Introduction

Persistent organic pollutants (POPs) are synthetic chemical substances that persist in the environment and accumulate in high concentrations in fatty tissues. Throughout the 20th century these chemicals were widely used as pesticides, such as hexachlorobenzene (HCB) and dichlorodiphenyltrichloroethane (DDT), or as industrial chemicals as is the case for polychlorinated biphenyls (PCBs). Because of their resistance to biodegradation and environmental toxicity, these compounds have been banned (HCB and PCBs), or restricted (DDT) by the Stockholm Convention on Persistent Organic Pollutants in 20011. However, due to earlier use and biomagnification of POPs in the food chain, humans are still exposed worldwide mainly as a consequence of dietary intake, and of exposure to air and water pollution2. This is of a major concern as PCBs have been classified as definite, HCB as probable and DDT as possible human carcinogens by the International Agency of Research on Cancer3. Several epidemiological studies suggest that PCB, but also organochlorine pesticides such as DDT and HCB levels measured in peripheral blood, are related to increased risk of multiple types of cancer, including non-Hodgkin lymphoma and breast cancer4,5,6.

Several omics studies have been conducted to better understand the relationship between cancer risks and environmental exposure by investigating the transcriptomics, metabolomics and proteomics responses to exposure to carcinogenic compounds, predominantly in peripheral blood samples. Recently, it has been shown that also the human microRNA (miRNA) machinery is altered in response to environmental carcinogens, interestingly before the onset of cancer7. These small non-coding RNA sequences (~22 nucleotides), regulating gene expression at the posttranscriptional level, are involved in all fundamental processes such as development, growth, differentiation, immune reaction, and adaptation to stress8. This large regulatory potential widely impacts the development and progression of cancer upon exposure-induced modulation of gene expression9. Therefore, these key regulators of disease related molecular mechanisms may have great potential as novel biomarkers of exposure and cancer risks10.

In this study we establish the impact of environmental carcinogens on miRNA and transcriptomic profiles in buffy coats of healthy subjects drawn from the general population. The present study was conducted in the context of the EnviroGenomarkers project11. The samples were selected from –at that time- healthy subjects of the Northern Sweden Health and Disease Study (NSHDS) some of whom eventually developed lymphoma. For the present study we generated the miRNA and transcriptomic expression profiles from buffy coats. The exposure markers of POPs were measured in serum12. We applied a linear model to relate a combination of 6 PCB congeners, HCB and DDE (a breakdown product of DDT) to the miRNA profile which was subsequently integrated with the transcriptome profile. To the best of our knowledge this is the first report on a population-based study showing that the miRNA machinery acts in concert with the transcriptome upon exposure to a combination of environmental carcinogens.

Results

We examined the impact of 6 PCB congeners, HCB and DDE on the blood miRNA machinery of a total of 207 subjects. The individual levels of PCB153, PCB138, PCB156, PCB170, PCB180, HCB and DDE13 highly correlated with the cumulative Z-Score (correlation coefficient (r) between 0.81 and 0.98) (Fig. 1). Only PCB118, a congener that in contrast to the other congeners has a coplanar structure, showed a marginally lower correlation (r = 0.77). The median, mean and range of the individual POP exposure and the cumulative Z-Score for the selected individuals is shown in Table 1.

Figure 1
figure 1

Pearson correlation among the eight POPs and the Z-Score. The individual levels of PCB153, PCB138, PCB156, PCB170, PCB180, HCB and DDE highly correlated with the cumulative Z-Score (r between 0.81 and 0.98). Only PCB118 showed a marginally lower correlation (r = 0.77).

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Table 1 Median, mean and range of the exposure to the individual POPS and the cumulative Z-Score.
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Regulation of miRNA expression by POPs

The linear model identified 93 out of the 543 observed miRNA expressions to be significantly associated with the cumulative Z-Score of POP exposure (FDR < 0.05). Of these miRNAs 53 were positively and 40 negatively correlated with the cumulative Z-Score (Fig. 2, Supplementary Table S1). To visualize the effect of the POP exposure on the miRNA expression we divided the cohort, based on the quantiles of the Z-Score of POPs’, into low, middle and high exposed subjects (54, 100 and 53 subjects; 1st, 2nd + 3rd, 4th quantile respectively). Figure 3 shows the exposure-related intensity of the top 12 significantly associated miRNAs (ranked by FDR).

