Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities

The study of the relationship between type 2 diabetes mellitus (T2DM) disease and other pathologies (comorbidities), together with patient age variation, poses a challenge for medical research. There is evidence that patients affected by T2DM are more likely to develop comorbidities as they grow older. Variation of gene expression can be correlated to changes in T2DM comorbidities insurgence and progression. Understanding gene expression changes requires the analysis of large heterogeneous data at different scales as well as the integration of different data sources into network medicine models. Hence, we designed a framework to shed light on uncertainties related to age effects and comorbidity by integrating existing data sources with novel algorithms. The framework is based on integrating and analysing existing data sources under the hypothesis that changes in the basal expression of genes may be responsible for the higher prevalence of comorbidities in older patients. Using the proposed framework, we selected genes related to comorbidities from existing databases, and then analysed their expression with age at the tissues level. We found a set of genes that changes significantly in certain specific tissues over time. We also reconstructed the associated protein interaction networks and the related pathways for each tissue. Using this mechanistic framework, we detected interesting pathways related to T2DM whose genes change their expression with age. We also found many pathways related to insulin regulation and brain activities, which can be used to develop specific therapies. To the best of our knowledge, this is the first study that analyses such genes at the tissue level together with age variations.

Temporal networks in biology and medicine: a survey on models, algorithms, and tools

The use of static graphs for modelling and analysis of biological and biomedical data plays a key role in biomedical research. However, many real-world scenarios present dynamic behaviours resulting in both node and edges modification as well as feature evolution. Consequently, ad-hoc models for capturing these evolutions along the time have been introduced, also referred to as dynamic, temporal, time-varying graphs. Here, we focus on temporal graphs, i.e., graphs whose evolution is represented by a sequence of time-ordered snapshots. Each snapshot represents a graph active in a particular timestamp. We survey temporal graph models and related algorithms, presenting fundamentals aspects and the recent advances. We formally define temporal graphs, focusing on the problem setting and we present their main applications in biology and medicine. We also present temporal graph embedding and the application to recent problems such as epidemic modelling. Finally, we further state some promising research directions in the area. Main results of this study include a systematic review of fundamental temporal network problems and their algorithmic solutions considered in the literature, in particular those having application in computational biology and medicine. We also include the main software developed in this context.

A Novel Algorithm for Local Network Alignment Based on Network Embedding

Networks are widely used in bioinformatics and biomedicine to represent associations across a large class of biological entities. Network alignment refers to the set of approaches that aim to reveal similarities among networks. Local Network Alignment (LNA) algorithms find (relatively small) local regions of similarity between two or more networks. Such algorithms are in general based on a set of seed nodes that are used to build the alignment incrementally. A large fraction of LNA algorithms uses a set of vertices based on context information as seed nodes, even if this may cause a bias or a data-circularity problem. Moreover, using topology information to choose seed nodes improves overall alignment. Finally, similarities among nodes can be identified by network embedding methods (or representation learning). Given there are two networks, we propose to use network embedding to capture structural similarity among nodes, which can also be used to improve LNA effectiveness. We present an algorithm and experimental tests on real and syntactic graph data to find LNAs.

The extra-nuclear interactome of the estrogen receptors: implications for physiological functions

