Binding sites analyser (BiSA): software for genomic binding sites archiving and overlap analysis

ERα/PR crosstalk is altered in the context of the ERα Y537S mutation and contributes to endocrine therapy-resistant tumor proliferation

The constitutively active ESR1 Y537S mutation is associated with endocrine therapy (ET) resistance and progression of metastatic breast cancer through its effects on estrogen receptor (ERα) gene regulatory functions. However, the complex relationship between ERα and the progesterone receptor (PR), known as ERα/PR crosstalk, has yet to be characterized in the context of the ERα Y537S mutation. Using proximity ligation assays, we identify an increased physical interaction of ERα and PR in the context of the ERα Y537S mutation, including in the nucleus where this interaction may translate to altered gene expression. As such, more than 30 genes were differentially expressed in both patient tumor and cell line data (MCF7 and/or T47D cells) in the context of the ERα Y537S mutation compared to ERα WT. Of these, IRS1 stood out as a gene of interest, and ERα and PR occupancy at chromatin binding sites along IRS1 were uniquely altered in the context of ERα Y537S. Furthermore, siRNA knockdown of IRS1 or treatment with the IRS1 inhibitor NT-157 had a significant anti-proliferative effect in ERα Y537S cell lines, implicating IRS1 as a potential therapeutic target for restoring treatment sensitivity to patients with breast cancers harboring ERα Y537S mutations.

Bedshift: perturbation of genomic interval sets

Functional genomics experiments, like ChIP-Seq or ATAC-Seq, produce results that are summarized as a region set. There is no way to objectively evaluate the effectiveness of region set similarity metrics. We present Bedshift, a tool for perturbing BED files by randomly shifting, adding, and dropping regions from a reference file. The perturbed files can be used to benchmark similarity metrics, as well as for other applications. We highlight differences in behavior between metrics, such as that the Jaccard score is most sensitive to added or dropped regions, while coverage score is most sensitive to shifted regions.

MatCol: a tool to measure fluorescence signal colocalisation in biological systems

Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object-based colocalisations in biological systems. Therefore, a novel method is required to automatically identify regions of fluorescent signal in two channels, identify the co-located parts of these regions, and calculate the statistical significance of the colocalisation. We have developed MatCol to address these needs. MatCol can be used to visualise protein and/or DNA colocalisations and fine tune user-defined parameters for the colocalisation analysis, including the application of median or Wiener filtering to improve the signal to noise ratio. Command-line execution allows batch processing of multiple images. Users can also calculate the statistical significance of the observed object colocalisations compared to overlap by random chance using Student's t-test. We validated MatCol in a biological setting. The colocalisations of telomeric DNA and TRF2 protein or TRF2 and PML proteins in >350 nuclei derived from three different cell lines revealed a highly significant correlation between manual and MatCol identification of colocalisations (linear regression R2 = 0.81, P < 0.0001). MatCol has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas.

Measuring the spatial correlations of protein binding sites

MOTIVATION

Understanding the interactions of different DNA binding proteins is a crucial first step toward deciphering gene regulatory mechanism. With advances of high-throughput sequencing technology such as ChIP-seq, the genome-wide binding sites of many proteins have been profiled under different biological contexts. It is of great interest to quantify the spatial correlations of the binding sites, such as their overlaps, to provide information for the interactions of proteins. Analyses of the overlapping patterns of binding sites have been widely performed, mostly based on ad hoc methods. Due to the heterogeneity and the tremendous size of the genome, such methods often lead to biased even erroneous results.

RESULTS

In this work, we discover a Simpson's paradox phenomenon in assessing the genome-wide spatial correlation of protein binding sites. Leveraging information from publicly available data, we propose a testing procedure for evaluating the significance of overlapping from a pair of proteins, which accounts for background artifacts and genome heterogeneity. Real data analyses demonstrate that the proposed method provide more biologically meaningful results.

AVAILABILITY AND IMPLEMENTATION

An R package is available at http://www.sta.cuhk.edu.hk/YWei/ChIPCor.html

CONTACTS

ywei@sta.cuhk.edu.hk or hao.wu@emory.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Benchmarking database performance for genomic data

Genomic regions represent features such as gene annotations, transcription factor binding sites and epigenetic modifications. Performing various genomic operations such as identifying overlapping/non-overlapping regions or nearest gene annotations are common research needs. The data can be saved in a database system for easy management, however, there is no comprehensive database built-in algorithm at present to identify overlapping regions. Therefore I have developed a novel region-mapping (RegMap) SQL-based algorithm to perform genomic operations and have benchmarked the performance of different databases. Benchmarking identified that PostgreSQL extracts overlapping regions much faster than MySQL. Insertion and data uploads in PostgreSQL were also better, although general searching capability of both databases was almost equivalent. In addition, using the algorithm pair-wise, overlaps of >1000 datasets of transcription factor binding sites and histone marks, collected from previous publications, were reported and it was found that HNF4G significantly co-locates with cohesin subunit STAG1 (SA1).Inc.

The unique transcriptional response produced by concurrent estrogen and progesterone treatment in breast cancer cells results in upregulation of growth factor pathways and switching from a Luminal A to a Basal-like subtype

BACKGROUND

In breast cancer, progesterone receptor (PR) positivity or abundance is positively associated with survival and treatment response. It was initially believed that PR was a useful diagnostic marker of estrogen receptor activity, but increasingly PR has been recognised to play an important biological role in breast homeostasis, carcinogenesis and metastasis. Although PR expression is almost exclusively observed in estrogen receptor positive tumors, few studies have investigated the cellular mechanisms of PR action in the context of ongoing estrogen signalling.

METHODS

In this study, we contrast PR function in estrogen pretreated ZR-75-1 breast cancer cells with vehicle treated ZR-75-1 and T-47D breast cancer cells using expression microarrays and chromatin immunoprecipitation-sequencing.

RESULTS

Estrogen cotreatment caused a dramatic increase in the number of genes regulated by progesterone in ZR-75-1 cells. In T-47D cells that have naturally high levels of PR, estrogen and progesterone cotreatment resulted in a reduction in the number of regulated genes in comparison to treatment with either hormone alone. At a genome level, estrogen pretreatment of ZR-75-1 cells led to a 10-fold increase in the number of PR DNA binding sites detected using ChIP-sequencing. Time course assessment of progesterone regulated genes in the context of estrogen pretreatment highlighted a series of important regulatory pathways, including those driven by epithelial growth factor receptor (EGFR). Importantly, progesterone applied to cells pretreated with estradiol resulted in switching of the PAM50-determined intrinsic breast cancer subtype from Luminal A to Basal-like, and increased the Oncotype DX® Unscaled Recurrence Score.

CONCLUSION

Estrogen pretreatment of breast cancer cells increases PR steady state levels, resulting in an unequivocal progesterone response that upregulates key members of growth factor pathways. The transformative changes progesterone exerts on the breast cancer subtype suggest that these subtyping tools should be used with caution in premenopausal women.

Bioinformatic analysis of cis-regulatory interactions between progesterone and estrogen receptors in breast cancer

Chromatin factors interact with each other in a cell and sequence-specific manner in order to regulate transcription and a wealth of publically available datasets exists describing the genomic locations of these interactions. Our recently published BiSA (Binding Sites Analyser) database contains transcription factor binding locations and epigenetic modifications collected from published studies and provides tools to analyse stored and imported data. Using BiSA we investigated the overlapping cis-regulatory role of estrogen receptor alpha (ERα) and progesterone receptor (PR) in the T-47D breast cancer cell line. We found that ERα binding sites overlap with a subset of PR binding sites. To investigate further, we re-analysed raw data to remove any biases introduced by the use of distinct tools in the original publications. We identified 22,152 PR and 18,560 ERα binding sites (<5% false discovery rate) with 4,358 overlapping regions among the two datasets. BiSA statistical analysis revealed a non-significant overall overlap correlation between the two factors, suggesting that ERα and PR are not partner factors and do not require each other for binding to occur. However, Monte Carlo simulation by Binary Interval Search (BITS), Relevant Distance, Absolute Distance, Jaccard and Projection tests by Genometricorr revealed a statistically significant spatial correlation of binding regions on chromosome between the two factors. Motif analysis revealed that the shared binding regions were enriched with binding motifs for ERα, PR and a number of other transcription and pioneer factors. Some of these factors are known to co-locate with ERα and PR binding. Therefore spatially close proximity of ERα binding sites with PR binding sites suggests that ERα and PR, in general function independently at the molecular level, but that their activities converge on a specific subset of transcriptional targets.