Figure 2
figure 2

Volcano plot representing all identified miRNAs. For each miRNA identified in this study the volcano plot shows the fold change (gradient of the association with the cumulative Z-Score) against the –log P value. Statistically significant associated miRNAs are depicted as red dots.

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Figure 3
figure 3

Expression levels of the top 12 associated miRNAs in the defined exposure groups. To visualize the effect of exposure on the miRNAs we divided the cohort, based on the quantiles of the cumulative Z-Score, into low, middle and high exposed subjects (54, 100 and 53 subjects; 1st, 2nd + 3rd, 4th quantile respectively).

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We also applied the linear model to the individual POPs and observed for PCB138 84 associations, PCB153 121 associations, PCB156 32 associations, PCB170 90 associations, PCB180 82 associations and for HCB 67 associations. For PCB118 and DDE, we did not find any associations with a FDR < 0.05. The signature of the Z-Score of POPs included between 70 and 100 percent of the individual POP associations (Supplementary Table S2). Furthermore, we applied the linear model of the cumulative Z-Score also to the stratified data for only males, females, smokers, non-smokers, future lymphoma cases and controls; however, after applying the correction for multiple testing only 32 associations were found for the subset of the non-smokers and 36 associations among the healthy controls. For only males, females, smokers and future cases no association had a FDR < 0.05 (Supplementary Table S3).

Associations between miRNAs and the transcriptome

The experimentally validated microRNA-target interactions database miRTarBase found a total of 7175 target genes for the identified set of miRNAs. Upon calculating the Pearson-correlation of the identified miRNAs with the previously obtained transcriptomics data14 we identified 217 target genes to be inversely correlated with their respective miRNA (r < −0.4 and FDR < 0.05). Using the gene set analysis of ConsensusPathDB we found that 20 KEGG pathways, including pathways in a range of human cancers, were associated with the list of inversely correlated target genes (q-value < 0.05) (Table 2). Furthermore, we observed an overrepresentation of 148 GO terms in the category of biological processes such as Wnt signaling pathway, apoptotic process, and regulation of cell cycle process (Supplementary Table S4).

Table 2 Associated KEGG pathways from inversely correlated target gene expressions. Cancer related pathways are marked in bold.
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We have also observed 253 positive correlations of a miRNA with its target gene (r > 0.4 and FDR < 0.05). For the positively correlated genes we found 6 KEGG pathways, including small cell lung cancer, p53 signaling pathway and chronic myeloid leukemia, to be enriched (q-value < 0.05) (Table 3). Four out of these 6 also appeared among the pathways retrieved from inversely correlated genes. For the positively correlated genes we identified 57 overrepresented GO terms that included biological processes relevant to carcinogenesis (Supplementary Table S5). To validate the associations of the inversely and positively correlated genes with an independent tool, we also performed a gene set analysis using the Molecular Signatures Database15. Indeed, we observed significant overlap with the hallmark and oncogenic gene sets from the Molecular Signatures Database (FDR < 0.05) (Supplementary Tables S6 and S7).

Table 3 Associated KEGG pathways from positively correlated target gene expressions. Cancer related pathways are marked in bold.
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Potential interference of miRNAs in human cancer

Figure 4 presents the potential interference of the POP exposure-associated miRNAs with the inversely regulated gene targets that were derived from the cancer-related KEGG pathways (Table 2). The eight upregulated miRNAs (green) target 13 inversely regulated genes associated with cancer. The miRNA miR-29a plays a major role in this network as it inversely correlates with eight cancer-related gene targets. Furthermore, we detected nine downregulated miRNAs (red) inversely correlating with 11 cancer-associated genes (Fig. 4).

Figure 4
figure 4

Potential interference of miRNAs with human cancer genes upon environmental exposure to PCBs, HCB and DDE. Red color indicates a downregulation and green color an upregulation of a miRNA with the exposure intensity. Blue color indicates cancer related genes derived from the KEGG pathways. All interactions are inverse correlations (r < −0.4 and FDR < 0.05).

