Global signalling network analysis of luminal T47D breast cancer cells in response to progesterone.

Background: Breast cancer cells enter into the cell cycle following progestin exposure by the activation of signalling cascades involving a plethora of enzymes, transcription factors and co-factors that transmit the external signal from the cell membrane to chromatin, ultimately leading to a change of the gene expression program. Although many of the events within the signalling network have been described in isolation, how they globally team up to generate the final cell response is unclear. Methods: In this study we used antibody microarrays and phosphoproteomics to reveal a dynamic global signalling map that reveals new key regulated proteins and phosphor-sites and links between previously known and novel pathways. T47D breast cancer cells were used, and phospho-sites and pathways highlighted were validated using specific antibodies and phenotypic assays. Bioinformatic analysis revealed an enrichment in novel signalling pathways, a coordinated response between cellular compartments and protein complexes. Results: Detailed analysis of the data revealed intriguing changes in protein complexes involved in nuclear structure, epithelial to mesenchyme transition (EMT), cell adhesion, as well as transcription factors previously not associated with breast cancer cell proliferation. Pathway analysis confirmed the key role of the MAPK signalling cascade following progesterone and additional hormone regulated phospho-sites were identified. Full network analysis shows the activation of new signalling pathways previously not associated with progesterone signalling in T47D breast cancer cells such as ERBB and TRK. As different post-translational modifications can mediate complex crosstalk mechanisms and massive PARylation is also rapidly induced by progestins, we provide details of important chromatin regulatory complexes containing both phosphorylated and PARylated proteins. Conclusions: This study contributes an important resource for the scientific community, as it identifies novel players and connections meaningful for breast cancer cell biology and potentially relevant for cancer management.

Plastome genomics in South American maize landraces: chloroplast lineages parallel the geographical structuring of nuclear gene pools.

BACKGROUND AND AIMS: The number of plastome sequences has increased exponentially during the last decade. However, there is still little knowledge of the levels and distribution of intraspecific variation. The aims of this study were to estimate plastome diversity within Zea mays and analyse the distribution of haplotypes in connection with the landrace groups previously delimited for South American maize based on nuclear markers. METHODS: We obtained the complete plastomes of 30 South American maize landraces and three teosintes by means of next-generation sequencing (NGS) and used them in combination with data from public repositories. After quality filtering, the curated data were employed to search for single-nucleotide polymorphisms, indels and chloroplast simple sequence repeats. Exact permutational contingency tests were performed to assess associations between plastome and nuclear variation. Network and Bayesian phylogenetic analyses were used to infer evolutionary relationships among haplotypes. KEY RESULTS: Our analyses identified a total of 124 polymorphic plastome loci, with the intergenic regions psbE-rps18, petN-rpoB, trnL_UAG-ndhF and rpoC2-atpI exhibiting the highest marker densities. Although restricted in number, these markers allowed the discrimination of 27 haplotypes in a total of 51 Zea mays individuals. Andean and lowland South American landraces differed significantly in haplotype distribution. However, overall differentiation patterns were not informative with respect to subspecies diversification, as evidenced by the scattered distribution of maize and teosinte plastomes in both the network and Bayesian phylogenetic reconstructions. CONCLUSIONS: Knowledge of intraspecific plastome variation provides the framework for a more comprehensive understanding of evolutionary processes at low taxonomic levels and may become increasingly important for future plant barcoding efforts. Whole-plastome sequencing provided useful variability to contribute to maize phylogeographic studies. The structuring of haplotype diversity in the maize landraces examined here clearly reflects the distinction between the Andean and South American lowland gene pools previously inferred based on nuclear markers.

Expression of Oncogenic Drivers in 3D Cell Culture Depends on Nuclear ATP Synthesis by NUDT5.

