ORCID Profile
0000-0001-5235-8483
Current Organisation
Janssen Pharmaceuticals
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Publisher: Oxford University Press (OUP)
Date: 15-07-2019
DOI: 10.1093/NAR/GKZ599
Abstract: RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript ersity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce an RNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.
Publisher: Springer Science and Business Media LLC
Date: 15-03-2018
Abstract: A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is ided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net-based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed.
Publisher: Springer Science and Business Media LLC
Date: 18-01-2016
DOI: 10.1038/NN.4222
Publisher: Springer Science and Business Media LLC
Date: 03-2014
DOI: 10.1038/NATURE12787
Publisher: Public Library of Science (PLoS)
Date: 22-01-2014
Publisher: Wiley
Date: 03-06-2011
Publisher: Public Library of Science (PLoS)
Date: 25-07-2022
DOI: 10.1371/JOURNAL.PCBI.1010310
Abstract: Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to Graphia’s functionality is support for the calculation of correlation matrices from any tabular matrix of continuous or discrete values, whereupon the software is designed to rapidly visualise the often very large graphs that result in 2D or 3D space. Following graph construction, an extensive range of measurement algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are available, for graph exploration and analysis. Combined, these provide a powerful solution for the interpretation of high-dimensional data from many sources, or data already in the form of a network or equivalent adjacency matrix. Several use cases of Graphia are described, to showcase its wide range of applications in the analysis biological data. Graphia runs on all major desktop operating systems, is extensible through the deployment of plugins and is freely available to download from graphia.app/ .
Publisher: Public Library of Science (PLoS)
Date: 10-08-2016
Publisher: Elsevier BV
Date: 03-2015
Publisher: Public Library of Science (PLoS)
Date: 25-03-2015
Publisher: Springer Science and Business Media LLC
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 24-04-2018
Publisher: Oxford University Press (OUP)
Date: 10-04-2014
Abstract: Macrophages respond to the TLR4 agonist LPS with a sequential transcriptional cascade controlled by a complex regulatory network of signaling pathways and transcription factors. At least two distinct pathways are currently known to be engaged by TLR4 and are distinguished by their dependence on the adaptor molecule MyD88. We have used gene expression microarrays to define the effects of each of three variables—LPS dose, LPS versus IFN-β and -γ, and genetic background—on the transcriptional response of mouse BMDMs. Analysis of correlation networks generated from the data has identified subnetworks or modules within the macrophage transcriptional network that are activated selectively by these variables. We have identified mouse strain-specific signatures, including a module enriched for SLE susceptibility candidates. In the modules of genes unique to different treatments, we found a module of genes induced by type-I IFN but not by LPS treatment, suggesting another layer of complexity in the LPS-TLR4 signaling feedback control. We also observe that the activation of the complement system, in common with the known activation of MHC class 2 genes, is reliant on IFN-γ signaling. Taken together, these data further highlight the exquisite nature of the regulatory systems that control macrophage activation, their likely relevance to disease resistance/susceptibility, and the appropriate response of these cells to proinflammatory stimuli.
Publisher: Elsevier BV
Date: 06-2010
DOI: 10.1016/J.YGENO.2010.03.002
Abstract: Very large microarray datasets showing gene expression across multiple tissues and cell populations provide a window on the transcriptional networks that underpin the differences in functional activity between biological systems. Clusters of co-expressed genes provide lineage markers, candidate regulators of cell function and, by applying the principle of guilt by association, candidate functions for genes of currently unknown function. We have analysed a dataset comprising pure cell populations from hemopoietic and non-hemopoietic cell types (biogps.gnf.org). Using a novel network visualisation and clustering approach, we demonstrate that it is possible to identify very tight expression signatures associated specifically with embryonic stem cells, mesenchymal cells and hematopoietic lineages. Selected ex les validate the prediction that gene function can be inferred by co-expression. One expression cluster was enriched in phagocytes, which, alongside endosome-lysosome constituents, contains genes that may make up a 'pathway' for phagocyte differentiation. Promoters of these genes are enriched for binding sites for the ETS/PU.1 and MITF families. Another cluster was associated with the production of a specific extracellular matrix, with high levels of gene expression shared by cells of mesenchymal origin (fibroblasts, adipocytes, osteoblasts and myoblasts). We discuss the limitations placed upon such data by the presence of alternative promoters with distinct tissue specificity within many protein-coding genes.