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Discussion

In this study we investigated the impact of exposure to carcinogenic POPs on the miRNA and transcriptomic profiles in buffy coats of healthy subjects. We applied a linear model to relate a cumulative Z-Score of 6 PCB congeners, HCB and DDE (a breakdown product of DDT) to the miRNA profile which was subsequently integrated with the transcriptomic profile. Our study revealed a total of 93 miRNAs (53 positively and 40 negatively) to be significantly correlated with the exposure intensity (Z-Score) (Table 1). The top 4 positively correlated miRNAs, ranked by FDR (miR-193a-3p, miR-152, miR-31-5p and miR-34a-5p), have been described as tumor suppressor miRNAs16,17,18,19. We applied the same linear model to the individual POPs to see if the Z-Score of POPs represents the effects of all individual compounds. The analyses revealed that between 70 and 100 percent of the individual associations were also represented by the cumulative Z-Score of POPs (Supplementary Table S2).

Upon integration of the miRNA response with the transcriptomic profile we identified 217 significantly inverse regulated miRNA-gene pairs. The conducted gene-overrepresentation analysis of the inversely regulated genes revealed that mainly cancer-associated pathways, such as small cell lung cancer or chronic myeloid leukemia, or signaling pathways, like the wnt signaling pathway, were affected. Interestingly, also the thyroid hormone signaling and thyroid cancer pathways were affected (Table 2). The thyroid hormones are among the main suspects for human effects of POP exposure20.

Though unexpected, we have also observed 253 significantly positively correlated miRNA-gene pairs. The gene-overrepresentation analysis of the positively correlated genes revealed that mainly cancer-associated pathways, including chronic myeloid leukemia, or signaling pathways such as the p53 signaling pathway, were associated (Table 3). This finding is in contradiction to the generally expected repressive effect of miRNAs on target expression. Nevertheless, positive correlations have also been described in earlier studies on miRNA gene interactions, and thus the function of miRNAs may not be only repressive21. Since miRNAs are interwoven into complex regulatory networks, a suppression of a signaling mediator can lead to the transcription of a target gene, and consequently, result in a positive correlation22. Further studies are needed to unravel these complex interactions to provide a better understanding of the signaling networks involved. As these intermediates are unknown or have not been measured in this study, we focused mainly on the inverse correlations.

Among the downregulated miRNAs, miR-484 showed the most inverse regulations with genes derived from the KEGG pathways. The downregulation of miR-484 appeared to activate the expression and translation of the target oncogene ABL1 and the Heat Shock Proteins HSP90AA1 and HSP90AB1. The Abelson tyrosine kinases of the family ABL have been identified as key drivers of leukemia in humans. Activation of the proto-oncogene ABL1, a gene involved in signaling pathways that control cell growth and survival, and heat shock proteins of the HSP90 family have been reported in many common cancer types23, 24.

We also found an increased expression in TBL1XR1, a gene playing an important role in the development of B-cell non-Hodgkin lymphomas25, in concordance with the downregulation of miR-501-3p and miR-502-3p. Furthermore, both miRNAs miR-320 and miR-486 have been reported to be downregulated in many types of cancer26, 27. In this study we observed miR-320 and 486-5p to be downregulated with the exposure and both activating the expression and translation of the forkhead box transcription Factor FOXP1. Increased abundance of FOXP1 is known to enhance wnt signaling and is a predictor of poor prognosis and resistance to therapy in diffuse large B cell lymphoma28.

These interactions of miRNAs and oncogenes seem to promote the expression and possibly translation of genes involved in the hallmarks of cancer. However, next to these cancer risk increasing interactions our results have also shown miRNA-mediated repression of cancer related genes and therefore protection from carcinogenesis.

The upregulated miRNA miR-29a, a well-known tumor suppressor miRNA, had the highest number of significantly inverse correlations among the cancer-related gene targets29. In the present study this miRNA appears to repress a total of 8 gene targets, including the lymphoma-related genes CCND1, BCL2, IGF1 and VEGFA. The protein encoded by CCND1 (also known as BCL1) is a regulator of the cell cycle progression and plays an import role in cancer development. A recent study has shown that knocking down CCND1 resulted in cell cycle arrest and induction of apoptosis30. Upregulation of the anti-apoptotic regulator BCL2 was found in non-Hodgkin lymphoma and small cell lung cancer31, 32. Inactivation of BCL2 is known to induce apoptosis and protects from cancer progression33. The Insulin-like growth factor IGF1 is involved in cell proliferation, differentiation and apoptosis. High levels of IGF1 have been found in several common cancers34. The vascular endothelial growth factor VEGFA induces angiogenesis by stimulating cell survival and proliferation. In cancer cells this gene promotes the formation of aggressive tumors35.