The growth of cancer cells as oncospheres in three-dimensional (3D) culture provides a robust cell model for understanding cancer progression, as well as for early drug discovery and validation. We have previously described a novel pathway in breast cancer cells, whereby ADP (Adenosine diphosphate)-ribose derived from hydrolysis of poly (ADP-Ribose) and pyrophosphate (PPi) are converted to ATP, catalysed by the enzyme NUDT5 (nucleotide diphosphate hydrolase type 5). Overexpression of the NUDT5 gene in breast and other cancer types is associated with poor prognosis, increased risk of recurrence and metastasis. In order to understand the role of NUDT5 in cancer cell growth, we performed phenotypic and global expression analysis in breast cancer cells grown as oncospheres. Comparison of two-dimensional (2D) versus 3D cancer cell cultures from different tissues of origin suggest that NUDT5 increases the aggressiveness of the disease via the modulation of several key driver genes, including ubiquitin specific peptidase 22 (USP22), RAB35B, focadhesin (FOCAD) and prostagladin E synthase (PTGES). NUDT5 functions as a master regulator of key oncogenic pathways and of genes involved in cell adhesion, cancer stem cell (CSC) maintenance and epithelial to mesenchyme transition (EMT). Inhibiting the enzymatic activities of NUDT5 prevents oncosphere formation and precludes the activation of cancer driver genes. These findings highlight NUDT5 as an upstream regulator of tumour drivers and may provide a biomarker for cancer stratification, as well as a novel target for drug discovery for combinatorial drug regimens for the treatment of aggressive cancer types and metastasis.

C/EBPα mediates the growth inhibitory effect of progestins on breast cancer cells.

Steroid hormones are key gene regulators in breast cancer cells. While estrogens stimulate cell proliferation, progestins activate a single cell cycle followed by proliferation arrest. Here, we use biochemical and genome-wide approaches to show that progestins achieve this effect via a functional crosstalk with C/EBPα. Using ChIP-seq, we identify around 1,000 sites where C/EBPα binding precedes and helps binding of progesterone receptor (PR) in response to hormone. These regions exhibit epigenetic marks of active enhancers, and C/EBPα maintains an open chromatin conformation that facilitates loading of ligand-activated PR. Prior to hormone exposure, C/EBPα favors promoter-enhancer contacts that assure hormonal regulation of key genes involved in cell proliferation by facilitating binding of RAD21, YY1, and the Mediator complex. Knockdown of C/EBPα disrupts enhancer-promoter contacts and decreases the presence of these architectural proteins, highlighting its key role in 3D chromatin looping. Thus, C/EBPα fulfills a previously unknown function as a potential growth modulator in hormone-dependent breast cancer.

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.

In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.

A comparison of mechanistic signaling pathway activity analysis methods.

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

Arginine Citrullination at the C-Terminal Domain Controls RNA Polymerase II Transcription.

The post-translational modification of key residues at the C-terminal domain of RNA polymerase II (RNAP2-CTD) coordinates transcription, splicing, and RNA processing by modulating its capacity to act as a landing platform for a variety of protein complexes. Here, we identify a new modification at the CTD, the deimination of arginine and its conversion to citrulline by peptidyl arginine deiminase 2 (PADI2), an enzyme that has been associated with several diseases, including cancer. We show that, among PADI family members, only PADI2 citrullinates R1810 (Cit1810) at repeat 31 of the CTD. Depletion of PADI2 or loss of R1810 results in accumulation of RNAP2 at transcription start sites, reduced gene expression, and inhibition of cell proliferation. Cit1810 is needed for interaction with the P-TEFb (positive transcription elongation factor b) kinase complex and for its recruitment to chromatin. In this way, CTD-Cit1810 favors RNAP2 pause release and efficient transcription in breast cancer cells.

Hormone-control regions mediate steroid receptor-dependent genome organization.

In breast cancer cells, some topologically associating domains (TADs) behave as hormonal gene regulation units, within which gene transcription is coordinately regulated in response to steroid hormones. Here we further describe that responsive TADs contain 20- to 100-kb-long clusters of intermingled estrogen receptor (ESR1) and progesterone receptor (PGR) binding sites, hereafter called hormone-control regions (HCRs). In T47D cells, we identified more than 200 HCRs, which are frequently bound by unliganded ESR1 and PGR. These HCRs establish steady long-distance inter-TAD interactions between them and organize characteristic looping structures with promoters in their TADs even in the absence of hormones in ESR1+-PGR+ cells. This organization is dependent on the expression of the receptors and is further dynamically modulated in response to steroid hormones. HCRs function as platforms that integrate different signals, resulting in some cases in opposite transcriptional responses to estrogens or progestins. Altogether, these results suggest that steroid hormone receptors act not only as hormone-regulated sequence-specific transcription factors but also as local and global genome organizers.