Over the last decades, a great body of evidence has defined a novel view of the cellular mechanism of action of the steroid hormone 17β-estradiol (E2) through its estrogen receptors (i.e., ERα and ERβ). It is now clear that the E2-activated ERs work both as transcription factors and extra-nuclear plasma membrane-localized receptors. The activation of a plethora of signal transduction cascades follows the E2-dependent engagement of plasma membrane-localized ERs and is required for the coordination of gene expression, which ultimately controls the occurrence of the pleiotropic effects of E2. The definition of the molecular mechanisms by which the ERs locate at the cell surface (i.e., palmitoylation and protein association) determined the quest for understanding the specificity of the extra-nuclear E2 signaling. The use of mice models lacking the plasma membrane ERα localization unveiled that the extra-nuclear E2 signaling is operational in vivo but tissue-specific. However, the underlying molecular details for such ERs signaling diversity in the perspective of the E2 physiological functions in the different cellular contexts are still not understood. Therefore, to gain insights into the tissue specificity of the extra-nuclear E2 signaling to physiological functions, here we reviewed the known ERs extra-nuclear interactors and tried to extrapolate from available databases the ERα and ERβ extra-nuclear interactomes. Based on literature data, it is possible to conclude that by specifically binding to extra-nuclear localized proteins in different sub-cellular compartments, the ERs fine-tune their molecular activities. Moreover, we report that the context-dependent diversity of the ERs-mediated extra-nuclear E2 actions can be ascribed to the great flexibility of the physical structures of ERs and the spatial-temporal organization of the logistics of the cells (i.e., the endocytic compartments). Finally, we provide lists of proteins belonging to the potential ERα and ERβ extra-nuclear interactomes and propose that the systematic experimental definition of the ERs extra-nuclear interactomes in different tissues represents the next step for the research in the ERs field. Such characterization will be fundamental for the identification of novel druggable targets for the innovative treatment of ERs-related diseases.

Exploiting the molecular basis of age and gender differences in outcomes of SARS-CoV-2 infections

Motivation: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease, 2019; COVID-19) is associated with adverse outcomes in patients. It has been observed that lethality seems to be related to the age of patients. While ageing has been extensively demonstrated to be accompanied by some modifications at the gene expression level, a possible link with COVID-19 manifestation still need to be investigated at the molecular level. Objectives: This study aims to shed out light on a possible link between the increased COVID-19 lethality and the molecular changes that occur in elderly people. Methods: We considered public datasets of ageing-related genes and their expression at the tissue level. We selected human proteins interacting with viral ones that are known to be related to the ageing process. Finally, we investigated changes in the expression level of coding genes at the tissue, gender and age level. Results: We observed a significant intersection between some SARS-CoV-2 interactors and ageing-related genes, suggesting that those genes are particularly affected by COVID-19 infection. Our analysis evidenced that virus infection particularly involves ageing molecular mechanisms centred around proteins EEF2, NPM1, HMGA1, HMGA2, APEX1, CHEK1, PRKDC, and GPX4. We found that HMGA1 and NPM1 have different expressions in the lung of males, while HMGA1, APEX1, CHEK1, EEF2, and NPM1 present changes in expression in males due to ageing effects. Conclusion: Our study generated a mechanistic framework to clarify the correlation between COVID-19 incidence in elderly patients and molecular mechanisms of ageing. We also provide testable hypotheses for future investigation and pharmacological solutions tailored to specific age ranges.

A Tale of Ice and Fire: The Dual Role for 17β-Estradiol in Balancing DNA Damage and Genome Integrity

17β-estradiol (E2) regulates human physiology both in females and in males. At the same time, E2 acts as a genotoxic substance as it could induce DNA damages, causing the initiation of cellular transformation. Indeed, increased E2 plasma levels are a risk factor for the development of several types of cancers including breast cancer. This paradoxical identity of E2 undermines the foundations of the physiological definition of "hormone" as E2 works both as a homeostatic regulator of body functions and as a genotoxic compound. Here, (i) the molecular circuitries underlying this double face of E2 are reviewed, and (ii) a possible framework to reconcile the intrinsic discrepancies of the E2 function is reported. Indeed, E2 is a regulator of the DNA damage response, which this hormone exploits to calibrate its genotoxicity with its physiological effects. Accordingly, the genes required to maintain genome integrity belong to the E2-controlled cellular signaling network and are essential for the appearance of the E2-induced cellular effects. This concept requires an "upgrade" to the vision of E2 as a "genotoxic hormone", which balances physiological and detrimental pathways to guarantee human body homeostasis. Deregulation of this equilibrium between cellular pathways would determine the E2 pathological effects.

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

MOTIVATION

The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data.

RESULTS

Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions.

CONTACT

hguzzi@unicz.it, sroy01@cus.ac.in.