Publisher: The Company of Biologists
Date: 15-05-2014
DOI: 10.1242/DEV.107029
Abstract: Sertoli cells (SCs) regulate testicular fate in the differentiating gonad and are the main regulators of spermatogenesis in the adult testis however, their role during the intervening period of testis development, in particular during adult Leydig cell (ALC) differentiation and function, remains largely unknown. To examine SC function during fetal and prepubertal development we generated two transgenic mouse models that permit controlled, cell-specific ablation of SCs in pre- and postnatal life. Results show that SCs are required: (1) to maintain the differentiated phenotype of peritubular myoid cells (PTMCs) in prepubertal life (2) to maintain the ALC progenitor population in the postnatal testis and (3) for development of normal ALC numbers. Furthermore, our data show that fetal LCs function independently from SC, germ cell or PTMC support in the prepubertal testis. Together, these findings reveal that SCs remain essential regulators of testis development long after the period of sex determination. These findings have significant implications for our understanding of male reproductive disorders and wider androgen-related conditions affecting male health.
Publisher: Elsevier BV
Date: 02-2014
Publisher: Public Library of Science (PLoS)
Date: 21-08-2014
Publisher: Springer Science and Business Media LLC
Date: 08-2009
DOI: 10.1038/NBT.1558
Abstract: Circuit diagrams and Unified Modeling Language diagrams are just two ex les of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
Publisher: Springer Science and Business Media LLC
Date: 03-1993
DOI: 10.1007/BF00374004
Publisher: Springer Science and Business Media LLC
Date: 16-06-2010
DOI: 10.1038/EJHG.2010.91
Publisher: Proceedings of the National Academy of Sciences
Date: 27-03-2014
Abstract: Naturally occurring regulatory T (Treg) cells, which specifically express the transcription factor forkhead box P3 (Foxp3), are engaged in the maintenance of immunological self-tolerance and homeostasis. By transcriptional start site cluster analysis, we assessed here how genome-wide patterns of DNA methylation or Foxp3 binding sites were associated with Treg-specific gene expression. We found that Treg-specific DNA hypomethylated regions were closely associated with Treg up-regulated transcriptional start site clusters, whereas Foxp3 binding regions had no significant correlation with either up- or down-regulated clusters in nonactivated Treg cells. However, in activated Treg cells, Foxp3 binding regions showed a strong correlation with down-regulated clusters. In accordance with these findings, the above two features of activation-dependent gene regulation in Treg cells tend to occur at different locations in the genome. The results collectively indicate that Treg-specific DNA hypomethylation is instrumental in gene up-regulation in steady state Treg cells, whereas Foxp3 down-regulates the expression of its target genes in activated Treg cells. Thus, the two events seem to play distinct but complementary roles in Treg-specific gene expression.
Publisher: Cold Spring Harbor Laboratory
Date: 05-2017
DOI: 10.1101/132696
Abstract: Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of ‘guilt by association’ was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages. Sheep are ruminant mammals kept as livestock for the production of meat, milk and wool in agricultural industries across the globe. Genetic and genomic information can be used to improve production traits such as disease resiliance. The sheep genome is however missing important information relating to gene function and many genes, which may be important for productivity, have no informative gene name. This can be remedied using RNA-Sequencing to generate a global expression profile of all protein-coding genes, across multiple organ systems and developmental stages. Clustering genes based on their expression profile across tissues and cells allows us to assign function to those genes. If for ex le a gene with no informative gene name is expressed in macrophages and is found within a cluster of known macrophage related genes it is likely to be involved in macrophage function and play a role in innate immunity. This information improves the quality of the reference genome and provides insight into biological processes underlying the complex traits that influence the productivity of sheep and other livestock species.
