Mural lymphatic endothelial cells regulate meningeal angiogenesis in the zebrafish.

Mural cells of the vertebrate brain maintain vascular integrity and function, play roles in stroke and are involved in maintenance of neural stem cells. However, the origins, diversity and roles of mural cells remain to be fully understood. Using transgenic zebrafish, we identified a population of isolated mural lymphatic endothelial cells surrounding meningeal blood vessels. These meningeal mural lymphatic endothelial cells (muLECs) express lymphatic endothelial cell markers and form by sprouting from blood vessels. In larvae, muLECs develop from a lymphatic endothelial loop in the midbrain into a dispersed, nonlumenized mural lineage. muLEC development requires normal signaling through the Vegfc-Vegfd-Ccbe1-Vegfr3 pathway. Mature muLECs produce vascular growth factors and accumulate low-density lipoproteins from the bloodstream. We find that muLECs are essential for normal meningeal vascularization. Together, these data identify an unexpected lymphatic lineage and developmental mechanism necessary for establishing normal meningeal blood vasculature.

Systems analysis identifies miR-29b regulation of invasiveness in melanoma.

BACKGROUND: In many cancers, microRNAs (miRs) contribute to metastatic progression by modulating phenotypic reprogramming processes such as epithelial-mesenchymal plasticity. This can be driven by miRs targeting multiple mRNA transcripts, inducing regulated changes across large sets of genes. The miR-target databases TargetScan and DIANA-microT predict putative relationships by examining sequence complementarity between miRs and mRNAs. However, it remains a challenge to identify which miR-mRNA interactions are active at endogenous expression levels, and of biological consequence. METHODS: We developed a workflow to integrate TargetScan and DIANA-microT predictions into the analysis of data-driven associations calculated from transcript abundance (RNASeq) data, specifically the mutual information and Pearson's correlation metrics. We use this workflow to identify putative relationships of miR-mediated mRNA repression with strong support from both lines of evidence. Applying this approach systematically to a large, published collection of unique melanoma cell lines - the Ludwig Melbourne melanoma (LM-MEL) cell line panel - we identified putative miR-mRNA interactions that may contribute to invasiveness. This guided the selection of interactions of interest for further in vitro validation studies. RESULTS: Several miR-mRNA regulatory relationships supported by TargetScan and DIANA-microT demonstrated differential activity across cell lines of varying matrigel invasiveness. Strong negative statistical associations for these putative regulatory relationships were consistent with target mRNA inhibition by the miR, and suggest that differential activity of such miR-mRNA relationships contribute to differences in melanoma invasiveness. Many of these relationships were reflected across the skin cutaneous melanoma TCGA dataset, indicating that these observations also show graded activity across clinical samples. Several of these miRs are implicated in cancer progression (miR-211, -340, -125b, -221, and -29b). The specific role for miR-29b-3p in melanoma has not been well studied. We experimentally validated the predicted miR-29b-3p regulation of LAMC1 and PPIC and LASP1, and show that dysregulation of miR-29b-3p or these mRNA targets can influence cellular invasiveness in vitro. CONCLUSIONS: This analytic strategy provides a comprehensive, systems-level approach to identify miR-mRNA regulation in high-throughput cancer data, identifies novel putative interactions with functional phenotypic relevance, and can be used to direct experimental resources for subsequent experimental validation. Computational scripts are available: http://github.com/uomsystemsbiology/LMMEL-miR-miner.

A novel EGR-1 dependent mechanism for YB-1 modulation of paclitaxel response in a triple negative breast cancer cell line.