Splicing factors control triple-negative breast cancer cell mitosis through SUN2 interaction and sororin intron retention

BACKGROUND

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic opportunities. Recently, splicing factors have gained attention as potential targets for cancer treatment. Here we systematically evaluated the role of RNA splicing factors in TNBC cell proliferation.

METHODS

In this study, we performed an RNAi screen targeting 244 individual splicing factors to systematically evaluate their role in TNBC cell proliferation. For top candidates, mechanistic insight was gained using amongst others western blot, PCR, FACS, molecular imaging and cloning. Pulldown followed by mass spectrometry were used to determine protein-protein interactions and patient-derived RNA sequencing data was used relate splicing factor expression levels to proliferation markers.

RESULTS

We identified nine splicing factors, including SNRPD2, SNRPD3 and NHP2L1, of which depletion inhibited proliferation in two TNBC cell lines by deregulation of sister chromatid cohesion (SCC) via increased sororin intron 1 retention and down-regulation of SMC1, MAU2 and ESPL1. Protein-protein interaction analysis of SNRPD2, SNRPD3 and NHP2L1 identified that seven out of the nine identified splicing factors belong to the same spliceosome complex including novel component SUN2 that was also critical for efficient sororin splicing. Finally, sororin transcript levels are highly correlated to various proliferation markers in BC patients.

CONCLUSION

We systematically determined splicing factors that control proliferation of breast cancer cells through a mechanism that involves effective sororin splicing and thereby appropriate sister chromatid cohesion. Moreover, we identified SUN2 as an important new spliceosome complex interacting protein that is critical in this process. We anticipate that deregulating sororin levels through targeting of the relevant splicing factors might be a potential strategy to treat TNBC.

Estrogen Receptors Alpha and Beta in Acute Myeloid Leukemia

Estrogen receptor (ER) signaling has been widely studied in a variety of solid tumors, where the differential expression of ERα and ERβ subtypes can impact prognosis. ER signaling has only recently emerged as a target of interest in acute myeloid leukemia (AML), an aggressive hematological malignancy with sub-optimal therapeutic options and poor clinical outcomes. In a variety of tumors, ERα activation has proliferative effects, while ERβ targeting results in cell senescence or death. Aberrant ER expression and hypermethylation have been characterized in AML, making ER targeting in this disease of great interest. This review describes the expression patterns of ERα and ERβ in AML and discusses the differing signaling pathways associated with each of these receptors. Furthermore, we assess how these signaling pathways can be targeted by various selective estrogen receptor modulators to induce AML cell death. We also provide insight into ER targeting in AML and discuss pending questions that require further study.

Splicing regulatory factors in breast cancer hallmarks and disease progression

By regulating transcript isoform expression levels, alternative splicing provides an additional layer of protein control. Recent studies show evidence that cancer cells use different splicing events to fulfill their requirements in order to develop, progress and metastasize. However, there has been less attention for the role of the complex catalyzing the complicated multistep splicing reaction: the spliceosome. The spliceosome consists of multiple sub-complexes in total comprising 244 proteins or splice factors and 5 associated RNA molecules. Here we discuss the role of splice factors in the oncogenic processes tumors cells need to fulfill their oncogenic properties (the so-called the hallmarks of cancer). Despite the fact that splice factors have been investigated only recently, they seem to play a prominent role in already five hallmarks of cancer: angiogenesis, resisting cell death, sustaining proliferation, deregulating cellular energetics and invasion and metastasis formation by affecting major signaling pathways such as epithelial-to-mesenchymal transition, the Warburg effect, DNA damage response and hormone receptor dependent proliferation. Moreover, we could relate expression of representative genes of four other hallmarks (enabling replicative mortality, genomic instability, avoiding immune destruction and evading growth suppression) to splice factor levels in human breast cancer tumors, suggesting that also these hallmarks could be regulated by splice factors. Since many splice factors are involved in multiple hallmarks of cancer, inhibiting splice factors might provide a new layer of oncogenic control and a powerful method to combat breast cancer progression.