Publisher: American Association for Cancer Research (AACR)
Date: 11-2018
DOI: 10.1158/2326-6066.CIR-18-0342
Abstract: The immune composition of the tumor microenvironment regulates processes including angiogenesis, metastasis, and the response to drugs or immunotherapy. To facilitate the characterization of the immune component of tumors from transcriptomics data, a number of immune cell transcriptome signatures have been reported that are made up of lists of marker genes indicative of the presence a given immune cell population. The majority of these gene signatures have been defined through analysis of isolated blood cells. However, blood cells do not reflect the differentiation or activation state of similar cells within tissues, including tumors, and consequently markers derived from blood cells do not necessarily transfer well to tissues. To address this issue, we generated a set of immune gene signatures derived directly from tissue transcriptomics data using a network-based deconvolution approach. We define markers for seven immune cell types, collectively named ImSig, and demonstrate how these markers can be used for the quantitative estimation of the immune cell content of tumor and nontumor tissue s les. The utility of ImSig is demonstrated through the stratification of melanoma patients into subgroups of prognostic significance and the identification of immune cells with the use of single-cell RNA-sequencing data derived from tumors. Use of ImSig is facilitated by an R package (imsig). Cancer Immunol Res 6(11) 1388–400. ©2018 AACR.
Publisher: Springer Science and Business Media LLC
Date: 2004
DOI: 10.1007/S10456-004-1677-0
Abstract: We recently published a review in this journal describing the design, hybridisation and basic data processing required to use gene arrays to investigate vascular biology (Evans et al. Angiogenesis 2003 6: 93-104). Here, we build on this review by describing a set of powerful and robust methods for the analysis and interpretation of gene array data derived from primary vascular cell cultures. First, we describe the evaluation of transcriptome heterogeneity between primary cultures derived from different in iduals, and estimation of the false discovery rate introduced by this heterogeneity and by experimental noise. Then, we discuss the appropriate use of Bayesian t-tests, clustering and independent component analysis to mine the data. We illustrate these principles by analysis of a previously unpublished set of gene array data in which human umbilical vein endothelial cells (HUVEC) cultured in either rich or low-serum media were exposed to vascular endothelial growth factor (VEGF)-A165 or placental growth factor (PlGF)-1(131). We have used Affymetrix U95A gene arrays to map the effects of these factors on the HUVEC transcriptome. These experiments followed a paired design and were biologically replicated three times. In addition, one experiment was repeated using serial analysis of gene expression (SAGE). In contrast to some previous studies, we found that VEGF-A and PlGF consistently regulated only small, non-overlapping and culture media-dependant sets of HUVEC transcripts, despite causing significant cell biological changes.
Publisher: Elsevier BV
Date: 2018
Publisher: Oxford University Press (OUP)
Date: 26-01-2018
Abstract: Several lines of evidence link macrophage activation and inflammation with (monoaminergic) nervous systems in the etiology of depression. IFN treatment is associated with depressive symptoms, whereas anti-TNFα therapies elicit positive mood. This study describes the actions of 2 monoaminergic antidepressants (escitalopram, nortriptyline) and 3 anti-inflammatory drugs (indomethacin, prednisolone, and anti-TNFα antibody) on the response of human monocyte-derived macrophages (MDMs) from 6 in iduals to LPS or IFN-α. Expression profiling revealed robust changes in the MDM transcriptome (3294 genes at P & 0.001) following LPS challenge, whereas a more limited subset of genes (499) responded to IFNα. Contrary to published reports, administered at nontoxic doses, neither monoaminergic antidepressant significantly modulated the transcriptional response to either inflammatory challenge. Each anti-inflammatory drug had a distinct impact on the expression of inflammatory cytokines and on the profile of inducible gene expression—notably on the regulation of enzymes involved in metabolism of tryptophan. Inter alia, the effect of anti-TNFα antibody confirmed a predicted autocrine stimulatory loop in human macrophages. The transcriptional changes were predictive of tryptophan availability and kynurenine synthesis, as analyzed by targeted metabolomic studies on cellular supernatants. We suggest that inflammatory processes in the brain or periphery could impact on depression by altering the availability of tryptophan for serotonin synthesis and/or by increasing production of neurotoxic kynurenine.