The modular network structure of the mutational landscape of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.

BACKGROUND: Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases. RESULTS: Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression. CONCLUSION: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker. REVIEWERS: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.

Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.

Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies.Significance: Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. Cancer Res; 78(21); 6059-72. ©2018 AACR.

The effects of death and post-mortem cold ischemia on human tissue transcriptomes.

Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.

Genomics of the origin and evolution of Citrus.

The genus Citrus, comprising some of the most widely cultivated fruit crops worldwide, includes an uncertain number of species. Here we describe ten natural citrus species, using genomic, phylogenetic and biogeographic analyses of 60 accessions representing diverse citrus germ plasms, and propose that citrus diversified during the late Miocene epoch through a rapid southeast Asian radiation that correlates with a marked weakening of the monsoons. A second radiation enabled by migration across the Wallace line gave rise to the Australian limes in the early Pliocene epoch. Further identification and analyses of hybrids and admixed genomes provides insights into the genealogy of major commercial cultivars of citrus. Among mandarins and sweet orange, we find an extensive network of relatedness that illuminates the domestication of these groups. Widespread pummelo admixture among these mandarins and its correlation with fruit size and acidity suggests a plausible role of pummelo introgression in the selection of palatable mandarins. This work provides a new evolutionary framework for the genus Citrus.

Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.

MOTIVATION: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome. RESULTS: The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples. AVAILABILITY AND IMPLEMENTATION: This tool is freely available at http://gitlab.com/carbonell/ces. CONTACT: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

The pan-cancer pathological regulatory landscape.

Dysregulation of the normal gene expression program is the cause of a broad range of diseases, including cancer. Detecting the specific perturbed regulators that have an effect on the generation and the development of the disease is crucial for understanding the disease mechanism and for taking decisions on efficient preventive and curative therapies. Moreover, detecting such perturbations at the patient level is even more important from the perspective of personalized medicine. We applied the Transcription Factor Target Enrichment Analysis, a method that detects the activity of transcription factors based on the quantification of the collective transcriptional activation of their targets, to a large collection of 5607 cancer samples covering eleven cancer types. We produced for the first time a comprehensive catalogue of altered transcription factor activities in cancer, a considerable number of them significantly associated to patient's survival. Moreover, we described several interesting TFs whose activity do not change substantially in the cancer with respect to the normal tissue but ultimately play an important role in patient prognostic determination, which suggest they might be promising therapeutic targets. An additional advantage of this method is that it allows obtaining personalized TF activity estimations for individual patients.

Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models.

The discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.

The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations.

Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations.

Babelomics 5.0: functional interpretation for new generations of genomic data.

Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.

A Phylogenetic Analysis of 34 Chloroplast Genomes Elucidates the Relationships between Wild and Domestic Species within the Genus Citrus.

Citrus genus includes some of the most important cultivated fruit trees worldwide. Despite being extensively studied because of its commercial relevance, the origin of cultivated citrus species and the history of its domestication still remain an open question. Here, we present a phylogenetic analysis of the chloroplast genomes of 34 citrus genotypes which constitutes the most comprehensive and detailed study to date on the evolution and variability of the genus Citrus. A statistical model was used to estimate divergence times between the major citrus groups. Additionally, a complete map of the variability across the genome of different citrus species was produced, including single nucleotide variants, heteroplasmic positions, indels (insertions and deletions), and large structural variants. The distribution of all these variants provided further independent support to the phylogeny obtained. An unexpected finding was the high level of heteroplasmy found in several of the analyzed genomes. The use of the complete chloroplast DNA not only paves the way for a better understanding of the phylogenetic relationships within the Citrus genus but also provides original insights into other elusive evolutionary processes, such as chloroplast inheritance, heteroplasmy, and gene selection.