Estrogen-induced miR-196a elevation promotes tumor growth and metastasis via targeting SPRED1 in breast cancer

BACKGROUND

Estrogen plays a critical role in breast cancer (BC) progression through estrogen receptor (ER)-mediated gene regulation. Emerging studies suggest that the malignant progress of BC cells is influenced by the cross talk between microRNAs (miRNAs) and ER-α signaling. However, the mechanism and functional linkage between estrogen and miRNAs remain unclear.

METHODS

The expression levels of miR-196a and SPRED1 in BC were tested by qRT-PCR in 46 paired BC and adjacent tissues and by the GEO datasets. The role of miR-196a in estrogen-induced BC development was examined by CCK-8 assay, wound healing assay, Matrigel invasion assay and tumorigenicity assay in nude mice. The binding site of ER-α in miR-196a promoter region was analyzed by ChIP-seq, ChIP assay and luciferase reporter assay. The potential targets of miR-196a in BC cells were explored using the luciferase reporter assay and western blot analysis, and the correlation between miR-196a and SPRED1 was analyzed by Spearman's correlation analysis in BC specimens and GEO dataset. TCGA BRCA data was used to characterize the ESR1 signatures according to MSigDB gene set.

RESULTS

The expression levels of miR-196a were higher in ER-positive (ER+) breast tumors compared to ER-negative (ER-) tumor tissue samples. Besides, miR-196a was involved in estrogen-induced BC cell proliferation, migration and invasion. Notably, the up-regulation of miR-196a was mediated by a direct interaction with estrogen receptor α (ER-α) but not estrogen receptor β (ER-β) in its promoter region, and miR-196a expression levels were positively correlated to ER-α signature scores. Furthermore, SPRED1 was a new direct target of miR-196a which participated in miR-196a-promoted BC development and was suppressed by ligand-activated ER-α signal pathway. Finally, forced expression of miR-196a induced tumor growth of MCF7 cells, while inhibition of miR-196a significantly suppressed the tumor progress in vivo.

CONCLUSIONS

Overall, the identification of estrogen/miR-196a/SPRED1 cascade will shed light on new molecular mechanism of estrogen signaling in BC development and therapy.

Quantitative mapping of RNA-mediated nuclear estrogen receptor β interactome in human breast cancer cells

The nuclear receptor estrogen receptor 2 (ESR2, ERβ) modulates cancer cell proliferation and tumor growth, exerting an oncosuppressive role in breast cancer (BC). Interaction proteomics by tandem affinity purification coupled to mass spectrometry was previously applied in BC cells to identify proteins acting in concert with ERβ to control key cellular functions, including gene transcription, RNA splicing and post-transcriptional mRNA regulation. These studies revealed an involvement of RNA in ERβ interactome assembly and functions. By applying native protein complex purification followed by nano LC-MS/MS before and after in vitro RNA removal, we generated a large dataset of newly identified nuclear ERβ interactors, including a subset associating with the receptor via RNA bridging. These datasets will be useful to investigate further the role of ERβ, nuclear RNAs and the other proteins identified here in BC and other cell types.

Effects of letrozole on breast cancer micro-metastatic tumor growth in bone and lung in mice inoculated with murine 4T1 cells