Publisher: Springer Science and Business Media LLC
Date: 30-10-2011
DOI: 10.1038/NATURE10531
Publisher: Elsevier BV
Date: 11-2011
DOI: 10.1016/J.IMBIO.2011.07.025
Abstract: Macrophages play a major role in tissue remodelling during development, wound healing and tissue homeostasis, and are central to innate immunity and to the pathology of tissue injury and inflammation. Given this fundamental role in many aspects of biological function, an enormous wealth of information has accumulated on these fascinating cells in the literature and other public repositories. With the escalation of genome-scale data derived from macrophages and related haematopoietic cell types, there is a growing need for an integrated resource that seeks to compile, organise and analyse our collective knowledge of macrophage biology. Here we describe a community-driven web-based resource, macrophages.com that aims to provide a portal onto various types of Omics data to facilitate comparative genomic studies, promoter and transcriptional network analyses, models of macrophage pathways together with other information on these cells. To this end, the website combines public and in-house analyses of expression data with pre-analysed views of co-expressed genes as supported by the network analysis tool BioLayout Express(3D), as well as providing access to maps of pathways active in macrophages. Macrophages.com also provides access to an extensive image library of macrophages in adult/embryonic tissue sections prepared from normal and transgenic mice. In addition, the site links to the Human Protein Atlas database so as to provide direct access to protein expression patterns in human macrophages. Finally, an integrated gene-centric portal provides the tools for rapid promoter analysis studies based on a comprehensive set of CAGE-derived transcription start site (TSS) sequences in human and mouse genomes as generated by the Functional Annotation of Mammalian genomes (FANTOM) projects initiated by the RIKEN Omics Science Center. Our aim is to continue to grow the macrophages.com resource using publicly available data, as well as in-house generated knowledge. In so doing we aim to provide a user-friendly community website and a community portal for access to comprehensive sets of macrophage-related data.
Publisher: BMJ
Date: 12-2011
DOI: 10.1136/BMJ.D7459
Publisher: Wiley
Date: 15-10-2014
DOI: 10.1111/IMR.12211
Abstract: Monocytes and macrophages differentiate from progenitor cells under the influence of colony-stimulating factors. Genome-scale data have enabled the identification of the sets of genes that are associated with specific functions and the mechanisms by which thousands of genes are regulated in response to pathogen challenge. In large datasets, it is possible to identify large sets of genes that are coregulated with the transcription factors that regulate them. They include macrophage-specific genes, interferon-responsive genes, early inflammatory genes, and those associated with endocytosis. Such analyses can also extract macrophage-associated signatures from large cancer tissue datasets. However, cluster analysis provides no support for a signature that distinguishes macrophages from antigen-presenting dendritic cells, nor the classification of macrophage activation states as classical versus alternative, or M1 versus M2. Although there has been a focus on a small subset of lineage-enriched transcription factors, such as PU.1, more than half of the transcription factors in the genome can be expressed in macrophage lineage cells under some state of activation, and they interact in a complex network. The network architecture is conserved across species, but many of the target genes evolve rapidly and differ between mouse and human. The data and publication deluge related to macrophage biology require the development of new analytical tools and ways of presenting information in an accessible form.
Publisher: Cold Spring Harbor Laboratory
Date: 04-04-2016
DOI: 10.1101/047043
Abstract: In silico modelling of biological pathways is a major endeavour of systems biology. Here we present a methodology for construction of pathway models from the literature and other sources using a biologist-friendly graphical modelling system. The pathway notation scheme, called mEPN, is based on the principles of the process diagrams and Petri nets, and facilitates both the graphical representation of complex systems as well as dynamic simulation of their activity. The protocol is ided into four sections: 1) assembly of the pathway in the yEd software package using the mEPN scheme, 2) conversion of the pathway into a computable format, 3) pathway visualisation and in silico simulation using the BioLayout Express 3D software, 4) optimisation of model parameterisation. This method allows reconstruction of any metabolic, signalling and transcriptional pathway as a means of knowledge management, as well as supporting the systems level modelling of their dynamic activity.