Chemotherapy with taxanes such as paclitaxel (PTX) is a key component of triple negative breast cancer (TNBC) treatment. PTX is used in combination with other drugs in both the adjuvant setting and in advanced breast cancer. Because a proportion of patients respond poorly to PTX or relapse after its use, a greater understanding of the mechanisms conferring resistance to PTX is required. One protein shown to be involved in drug resistance is Y-box binding protein 1 (YB-1). High levels of YB-1 have previously been associated with resistance to PTX in TNBCs. In this study, we aimed to determine mechanisms by which YB-1 confers PTX resistance. We generated isogenic TNBC cell lines that differed by YB-1 levels and treated these with PTX. Using microarray analysis, we identified EGR1 as a potential target of YB-1. We found that low EGR1 mRNA levels are associated with poor breast cancer patient prognosis, and that EGR1 and YBX1 mRNA expression was inversely correlated in a TNBC line and in a proportion of TNBC tumours. Reducing the levels of EGR1 caused TNBC cells to become more resistant to PTX. Given that PTX targets cycling cells, we propose a model whereby high YB-1 levels in some TNBC cells can lead to reduced levels of EGR1, which in turn promotes slow cell cycling and resistance to PTX. Therefore YB-1 and EGR1 levels are biologically linked and may provide a biomarker for TNBC response to PTX.

MtVRN2 is a Polycomb VRN2-like gene which represses the transition to flowering in the model legume Medicago truncatula.

Optimising the timing of flowering contributes to successful sexual reproduction and yield in agricultural plants. FLOWERING LOCUS T (FT) genes, first identified in Arabidopsis thaliana (Arabidopsis), promote flowering universally, but the upstream flowering regulatory pathways can differ markedly among plants. Flowering in the model legume, Medicago truncatula (Medicago) is accelerated by winter cold (vernalisation) followed by long day (LD) photoperiods leading to elevated expression of the floral activator, FT-like gene FTa1. However, Medicago, like some other plants, lacks the activator CONSTANS (CO) and the repressor FLOWERING LOCUS C (FLC) genes which directly regulate FT and are key to LD and vernalisation responses in Arabidopsis. Conversely, Medicago has a VERNALISATION2-LIKE VEFS-box gene (MtVRN2). In Arabidopsis AtVRN2 is a key member of a Polycomb complex involved in stable repression of Arabidopsis FLC after vernalisation. VRN2-like genes have been identified in other eudicot plants, but their function has never been reported. We show that Mtvrn2 mutants bypass the need for vernalisation for early flowering in LD conditions in Medicago. Investigation of the underlying mechanism by transcriptome analysis reveals that Mtvrn2 mutants precociously express FTa1 and other suites of genes including floral homeotic genes. Double-mutant analysis indicates that early flowering is dependent on functional FTa1. The broad significance of our study is that we have demonstrated a function for a VRN2-like VEFS gene beyond the Brassicaceae. In particular, MtVRN2 represses the transition to flowering in Medicago by regulating the onset of expression of the potent floral activator, FTa1.

Spatially transformed fluorescence image data for ERK-MAPK and selected proteins within human epidermis.

BACKGROUND: Phosphoprotein signalling pathways have been intensively studied in vitro, yet their role in regulating tissue homeostasis is not fully understood. In the skin, interfollicular keratinocytes differentiate over approximately 2 weeks as they traverse the epidermis. The extracellular signal-regulated kinase (ERK) branch of the mitogen-activated protein kinase (MAPK) pathway has been implicated in this process. Therefore, we examined ERK-MAPK activity within human epidermal keratinocytes in situ. FINDINGS: We used confocal microscopy and immunofluorescence labelling to measure the relative abundances of Raf-1, MEK1/2 and ERK1/2, and their phosphorylated (active) forms within three human skin samples. Additionally, we measured the abundance of selected proteins thought to modulate ERK-MAPK activity, including calmodulin, ß1 integrin and stratifin (14-3-3σ); and of transcription factors known to act as effectors of ERK1/2, including the AP-1 components Jun-B, Fra2 and c-Fos. Imaging was performed with sufficient resolution to identify the plasma membrane, cytoplasm and nucleus as distinct domains within cells across the epidermis. The image field of view was also sufficiently large to capture the entire epidermis in cross-section, and thus the full range of keratinocyte differentiation in a single observation. Image processing methods were developed to quantify image data for mathematical and statistical analysis. Here, we provide raw image data and processed outputs. CONCLUSIONS: These data indicate coordinated changes in ERK-MAPK signalling activity throughout the depth of the epidermis, with changes in relative phosphorylation-mediated signalling activity occurring along the gradient of cellular differentiation. We believe these data provide unique information about intracellular signalling as they are obtained from a homeostatic human tissue, and they might be useful for investigating intercellular heterogeneity.