Breast cancer (BC) is the leading cancer in women worldwide. Metastasis occurs in stage IV BC with bone and lung being common metastatic sites. Here we evaluate the effects of the aromatase inhibitor letrozole on BC micro-metastatic tumor growth in bone and lung metastasis in intact and ovariectomized (OVX) mice with murine estrogen receptor negative (ER-) BC cells inoculated in tibia. Forty-eight BALB/c mice were randomly assigned to one of four groups: OVX, OVX + Letrozole, Intact, and Intact + Letrozole, and injected with 4T1 cells intra-tibially. Letrozole was subcutaneously injected daily for 23 days at a dose of 1.75 µg/g body weight. Tumor progression was monitored by bioluminescence imaging (BLI). Following necropsy, inoculated tibiae were scanned via µCT and bone response to tumor was scored from 0 (no ectopic mineralization/osteolysis) to 5 (extensive ectopic mineralization/osteolysis). OVX mice had higher tibial pathology scores indicative of more extensive bone destruction than intact mice, irrespective of letrozole treatment. Letrozole decreased serum estradiol levels and reduced lung surface tumor numbers in intact animals. Furthermore, mice receiving letrozole had significantly fewer tumor colonies and fewer proliferative cells in the lung than OVX and intact controls based on H&E and Ki-67 staining, respectively. In conclusion, BC-inoculated OVX animals had higher tibia pathology scores than BC-inoculated intact animals and letrozole reduced BC metastases to lungs. These findings suggest that, by lowering systemic estrogen level and/or by interacting with the host organ, the aromatase inhibitor letrozole has the potential to reduce ER- BC metastasis to lung.

Estrogen receptor alpha and beta in health and disease

Estrogen receptors alpha (ERα) and beta (ERβ) are transcription factors that are involved in the regulation of many complex physiological processes in humans. Abnormal ER signaling leads to development of a variety of diseases, such as cancer, metabolic and cardiovascular disease, neurodegeneration, inflammation, and osteoporosis. This review provides an overview and update on ERα and ERβ in health and disease with focus on their role in cancer and metabolic disease and in the context of recent years' success in providing genome wide data on ER function. Furthermore, potential clinical applications and challenges are also discussed.

Estrogen receptor beta impacts hormone-induced alternative mRNA splicing in breast cancer cells

BACKGROUND

Estrogens play an important role in breast cancer (BC) development and progression; when the two isoforms of the estrogen receptor (ERα and ERβ) are co-expressed each of them mediate specific effects of these hormones in BC cells. ERβ has been suggested to exert an antagonist role toward the oncogenic activities of ERα, and for this reason it is considered an oncosuppressor. As clinical evidence regarding a prognostic role for this receptor subtype in hormone-responsive BC is still limited and conflicting, more knowledge is required on the biological functions of ERβ in cancer cells. We have previously described the ERβ and ERα interactomes from BC cells, identifying specific and distinct patterns of protein interactions for the two receptors. In particular, we identified factors involved in mRNA splicing and maturation as important components of both ERα and ERβ pathways. Guided by these findings, here we performed RNA sequencing to investigate in depth the differences in the early transcriptional events and RNA splicing patterns induced by estradiol in cells expressing ERα alone or ERα and ERβ.

RESULTS

Exon skipping was the most abundant splicing event in the post-transcriptional regulation by estradiol. We identified several splicing events induced by ERα alone and by ERα+ERβ, demonstrating for the first time that ERβ significantly affects estrogen-induced splicing in BC cells, as revealed by modification of a subset of ERα-dependent splicing by ERβ, as well as by the presence of splicing isoforms only in ERβ+cells. In particular, we observed that ERβ+BC cell lines exhibited around 2-fold more splicing events than the ERβ- cells. Interestingly, we identified putative direct targets of ERβ-mediated alternative splicing by correlating the genomic locations of ERβ and ERα binding sites with estradiol-induced differential splicing in the corresponding genes.

CONCLUSIONS

Taken together, these results demonstrate that ERβ significantly affects estrogen-induced early transcription and mRNA splicing in hormone-responsive BC cells, providing novel information on the biological role of ERβ in these tumors.

Proteomics reveals the importance of the dynamic redistribution of the subcellular location of proteins in breast cancer cells

At the molecular level, living cells are enormously complicated complex adaptive systems in which intertwined genomic, transcriptomic, proteomic and metabolic networks all play a crucial role. At the same time, cells are spatially heterogeneous systems in which subcellular compartmentalization of different functions is ubiquitous and requires efficient cross-compartmental communication. Dynamic redistribution of multitudinous proteins to different subcellular locations in response to cellular functional state is increasingly recognized as a crucial characteristic of cellular function that seems to be at least as important as overall changes in protein abundance. Characterization of the subcellular spatial dynamics of protein distribution is a major challenge for proteomics and recent results with MCF7 breast cancer cells suggest that this may be of particular importance for cancer cells.