Furthermore, we observed an inverse correlation between the upregulated tumor suppressor miR-31-5p and its suppressed target, the proto-oncogene SRC. Elevations of the protein encoded by SRC have been described to induce cellular transformation, tumorigenicity, tumor progression, and metastasis36.

Altered expressions of miR-21 have been found in all common types of cancer and it has therefore been classified as an oncomir. Previous studies have shown that miR-21 plays a major role in the genesis of lymphoma37. In this study we identified both, miR-21-5p and miR-21-3p, to be upregulated with the intensity of exposure. The miRNA miR-21-5p showed a repressive effect on the anti-apoptotic BCL2, but also on the proto-oncogenes TIAM1 (T-cell lymphoma invasion and metastasis 1) and MYC. Previous studies have reported that TIAM1 modulates a number of cellular processes associated with tumor progression and overexpression of this gene has been found in various tumor types38.

The proto-oncogene MYC is strongly associated with lymphomas and adverse clinical outcomes related to B-cell malignancies. It is known to be the most commonly overexpressed oncogene in cancer and a robust prognostic marker for B-cell lymphomas. Recent evidence showed that there is significant crosstalk between MYC and miRNAs, with MYC also controlling the expression of a group of miRNAs. Repression of miRNAs by MYC, including tumor suppressor miRNAs like miR-29 and miR-34a, has been shown to contribute to cellular survival by activating anti-apoptotic proteins such as CCND1 and BCL2. But also MYC-induced activation of miRNAs has been shown to promote cell cycle progression39. Our data showed a significant inverse correlation for the increased miR-21-5p and decreased MYC. However, In addition to the repressive effect of miR-21-5p on MYC, we observed five decreased miRNAs (let-7b-5p, miR-92a, miR-320b, miR-324-3p and miR-423-5p) to exhibit significant positive correlations with MYC. The roles of these miRNAs in carcinogenesis have not yet been fully understood. Nevertheless, these five decreased miRNAs could be suggested to have a potential stimulating role as regulators in the MYC-driven lymphomagenesis.

A recently published study demonstrated a signature of 128 miRNAs as potential novel diagnostic markers for B-cell lymphomas40. The herein presented exposure signature of 93 miRNAs showed an overlap of 28 (30%) miRNAs with the diagnostic lymphoma signature (including the tumor suppressor miRNAs mir-193, miR-152 and miR-34a). This overlap of miRNAs suggests similar pathways to be affected by lymphoma and the POP exposure. To assess whether the future lymphoma cases had an enrichment of risk-increasing miRNAs over the healthy controls, we performed a stratified analysis on only the future lymphoma cases and the healthy controls separately (Supplementary Table S3). The 91 associations of the future cases showed 31 (34%) miRNAs and the 128 associations of the healthy controls showed 54 (42%) miRNAs overlapping with the lymphoma signature. According to this overlap we did not see an overrepresentation of risk-increasing associations among the future lymphoma cases. A similar pattern was observed for the non-smokers showing 162 and the smoker with only 60 associations. The lower number of associations among the future cases and smokers might be a result of a higher biological variability with respect to miRNA expression among the future cases. Possibly, an early cancer and smoking increases the miRNA variability.

To our knowledge we have demonstrated the first evidence of alternations in the miRNA machinery upon environmental exposure to POPs in a population-based study. Unlike conventional approaches in cancer risk assessment we have shown that these miRNAs respond to a mixture of environmental carcinogens. Furthermore, we have shown that the interplay of the affected miRNA profile with the transcriptome involves genes essential for carcinogenesis. The miRNA and transcriptomic response to environmental carcinogens revealed that mechanisms are activated towards processes that possibly increase the risk of carcinogenesis, but also may be protective. Therefore, the observed miRNAs can be seen as key regulators of health and disease and have great potential to contribute to biomarker-based environmental health risk assessment.