Regulation of ERK-MAPK signaling in human epidermis.

BACKGROUND: The skin is largely comprised of keratinocytes within the interfollicular epidermis. Over approximately two weeks these cells differentiate and traverse the thickness of the skin. The stage of differentiation is therefore reflected in the positions of cells within the tissue, providing a convenient axis along which to study the signaling events that occur in situ during keratinocyte terminal differentiation, over this extended two-week timescale. The canonical ERK-MAPK signaling cascade (Raf-1, MEK-1/2 and ERK-1/2) has been implicated in controlling diverse cellular behaviors, including proliferation and differentiation. While the molecular interactions involved in signal transduction through this cascade have been well characterized in cell culture experiments, our understanding of how this sequence of events unfolds to determine cell fate within a homeostatic tissue environment has not been fully characterized. METHODS: We measured the abundance of total and phosphorylated ERK-MAPK signaling proteins within interfollicular keratinocytes in transverse cross-sections of human epidermis using immunofluorescence microscopy. To investigate these data we developed a mathematical model of the signaling cascade using a normalized-Hill differential equation formalism. RESULTS: These data show coordinated variation in the abundance of phosphorylated ERK-MAPK components across the epidermis. Statistical analysis of these data shows that associations between phosphorylated ERK-MAPK components which correspond to canonical molecular interactions are dependent upon spatial position within the epidermis. The model demonstrates that the spatial profile of activation for ERK-MAPK signaling components across the epidermis may be maintained in a cell-autonomous fashion by an underlying spatial gradient in calcium signaling. CONCLUSIONS: Our data demonstrate an extended phospho-protein profile of ERK-MAPK signaling cascade components across the epidermis in situ, and statistical associations in these data indicate canonical ERK-MAPK interactions underlie this spatial profile of ERK-MAPK activation. Using mathematical modelling we have demonstrated that spatially varying calcium signaling components across the epidermis may be sufficient to maintain the spatial profile of ERK-MAPK signaling cascade components in a cell-autonomous manner. These findings may have significant implications for the wide range of cancer drugs which therapeutically target ERK-MAPK signaling components.

Modelling the conditional regulatory activity of methylated and bivalent promoters.

BACKGROUND: Predictive modelling of gene expression is a powerful framework for the in silico exploration of transcriptional regulatory interactions through the integration of high-throughput -omics data. A major limitation of previous approaches is their inability to handle conditional interactions that emerge when genes are subject to different regulatory mechanisms. Although chromatin immunoprecipitation-based histone modification data are often used as proxies for chromatin accessibility, the association between these variables and expression often depends upon the presence of other epigenetic markers (e.g. DNA methylation or histone variants). These conditional interactions are poorly handled by previous predictive models and reduce the reliability of downstream biological inference. RESULTS: We have previously demonstrated that integrating both transcription factor and histone modification data within a single predictive model is rendered ineffective by their statistical redundancy. In this study, we evaluate four proposed methods for quantifying gene-level DNA methylation levels and demonstrate that inclusion of these data in predictive modelling frameworks is also subject to this critical limitation in data integration. Based on the hypothesis that statistical redundancy in epigenetic data is caused by conditional regulatory interactions within a dynamic chromatin context, we construct a new gene expression model which is the first to improve prediction accuracy by unsupervised identification of latent regulatory classes. We show that DNA methylation and H2A.Z histone variant data can be interpreted in this way to identify and explore the signatures of silenced and bivalent promoters, substantially improving genome-wide predictions of mRNA transcript abundance and downstream biological inference across multiple cell lines. CONCLUSIONS: Previous models of gene expression have been applied successfully to several important problems in molecular biology, including the discovery of transcription factor roles, identification of regulatory elements responsible for differential expression patterns and comparative analysis of the transcriptome across distant species. Our analysis supports our hypothesis that statistical redundancy in epigenetic data is partially due to conditional relationships between these regulators and gene expression levels. This analysis provides insight into the heterogeneous roles of H3K4me3 and H3K27me3 in the presence of the H2A.Z histone variant (implicated in cancer progression) and how these signatures change during lineage commitment and carcinogenesis.