Post-transcriptional regulation of human breast cancer cell proteome by unliganded estrogen receptor β via microRNAs

Estrogen receptor β (ERβ) is a member of the nuclear receptor family of homeostatic regulators that is frequently lost in breast cancer (BC), where its presence correlates with a better prognosis and a less aggressive clinical outcome of the disease. In contrast to ERα, its closest homolog, ERβ shows significant estrogen-independent activities, including the ability to inhibit cell cycle progression and regulate gene transcription in the absence of the ligand. Investigating the nature and extent of this constitutive activity of ERβ in BC MCF-7 and ZR-75.1 cells by means of microRNA (miRNA) sequencing, we identified 30 miRNAs differentially expressed in ERβ+ versus ERβ- cells in the absence of ligand, including up-regulated oncosuppressor miRs such miR-30a. In addition, a significant fraction of >1,600 unique proteins identified in MCF-7 cells by iTRAQ quantitative proteomics were either increased or decreased by ERβ, revealing regulation of multiple cell pathways by ligand-free receptors. Transcriptome analysis showed that for a large number of proteins regulated by ERβ, the corresponding mRNAs are unaffected, including a large number of putative targets of ERβ-regulated miRNAs, indicating a central role of miRNAs in mediating BC cell proteome regulation by ERβ. Expression of a mimic of miR-30a-5p, a direct target and downstream effector of ERβ in BC, led to the identification of several target transcripts of this miRNA, including 11 encoding proteins whose intracellular concentration was significantly affected by unliganded receptor. These results demonstrate a significant effect of ligand-free ERβ on BC cell functions via modulation of the cell proteome and suggest that miRNA regulation might represent a key event in the control of the biological and clinical phenotype of hormone-responsive BC by this nuclear receptor.

Systematic nucleo-cytoplasmic trafficking of proteins following exposure of MCF7 breast cancer cells to estradiol

We have used a proteomics subcellular spatial razor approach to look at changes in total protein abundance and in protein distribution between the nucleus and cytoplasm following exposure of MCF7 breast cancer cells to estradiol. The dominant response of MCF7 cells to estrogen stimulation involves dynamic changes in protein subcellular spatial distribution rather than changes in total protein abundance. Of the 3604 quantitatively monitored proteins, only about 2% show substantial changes in total abundance (>2-fold), whereas about 20% of the proteins show substantial changes in local abundance and/or redistribution of their subcellular location, with up to 16-fold changes in their local concentration in the nucleus or the cytoplasm. We propose that dynamic redistribution of the subcellular location of multiple proteins in response to stimuli is a fundamental characteristic of cells and suggest that perturbation of cellular spatial control may be an important feature of cancer.

The anti-proliferative effect of 2-[piperidinoethoxyphenyl]-3-[4-hydroxyphenyl]-2H-benzo(b) pyran is potentiated via induction of estrogen receptor beta and p21 in human endometrial adenocarcinoma cells

In an effort to develop novel therapeutic agents for endometrial cancer, benzopyran derivatives synthesized at our institute display significant inhibitory activity on cellular growth in uterine cancer cells. The current study was undertaken to demonstrate and explore the estrogen receptor (ER) subtype mediated mechanism of action of benzopyran derivative 2-[piperidinoethoxyphenyl]-3-[4-hydroxyphenyl]-2H-benzo(b) pyran (K-1) in human endometrial cancer cells. K-1 competitively inhibited the estradiol binding to human ERα and ERβ and showed growth inhibitory activity in human endometrial Ishikawa, HEC1B and primary endometrial adenocarcinoma cells. Transient transactivation assays carried out in COS-1 cells have demonstrated the diminished ERα-ERE mediated- and induced the ERβ-ERE mediated-transactivation triggered by compound. It also induced ER-mediated transactivation of the cyclin-dependent kinase inhibitor (CDKI) p21(WAF-1) in both COS-1 cells and in Ishikawa cells. ERβ inducing effects of compound were blocked by ICI182,780. In endometrial adenocarcinoma cells, it induced ERβ and p21 expression significantly whereas the expression of fos, jun and ERα were significantly reduced. In addition, compound promoted ERα-β heterodimerization as observed in Ishikawa cells. These results demonstrate that the benzopyran compound suppressed the cellular growth via ERβ agonism, induction of p21 and via promoting the ERα-β heterodimerization, in addition to its antagonistic effects exerted on ERα, in human endometrial cancer cells. The study suggests that the dual action of benzopyran molecule may be of significant therapeutic value in ERα/β-positive cases of endometrial cancer.

