actin (Actb1) or Rplp0 (data not shown); expression levels for these genes were very similar across samples with an average of 13.6 and an average standard deviation of 0.082 between triplicates (max of 0.5) for Actb1. Cytokine Protein Expression Quantification by Cytokine Arrays. Due to limited availability of tissue, cytokine protein abundance was measured on three control mice and five mice exposed to 162 μg of Mitsui-7 for 24 h. Protein lysates were prepared by homogenizing 10− 15 mg of frozen lung tissue in 700 μL of ice-cold PBS added 7 μL of protease inhibitor cocktail (Sigma). The homogenizations were performed using a T10 Ultraturrax (IKA, Germany), with a 15−20 s pulse pattern on slush-ice. After homogenization, 7 μL of Triton-X-100 was added to disrupt cell membranes and release proteins. The lysate was vortexed thoroughly and frozen on dry ice to further disrupt the cells. The lysate was then defrosted on ice and centrifuged at 10 000g for 5 min, before recovering the supernatant. The protein content of the supernatant was then quantified with the Qubit protein assay kit (Thermo Fisher Scientific). For cytokine profiling, the proteome profiler mouse cytokine array panel A (cat ARY006, R&D Systems) was used as recommended by the manufacturer with an input of 300 μg of protein per array. For visualization of the arrays, we used SuperSignal West Femto maximum sensitivity substrate (Thermo Fisher Scientific) and a high-sensitivity Sony A7S digital camera equipped with CaptureOne v.8 for image analysis. Quantification of the cytokines was performed using ImageJ, and normalization between arrays was based on incorporated reference spots. Computational Analysis of the Non-annotated Alternative Transcription Start Site Located in Med24. The ENSEMBL database65 transcript model (ENSMUST00000139849.1) was located in very close proximity to our alternative TSS. To predict the coding potential of the transcript, we used the CPAT tool85 resulting in a predicted coding potential of 0.78, which is substantially higher than suggested cutoff in the mouse (0.44). We identified an open reading frame with 285 nucleotides (95 amino acids). Using the PFAM domain database tools,86 we identified one Med24_N protein domain within the transcript. In the open reading frame, we found one instance of the nuclear receptor recognition motif LXXLL. Enhancer Analysis. Enhancers were detected as described previously.43 Differential expression was assessed as with TSSs, described above, but with a relaxed statistical threshold (P < 0.05 instead of FDR < 0.05), motivated by the lower expression strengths in enhancers versus gene TSSs. Of all differentially expressed enhancers, 175 were within 500 kb from significantly regulated TSSs. Each such TSS−enhancer pair was tested for correlation using a correlation test across all samples. A total of 99 enhancer−TSS pairs was detected, with a positive correlation and FDR < 0.5. Three cases where the enhancer was overlapping a TSS were removed. Motifs in enhancer regions were predicted using Homer84 as described above. Enhancer regions were padded with 300 bp on each side before motif analysis. Analysis of Tissue Expression Distribution. FANTOM libraries from the FANTOM5 project39,42,56 were downloaded from http:// fantom.gsc.riken.jp/5/datafiles/latest/basic/. We chose libraries from 13 different tissues and primary cells, including lung, trachea epithelial cells, B cells, T cells, granulocytes, and macrophages with and without stimulation. Our set of differentially expressed enhancers was overlapped with FANTOM libraries’ CTSS files, giving tag counts per position. The number of tags overlapping each CAGE-defined TSS region was calculated and normalized to the total library size. Library replicates were merged by calculating the average expression across TSSs. Correlations were found using the cor() function in R with default settings, and a correlation plot was made using the Corrplot() function. A relative importance plot was made using all TSSs with three TPM or more and overlapped with FANTOM CTSS files as above. 3609


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ACS Nano Outliers with expression values >2000 TPM were removed. The function calc.relimp() from the relaimpo package87 as used where the lmg metric was used to calculate r2 values and relative importance of each sample.

Sequencing Core Facility in Copenhagen for performing the CAGE sequencing.

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ASSOCIATED CONTENT S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.6b07533. CAGE data are available in the NCBI Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) under accession no. GSE70386 Supplementary Table 1: list of differentially expressed TSSs (tag clusters) (XLSX) Supplementary Table 2: list of FANTOM5 libraries used for the expression modeling (XLSX) Supplementary Table 3: list of differentially expressed alternative TSSs localized downstream of protein-coding exons (XLSX) Supplementary Table 4: list of CAGE-predicted enhancers with differential expression (XLSX) Supplementary Table 5: list of predicted enhancer−TSS interactions (XLSX) Supplementary Table 6: list of primer sequences used for qPCR validation (XLSX) Supplementary text 1, including a description of the analysis of protein levels following Mistsui-7 treatment, supplementary table titles and supplementary figures (PDF)

AUTHOR INFORMATION Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Albin Sandelin: 0000-0002-7109-7378 Present Address ¶

Statens Serum Institut, 2300 Copenhagen, Denmark.

Author Contributions

∞ J.B. and A.T.S. are co-first authors. J.B., A.T.S., M.B., and A.S. designed the study. J.B. and M.B. conducted the CAGE experiments and qPCR validations. A.T.S., N.R.J., K.S.H., U.V., S.S.P., C.L.Y., S.H., J.W., and H.W. designed and conducted the mouse experiments. T.B. was responsible for optical microscopy. J.B. and S.B. performed the cytokine arrays. B.L., J.B., M.J., Y.C., K.V.S., M.V., R.A., and A.S. analyzed the data. J.B. and A.S. wrote the paper with input from all authors.

Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS Grants from the Novo Nordisk Foundations and Lundbeck foundations (to A.S.) supported this work. M.V. was supported by a Marie-Curie fellowship (Grant 659505). The Danish Centre for Nanosafety I (http://nanosafety.dk/, Grant 20110092173-3 from the Danish Working Environment Research Foundation) and The Danish Centre for Nanosafety II supported A.T.S., N.R.J., S.S.P., T.B., K.S.H., H.W., and U.V. We thank C. Købler and K. Mølhave for the TEM picture of Mitsui-7, A. Mortensen for the help interpreting the histology data, C.D. Vaagensoe for technical assistance in the laboratory, and the National DNA 3610


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