NAIL, a software toolset for inferring, analyzing and visualizing regulatory networks.

UNLABELLED: The wide variety of published approaches for the problem of regulatory network inference makes using multiple inference algorithms complex and time-consuming. Network Analysis and Inference Library (NAIL) is a set of software tools to simplify the range of computational activities involved in regulatory network inference. It uses a modular approach to connect different network inference algorithms to the same visualization and network-based analyses. NAIL is technology-independent and includes an interface layer to allow easy integration of components into other applications. AVAILABILITY AND IMPLEMENTATION: NAIL is implemented in MATLAB, runs on Windows, Linux and OSX, and is available from SourceForge at https://sourceforge.net/projects/nailsystemsbiology/ for all researchers to use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predictive modelling of gene expression from transcriptional regulatory elements.

Predictive modelling of gene expression provides a powerful framework for exploring the regulatory logic underpinning transcriptional regulation. Recent studies have demonstrated the utility of such models in identifying dysregulation of gene and miRNA expression associated with abnormal patterns of transcription factor (TF) binding or nucleosomal histone modifications (HMs). Despite the growing popularity of such approaches, a comparative review of the various modelling algorithms and feature extraction methods is lacking. We define and compare three methods of quantifying pairwise gene-TF/HM interactions and discuss their suitability for integrating the heterogeneous chromatin immunoprecipitation (ChIP)-seq binding patterns exhibited by TFs and HMs. We then construct log-linear and ϵ-support vector regression models from various mouse embryonic stem cell (mESC) and human lymphoblastoid (GM12878) data sets, considering both ChIP-seq- and position weight matrix- (PWM)-derived in silico TF-binding. The two algorithms are evaluated both in terms of their modelling prediction accuracy and ability to identify the established regulatory roles of individual TFs and HMs. Our results demonstrate that TF-binding and HMs are highly predictive of gene expression as measured by mRNA transcript abundance, irrespective of algorithm or cell type selection and considering both ChIP-seq and PWM-derived TF-binding. As we encourage other researchers to explore and develop these results, our framework is implemented using open-source software and made available as a preconfigured bootable virtual environment.

Virtual Reference Environments: a simple way to make research reproducible.

'Reproducible research' has received increasing attention over the past few years as bioinformatics and computational biology methodologies become more complex. Although reproducible research is progressing in several valuable ways, we suggest that recent increases in internet bandwidth and disk space, along with the availability of open-source and free-software licences for tools, enable another simple step to make research reproducible. In this article, we urge the creation of minimal virtual reference environments implementing all the tools necessary to reproduce a result, as a standard part of publication. We address potential problems with this approach, and show an example environment from our own work.

Predicting expression: the complementary power of histone modification and transcription factor binding data.

BACKGROUND: Transcription factors (TFs) and histone modifications (HMs) play critical roles in gene expression by regulating mRNA transcription. Modelling frameworks have been developed to integrate high-throughput omics data, with the aim of elucidating the regulatory logic that results from the interactions of DNA, TFs and HMs. These models have yielded an unexpected and poorly understood result: that TFs and HMs are statistically redundant in explaining mRNA transcript abundance at a genome-wide level. RESULTS: We constructed predictive models of gene expression by integrating RNA-sequencing, TF and HM chromatin immunoprecipitation sequencing and DNase I hypersensitivity data for two mammalian cell types. All models identified genome-wide statistical redundancy both within and between TFs and HMs, as previously reported. To investigate potential explanations, groups of genes were constructed for ontology-classified biological processes. Predictive models were constructed for each process to explore the distribution of statistical redundancy. We found significant variation in the predictive capacity of TFs and HMs across these processes and demonstrated the predictive power of HMs to be inversely proportional to process enrichment for housekeeping genes. CONCLUSIONS: It is well established that the roles played by TFs and HMs are not functionally redundant. Instead, we attribute the statistical redundancy reported in this and previous genome-wide modelling studies to the heterogeneous distribution of HMs across chromatin domains. Furthermore, we conclude that statistical redundancy between individual TFs can be readily explained by nucleosome-mediated cooperative binding. This could possibly help the cell confer regulatory robustness by rejecting signalling noise and allowing control via multiple pathways.