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data

A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power Tools), (ii) the manual loading of preprocessing libraries, and (iii) the management of intermediate files, such as results and metadata. Micro-Analyzer users can directly manage Affymetrix binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the Micro-Analyzer tool can load the preprocessed data directly into the well-known TM4 platform, extending in such a way also the TM4 capabilities. Consequently, Micro Analyzer offers the following advantages: (i) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or due to the use of old libraries, (ii) it enables the combined and centralized pre-processing of different arrays, (iii) it may enhance the quality of further analysis by storing the workflow, i.e. information about the preprocessing steps, and (iv) finally Micro-Analzyer is freely available as a standalone application at the project web site http://sourceforge.net/projects/microanalyzer/.

Negative regulation of estrogen signaling by ERβ and RIP140 in ovarian cancer cells

In hormone-dependent tissues such as breast and ovary, tumorigenesis is associated with an altered expression ratio between the two estrogen receptor (ER) subtypes. In this study, we investigated the effects of ERβ ectopic expression on 17β-estradiol (E2)-induced transactivation and cell proliferation in ERα-positive BG1 ovarian cancer cells. As expected, ERβ expression strongly decreased the mitogenic effect of E2, significantly reduced E2-dependent transcriptional responses (both on a stably integrated estrogen response element [ERE] reporter gene and on E2-induced mRNAs), and strongly enhanced the formation of ER heterodimers as evidenced by chromatin immunoprecipitation analysis. Inhibition by the ERα-selective ligand propyl pyrazole triol was less marked than with the pan-agonist (E2) or the ERβ-selective (8β-vinyl-estradiol) ligands, indicating that ERβ activation reinforced the inhibitory effects of ERβ. Interestingly, in E2-stimulated BG1 cells, ERβ was more efficient than ERα to regulate the expression of receptor-interacting protein 140 (RIP140), a major ERα transcriptional corepressor. In addition, we found that the RIP140 protein interacted better with ERβ than with ERα (both in vitro and in intact cells by fluorescence cross-correlation spectroscopy). Moreover, RIP140 recruitment on the stably integrated reporter ERE was increased upon ERβ overexpression, and ERβ activity was more sensitive to repression by RIP140. Finally, small interfering RNA-mediated knockdown of RIP140 expression abolished the repressive effect exerted by activated ERβ on the regulation of ERE-controlled transcription by estrogens. Altogether, these data demonstrate the inhibitory effects of ERβ on estrogen signaling in ovarian cancer cells and the key role that RIP140 plays in this phenomenon.

Integrative genomics of gene and metabolic regulation by estrogen receptors α and β, and their coregulators

The closely related transcription factors (TFs), estrogen receptors ERα and ERβ, regulate divergent gene expression programs and proliferative outcomes in breast cancer. Utilizing breast cancer cells with ERα, ERβ, or both receptors as a model system to define the basis for differing response specification by related TFs, we show that these TFs and their key coregulators, SRC3 and RIP140, generate overlapping as well as unique chromatin-binding and transcription-regulating modules. Cistrome and transcriptome analyses and the use of clustering algorithms delineated 11 clusters representing different chromatin-bound receptor and coregulator assemblies that could be functionally associated through enrichment analysis with distinct patterns of gene regulation and preferential coregulator usage, RIP140 with ERβ and SRC3 with ERα. The receptors modified each other's transcriptional effect, and ERβ countered the proliferative drive of ERα through several novel mechanisms associated with specific binding-site clusters. Our findings delineate distinct TF-coregulator assemblies that function as control nodes, specifying precise patterns of gene regulation, proliferation, and metabolism, as exemplified by two of the most important nuclear hormone receptors in human breast cancer.