Publisher: Springer Science and Business Media LLC
Date: 03-2014
DOI: 10.1038/NATURE13182
Publisher: BMJ
Date: 11-1991
Abstract: Pancreatic secretory trypsin inhibitor (PSTI) is a potent protease inhibitor that also has growth promoting activity. It has recently been identified in the foveolar cells of the stomach, which secrete mucus. We examined the effects of the prostaglandin E1 analogue misoprostol on gastric PSTI output. Seven normal volunteers took part. An initial period of gastric aspiration was followed by four 40 minute periods of gastric perfusion at 5 ml/minute of: 0.14 mol/l saline, 0.17 mmol/l bicarbonate, bicarbonate with misoprostol 400 micrograms, and then bicarbonate again. All perfusates contained polyethylene glycol 4000 as a marker. Misoprostol increased median gastric secretion of PSTI from 11 to 33 micrograms/hour (p less than 0.05), producing concentrations in gastric juice six times higher than those found in jejunal juice and about 1/30 of the values seen in pancreatic juice. Median mucus secretion increased to a lesser extent from 29 to 38 mg/hour during misoprostol. There was no change in intragastric concentrations of protein or of epidermal growth factor during infusion of misoprostol. Infusion of pentagastrin (6 micrograms/kg/hour) had no effect on gastric secretion of mucus, PSTI, or protein. Human gastric mucus was degraded on incubation with trypsin in vitro and this was prevented by the addition of PSTI. These results suggest that gastric PSTI may protect the gastric mucus layer against refluxed pancreatic proteases. Increased output of PSTI during microprostol may contribute to the protective effect of this drug.
Publisher: Springer Science and Business Media LLC
Date: 05-01-2015
Publisher: Springer Science and Business Media LLC
Date: 11-07-2013
Abstract: Biopsies taken from in idual tumours exhibit extensive differences in their cellular composition due to the inherent heterogeneity of cancers and vagaries of s le collection. As a result genes expressed in specific cell types, or associated with certain biological processes are detected at widely variable levels across s les in transcriptomic analyses. This heterogeneity also means that the level of expression of genes expressed specifically in a given cell type or process, will vary in line with the number of those cells within s les or activity of the pathway, and will therefore be correlated in their expression. Using a novel 3D network-based approach we have analysed six large human cancer microarray datasets derived from more than 1,000 in iduals. Based upon this analysis, and without needing to isolate the in idual cells, we have defined a broad spectrum of cell-type and pathway-specific gene signatures present in cancer expression data which were also found to be largely conserved in a number of independent datasets. The conserved signature of the tumour-associated macrophage is shown to be largely-independent of tumour cell type. All stromal cell signatures have some degree of correlation with each other, since they must all be inversely correlated with the tumour component. However, viewed in the context of established tumours, the interactions between stromal components appear to be multifactorial given the level of one component e.g. vasculature, does not correlate tightly with another, such as the macrophage.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 27-02-2015
Abstract: In order to understand cellular differentiation, it is important to understand the timing of the regulation of gene expression. Arner et al. used cap analysis of gene expression (CAGE) to analyze gene enhancer and promoter activities in a number of human and mouse cell types. The RNA of enhancers was transcribed first, followed by that of transcription factors, and finally by genes that are not transcription factors. Science , this issue p. 1010
Publisher: Public Library of Science (PLoS)
Date: 30-09-2010
Publisher: Elsevier BV
Date: 09-2010
DOI: 10.1016/J.IMBIO.2010.05.012
Abstract: In order to address fundamental questions associated with the relationships between mononuclear phagocytes and other myeloid and lymphoid cell populations, we have taken advantage of the growing body of expression data available in the public domain. We collated a large number of published expression studies on mouse haemopoietic cell lineages comprising 304 cell s les from 29 independent experiments performed on a single microarray platform (Affymetrix MOE430-2). The data were subjected to network-based cluster analysis using Biolayout Express(3D). Genes with related function clustered together in distinct regions of the graph reaffirming many known associations between gene expression and role in specific pathways and defining most major cell types of the immune system. Promoters of genes within in idual clusters were distinguished by over-representation of regulatory motifs recognised by specific transcription factors. However, these data indicate that commonly used myeloid subpopulation markers, such as CD11c (Itgax), do not correlate with expression of other genes, and further bring into question their use in defining myeloid cell lineage, activation (M1 vs. M2) and antigen-presenting cell function. In particular, there were few mRNA markers that clearly distinguished classical dendritic cells (DC) from macrophages, other than low expression of genes required for phagocytic activity. Bone marrow-derived DC, grown in GM-CSF, were clearly identified as phagocytes and distinguished from isolated lymphoid tissue DC. Thus, through pooling datasets from public data and examining the gene expression clusters within, we can learn a great deal about the transcriptional networks that underpin the differences in functional activities between cell populations of the immune system.