The flavoprotein FOXRED2 reductively activates nitro-chloromethylbenzindolines and other hypoxia-targeting prodrugs.

The nitro-chloromethylbenzindoline prodrug SN29428 has been rationally designed to target tumour hypoxia. SN29428 is metabolised to a DNA minor groove alkylator via oxygen-sensitive reductive activation initiated by unknown one-electron reductases. The present study sought to identify reductases capable of activating SN29428 in tumours. Expression of candidate reductases in cell lines was modulated using forced expression and, for P450 (cytochrome) oxidoreductase (POR), by zinc finger nuclease-mediated gene knockout. Affymetrix microarray mRNA expression of flavoreductases was correlated with SN29428 activation in a panel of 23 cancer cell lines. Reductive activation and cytotoxicity of prodrugs were measured using mass spectrometry and antiproliferative assays, respectively. SN29428 activation under hypoxia was strongly attenuated by the pan-flavoprotein inhibitor diphenyliodonium, but less so by knockout of POR suggesting other flavoreductases contribute. Forced expression of 5-methyltetrahydrofolate-homocysteine methyltransferase reductase (MTRR), as well as POR, increased activation of SN29428 in hypoxic HCT 116 cells. SN29428 activation strongly correlated with expression of POR and also FAD-dependent oxidoreductase domain containing 2 (FOXRED2), in cancer cell lines. This association persisted after removing the effect of POR enzyme activity using first-order partial correlation. Forced expression of FOXRED2 increased SN29428 activation and cytotoxicity in hypoxic HEK293 cells and also increased activation of hypoxia-targeted prodrugs PR-104A, tirapazamine and SN30000, and increased cytotoxicity of the clinical-stage prodrug TH-302. Thus this study has identified three flavoreductases capable of enzymatically activating SN29428, one of which (FOXRED2) has not previously been implicated in xenobiotic metabolism. These results will inform future development of biomarkers predictive of SN29428 sensitivity.

Links between the oncoprotein YB-1 and small non-coding RNAs in breast cancer.

BACKGROUND: The nucleic acid-binding protein YB-1, a member of the cold-shock domain protein family, has been implicated in the progression of breast cancer and is associated with poor patient survival. YB-1 has sequence similarity to LIN28, another cold-shock protein family member, which has a role in the regulation of small noncoding RNAs (sncRNAs) including microRNAs (miRNAs). Therefore, to investigate whether there is an association between YB-1 and sncRNAs in breast cancer, we investigated whether sncRNAs were bound by YB-1 in two breast cancer cell lines (luminal A-like and basal cell-like), and whether the abundance of sncRNAs and mRNAs changed in response to experimental reduction of YB-1 expression. RESULTS: RNA-immunoprecipitation with an anti-YB-1 antibody showed that several sncRNAs are bound by YB-1. Some of these were bound by YB-1 in both breast cancer cell lines; others were cell-line specific. The small RNAs bound by YB-1 were derived from various sncRNA families including miRNAs such as let-7 and miR-320, transfer RNAs, ribosomal RNAs and small nucleolar RNAs (snoRNA). Reducing YB-1 expression altered the abundance of a number of transcripts encoding miRNA biogenesis and processing proteins but did not alter the abundance of mature or precursor miRNAs. CONCLUSIONS: YB-1 binds to specific miRNAs, snoRNAs and tRNA-derived fragments and appears to regulate the expression of miRNA biogenesis and processing machinery. We propose that some of the oncogenic effects of YB-1 in breast cancer may be mediated through its interactions with sncRNAs.

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

Cell cycle gene networks are associated with melanoma prognosis.

BACKGROUND: Our understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways. METHODOLOGY/PRINCIPAL FINDINGS: Affymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro. CONCLUSIONS/SIGNIFICANCE: This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes (e.g., proliferation) but not others (e.g., immune response). The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets.