DNA methylome-wide alterations associated with estrogen receptor-dependent effects of bisphenols in breast cancer

BACKGROUND

Bisphenol A (BPA), an estrogen-like endocrine disruptor used in plastics, has been associated with development and promotion of breast cancer, so plastic manufacturers shifted towards less-studied analogs, BPF and BPS. Studying the associated DNA methylome-wide mechanisms of these derivatives is timely, particularly in comparison with BPA.

METHODS

We assessed proliferation, cell cycle, and migration of breast cancer cells (estrogen receptor (ER)-positive: MCF-7 and ER-negative: MDA-MB-231) treated with BPF and BPS ± estrogen receptor inhibitor (ERI) in comparison to BPA ± ERI. RNA expression and activity of DNA (de)methylation enzymes and LINE-1 methylation were quantified. DNA methylome-wide analysis was evaluated in bisphenol-exposed cells and compared to clinical breast cancer data.

RESULTS

The three bisphenols caused ER-dependent increased proliferation and migration of MCF-7 but not MDA-MB-231 cells, with BPS being 10 times less potent than BPA and BPF. Although they have similar chemical structures, the three bisphenols induced differential DNA methylation alterations at several genomic clusters of or single CpG sites, with the majority of these being ER-dependent. At equipotent doses, BPA had the strongest effect on the methylome, followed by BPS then BPF. No pathways were enriched for BPF while BPA- and BPS-induced methylome alterations were enriched in focal adhesion, cGMP-PKG, and cancer pathways, which were also dysregulated in methylome-wide alterations comparing ER-positive breast cancer samples to adjacent normal tissues.

CONCLUSIONS

The three bisphenols have important epigenetic effects in breast cell lines, with those of BPA and BPS overlapping with cancer-related pathways in clinical breast cancer models. Hence, further investigation of their safety is warranted.

DNA methylation signal has a major role in the response of human breast cancer cells to the microenvironment

Breast cancer-associated fibroblasts (CAFs) have a crucial role in tumor initiation, metastasis and therapeutic resistance by secreting various growth factors, cytokines, protease and extracellular matrix components. Soluble factors secreted by CAFs are involved in many pathways including inflammation, metabolism, proliferation and epigenetic modulation, suggesting that CAF-dependent reprograming of cancer cells affects a large set of genes. This paracrine signaling has an important role in tumor progression, thus deciphering some of these processes could lead to relevant discoveries with subsequent clinical implications. Here, we investigated the mechanisms underlying the changes in gene expression patterns associated with the cross-talk between breast cancer cells and the stroma. From RNAseq data obtained from breast cancer cell lines grown in presence of CAF-secreted factors, we identified 372 upregulated genes, exhibiting an expression level positively correlated with the stromal content of breast cancer specimens. Furthermore, we observed that gene expression changes were not mediated through significant DNA methylation changes. Nevertheless, CAF-secreted factors but also stromal content of the tumors remarkably activated specific genes characterized by a DNA methylation pattern: hypermethylation at transcription start site and shore regions. Experimental approaches (inhibition of DNA methylation, knockdown of methyl-CpG-binding domain protein 2 and chromatin immunoprecipitation assays) indicated that this set of genes was epigenetically controlled. These data elucidate the importance of epigenetics marks in the cancer cell reprogramming induced by stromal cell and indicated that the interpreters of the DNA methylation signal have a major role in the response of the cancer cells to the microenvironment.

Reference-free transcriptome assembly in non-model animals from next-generation sequencing data

Next-generation sequencing (NGS) technologies offer the opportunity for population genomic study of non-model organisms sampled in the wild. The transcriptome is a convenient and popular target for such purposes. However, designing genetic markers from NGS transcriptome data requires assembling gene-coding sequences out of short reads. This is a complex task owing to gene duplications, genetic polymorphism, alternative splicing and transcription noise. Typical assembling programmes return thousands of predicted contigs, whose connection to the species true gene content is unclear, and from which SNP definition is uneasy. Here, the transcriptomes of five diverse non-model animal species (hare, turtle, ant, oyster and tunicate) were assembled from newly generated 454 and Illumina sequence reads. In two species for which a reference genome is available, a new procedure was introduced to annotate each predicted contig as either a full-length cDNA, fragment, chimera, allele, paralogue, genomic sequence or other, based on the number of, and overlap between, blast hits to the appropriate reference. Analyses showed that (i) the highest quality assemblies are obtained when 454 and Illumina data are combined, (ii) typical de novo assemblies include a majority of irrelevant cDNA predictions and (iii) assemblies can be appropriately cleaned by filtering contigs based on length and coverage. We conclude that robust, reference-free assembly of thousands of genes from transcriptomic NGS data is possible, opening promising perspectives for transcriptome-based population genomics in animals. A Galaxy pipeline implementing our best-performing assembling strategy is provided.

Reference-free transcriptome assembly in non-model animals from next-generation sequencing data

Next-generation sequencing (NGS) technologies offer the opportunity for population genomic study of non-model organisms sampled in the wild. The transcriptome is a convenient and popular target for such purposes. However, designing genetic markers from NGS transcriptome data requires assembling gene-coding sequences out of short reads. This is a complex task owing to gene duplications, genetic polymorphism, alternative splicing and transcription noise. Typical assembling programmes return thousands of predicted contigs, whose connection to the species true gene content is unclear, and from which SNP definition is uneasy. Here, the transcriptomes of five diverse non-model animal species (hare, turtle, ant, oyster and tunicate) were assembled from newly generated 454 and Illumina sequence reads. In two species for which a reference genome is available, a new procedure was introduced to annotate each predicted contig as either a full-length cDNA, fragment, chimera, allele, paralogue, genomic sequence or other, based on the number of, and overlap between, blast hits to the appropriate reference. Analyses showed that (i) the highest quality assemblies are obtained when 454 and Illumina data are combined, (ii) typical de novo assemblies include a majority of irrelevant cDNA predictions and (iii) assemblies can be appropriately cleaned by filtering contigs based on length and coverage. We conclude that robust, reference-free assembly of thousands of genes from transcriptomic NGS data is possible, opening promising perspectives for transcriptome-based population genomics in animals. A Galaxy pipeline implementing our best-performing assembling strategy is provided.