Molecular mechanisms of selective estrogen receptor modulator activity in human breast cancer cells: identification of novel nuclear cofactors of antiestrogen-ERα complexes by interaction proteomics

Estrogen receptor alpha (ERα) is a ligand-activated transcription factor that controls key cellular pathways via protein-protein interactions involving multiple components of transcriptional coregulator and signal transduction complexes. Natural and synthetic ERα ligands are classified as agonists (17β-estradiol/E(2)), selective estrogen receptor modulators (SERMs: Tamoxifen/Tam and Raloxifene/Ral), and pure antagonists (ICI 182,780-Fulvestrant/ICI), according to the response they elicit in hormone-responsive cells. Crystallographic analyses reveal ligand-dependent ERα conformations, characterized by specific surface docking sites for functional protein-protein interactions, whose identification is needed to understand antiestrogen effects on estrogen target tissues, in particular breast cancer (BC). Tandem affinity purification (TAP) coupled to mass spectrometry was applied here to map nuclear ERα interactomes dependent upon different classes of ligands in hormone-responsive BC cells. Comparative analyses of agonist (E(2))- vs antagonist (Tam, Ral or ICI)-bound ERα interacting proteins reveal significant differences among ER ligands that relate with their biological activity, identifying novel functional partners of antiestrogen-ERα complexes in human BC cell nuclei. In particular, the E(2)-dependent nuclear ERα interactome is different and more complex than those elicited by Tam, Ral, or ICI, which, in turn, are significantly divergent from each other, a result that provides clues to explain the pharmacological specificities of these compounds.

Global analysis of estrogen receptor beta binding to breast cancer cell genome reveals an extensive interplay with estrogen receptor alpha for target gene regulation

Background

Estrogen receptors alpha (ERα) and beta (ERβ) are transcription factors (TFs) that mediate estrogen signaling and define the hormone-responsive phenotype of breast cancer (BC). The two receptors can be found co-expressed and play specific, often opposite, roles, with ERβ being able to modulate the effects of ERα on gene transcription and cell proliferation. ERβ is frequently lost in BC, where its presence generally correlates with a better prognosis of the disease. The identification of the genomic targets of ERβ in hormone-responsive BC cells is thus a critical step to elucidate the roles of this receptor in estrogen signaling and tumor cell biology.

Results

Expression of full-length ERβ in hormone-responsive, ERα-positive MCF-7 cells resulted in a marked reduction in cell proliferation in response to estrogen and marked effects on the cell transcriptome. By ChIP-Seq we identified 9702 ERβ and 6024 ERα binding sites in estrogen-stimulated cells, comprising sites occupied by either ERβ, ERα or both ER subtypes. A search for TF binding matrices revealed that the majority of the binding sites identified comprise one or more Estrogen Response Element and the remaining show binding matrixes for other TFs known to mediate ER interaction with chromatin by tethering, including AP2, E2F and SP1. Of 921 genes differentially regulated by estrogen in ERβ+ vs ERβ- cells, 424 showed one or more ERβ site within 10 kb. These putative primary ERβ target genes control cell proliferation, death, differentiation, motility and adhesion, signal transduction and transcription, key cellular processes that might explain the biological and clinical phenotype of tumors expressing this ER subtype. ERβ binding in close proximity of several miRNA genes and in the mitochondrial genome, suggests the possible involvement of this receptor in small non-coding RNA biogenesis and mitochondrial genome functions.

Conclusions

Results indicate that the vast majority of the genomic targets of ERβ can bind also ERα, suggesting that the overall action of ERβ on the genome of hormone-responsive BC cells depends mainly on the relative concentration of both ERs in the cell.