Publisher: Public Library of Science (PLoS)
Date: 03-2018
Publisher: Oxford University Press (OUP)
Date: 25-02-2015
DOI: 10.1189/JLB.6TA1014-477RR
Abstract: The generation of myeloid cells from their progenitors is regulated at the level of transcription by combinatorial control of key transcription factors influencing cell-fate choice. To unravel the global dynamics of this process at the transcript level, we generated transcription profiles for 91 human cell types of myeloid origin by use of CAGE profiling. The CAGE sequencing of these s les has allowed us to investigate erse aspects of transcription control during myelopoiesis, such as identification of novel transcription factors, miRNAs, and noncoding RNAs specific to the myeloid lineage. We further reconstructed a transcription regulatory network by clustering coexpressed transcripts and associating them with enriched cis-regulatory motifs. With the use of the bidirectional expression as a proxy for enhancers, we predicted over 2000 novel enhancers, including an enhancer 38 kb downstream of IRF8 and an intronic enhancer in the KIT gene locus. Finally, we highlighted relevance of these data to dissect transcription dynamics during progressive maturation of granulocyte precursors. A multifaceted analysis of the myeloid transcriptome is made available (www.myeloidome.roslin.ed.ac.uk). This high-quality dataset provides a powerful resource to study transcriptional regulation during myelopoiesis and to infer the likely functions of unannotated genes in human innate immunity.
Publisher: Springer Science and Business Media LLC
Date: 28-08-2013
Abstract: The draft genome of the domestic pig (Sus scrofa) has recently been published permitting refined analysis of the transcriptome. Pig breeds have been reported to differ in their resistance to infectious disease. In this study we examine whether there are corresponding differences in gene expression in innate immune cells We demonstrate that macrophages can be harvested from three different compartments of the pig (lungs, blood and bone-marrow), cryopreserved and subsequently recovered and differentiated in CSF-1. We have performed surface marker analysis and gene expression profiling on macrophages from these compartments, comparing twenty-five animals from five different breeds and their response to lipopolysaccharide. The results provide a clear distinction between alveolar macrophages (AM) and monocyte-derived (MDM) and bone-marrow-derived macrophages (BMDM). In particular, the lung macrophages express the growth factor, FLT1 and its ligand, VEGFA at high levels, suggesting a distinct pathway of growth regulation. Relatively few genes showed breed-specific differential expression, notably CXCR2 and CD302 in alveolar macrophages. In contrast, there was substantial inter-in idual variation between pigs within breeds, mostly affecting genes annotated as being involved in immune responses. Pig macrophages more closely resemble human, than mouse, in their set of macrophage-expressed and LPS-inducible genes. Future research will address whether inter-in idual variation in macrophage gene expression is heritable, and might form the basis for selective breeding for disease resistance.
Publisher: Cold Spring Harbor Laboratory
Date: 03-09-2020
DOI: 10.1101/2020.09.02.279349
Abstract: Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics, genomics data. The software imports data already defined as a network or a similarity matrix and is designed to rapidly visualise very large graphs in 2D or 3D space, providing a wide range of functionality for graph exploration. An extensive range of analysis algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are also available. Graphia’s core is extensible through the deployment of plugins, supporting rapid development of additional computational analyses and features necessary for a given analysis task or data source. A plugin for correlation network analysis is distributed with the core application, to support the generation of correlation graphs from any tabular matrix of continuous or discrete values. This provides a powerful analysis solution for the interpretation of high-dimensional data from many sources. Several use cases of Graphia are described, to showcase its wide range of applications. Graphia runs on all major desktop operating systems and is freely available to download from graphia.app/ .
Publisher: Elsevier BV
Date: 07-2004
Publisher: Springer Science and Business Media LLC
Date: 15-11-2012
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Thomas Freeman.