Methods

Selection of the population

The Northern Sweden Health and Disease Study (NSHDS) comprises of 94,630 sampling occasions from 74,690 unique individuals. Within the EnviroGenomarkers project archived blood samples and exposure/health data were derived from -at that time- healthy subjects, including 229 future cases of B-cell lymphoma and 327 controls of the prospective NSHDS. No subject was diagnosed with lymphoma within less than two years of blood sample collection. Cases were matched to healthy controls by gender, age (+/− 2.5years), hospital and date of blood collection (+/− 6 months)11. For this study we randomly selected 226 buffy coat samples for integrated miRNA and transcriptome analysis (Table 4, Supplementary Figure S1). To determine the statistical power of the sample size we conducted a power calculation for microarray experiments using the R package “ssize.fdr”41. Accordingly, a minimum of 190 subjects were required to achieve 80% power. For the 207 subjects that were analyzed in our study we calculated 84% power at the 5% significance level (FDR corrected).The EnviroGenomarkers project and its associated studies and protocols were approved by the Regional Ethical Review Board of the Umea Division of Medical Research and all participants gave written informed consent. This study was conducted in accordance with the approved guidelines and regulations.

Table 4 Study population data.
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Internal exposure assessment

Serum concentrations of 6 PCB congeners (PCB118, PCB153, PCB138, PCB156, PCB170 and PCB180), DDE and HCB were determined by means of a Agilent 6890 gas chromatographer connected to a Waters Autospec Ultima high resolution mass spectrometer as described in an earlier study on the exposure data12, 13. Per subject we calculated a Z-Score (POPs) as a representative of the internal exposure to the mixture of POPs. The Z-Score (POPs) was defined as the sum of the Z-Scores for each compound: Z-Score (compound)  = (X − μ)/σ (where X represents the value of the subject, μ the mean and σ the standard deviation of the population)42. Therefore, Z-Score (POPs)  = Z-Score (PCB118)  + Z-Score (PCB153)  + Z-Score (PCB138)  + Z-Score (PCB156)  + Z-Score (PCB170)  + Z-Score (PCB180)  + Z-Score (HCB)  + Z-Score (DDE) .

Analytical procedures

Total RNA extraction from buffy coats, analysis of miRNA (Agilent 8 × 60K human miRNA microarray) and transcriptome profiling (Agilent 4 × 44K human whole genome microarray), and the corresponding data quality assessment and preprocessing were performed as described in an earlier publication14. These analyses provided expression data for 547 miRNAs and 15,805 genes. Due to insufficient quality and missing values in exposure/health data 19 subjects were excluded leaving 207 subjects for the data analysis.

Statistical analysis

The miRNA and transcriptomic data was analyzed using the open-source software R (version 3.1.1) and Bioconductor43. The miRNA and transcriptomic raw signals were corrected for hybridization batch-effect as well as white blood cell counts (CD4, CD8, NK, B cells, monocytes, granulocytes) using ComBat (sva package version 3.18.0)44. The cell counts were derived from methylation data as described earlier14. We used a linear model provided by the R package limma (version 3.26.9) to determine miRNAs significantly associated with the exposure intensity (cumulative Z-Score) as described in the limma manual45. Within the analysis we additionally adjusted for the confounding variables sex, age, smoking status and future disease (future case or control). MicroRNAs were considered to be significantly associated with the Z-Score at a false discovery rate (FDR) below five percent46.

Data integration and pathway analysis

From the experimentally validated microRNA-target interaction database miRTarBase (release 6) all gene-targets per exposure associated miRNA were retrieved47. A matrix for each miRNA consisting of the microarray signals of that particular miRNA and the signals of all its gene-targets was generated. Upon calculating the Pearson correlation using R package stats (version 3.2.2), all significantly correlating targets were selected for subsequent pathway analysis (r < −0.4 or > 0.4, FDR < 0.05). Pathway and Gene Ontology analyses of the target genes were performed by over-representation analysis in ConsensuthPathDB (Release 31)48 and gene set analysis using the Molecular Signatures Database (version 6.0)15. All genes linked to the KEGG pathways related to cancer were exported to Cytoscape (version 3.4.0) and visualized with their miRNA interactions.