I'm Rob, a postdoctoral researcher at the Max Delbruck Centre in Berlin. My main research interest is regulatory sequences in animal genomes - regions of DNA that can control the activity of other genes. I am especially interested the way that cells fold their DNA, and how this can influence the effects of these regulatory sequences. There is growing evidence that this folding may be disregulated in some human diseases, and I think research in this area has the potential for opening new therapeutic avenues.

I'm also a keen programmer, especially in python. I've contributed to a number of open source projects, including doit and metaseq. I'm currently writing a genome browser in python called EIYBrowse (Extend It Yourself Browser). I've found programming an invaluable skill during my research and I like to spread the knowledge around, so I contribute to and teach for Software Carpentry. I've written a series of Software Carpentry lessons on using python's doit for automating scientific analysis pipelines.

I still play my guitar every now and then, snowboard as often as I can (i.e. not very often), occasionally sing "Blue Moon" and love to cycle.

Here's some other stuff I've been enjoying recently:






  • 2016
  • Postdoctoral Researcher

    Berlin Institute for Medical Systems Biology, Berlin (Aug 2015-Current)

    • Working on chromatin folding and nuclear organization in Prof. Ana Pombo's laboratory
  • 2015
  • MRC Funded PhD Studentship

    MRC Clinical Sciences Centre, London (Oct 2011-Jul 2015)

    • Investigated regulation of gene expression by long-range chromatin interactions
    • Co-supervised by Prof. Ana Pombo (MDC) and Prof. Niall Dillon (CSC)
  • 2016
  • Technical Support & Research Assistant

    Charles Beagrie Ltd, Salisbury (Jan 2004-Current)

    • Assisted with data analysis and desktop research for UK Research Data Service Feasibility Study and Keeping Research Data Safe project
  • 2011
  • Research Assistant

    Department of Biochemistry, Cambridge University (Oct 2010-Aug 2011)

    • Studied C. crescentus polynucleotide phosphorylase in Prof. Ben Luisi’s group
    • Cloning, expression, protein affinity purification, enzymatic assays and crystallography
  • 2010
  • Cancer Research UK Summer Studentship

    CRUK London Research Institute, London (Holborn) (Jun 2010-Sep 2010)

    • Studied Polycomb group proteins and their interaction with the ncRNA HOTAIR with Dr. Gordon Peters
    • Chromatin Immunoprecipitation, qPCR, lentiviral shRNA knockdowns, qRT-PCR and western blotting
  • Research Assistant

    Department of Biochemistry, Cambridge University (Jan 2010-May 2010)

    • Development of the NMR analysis tool “DANGLE” with Dr. William Broadhurst
    • Programming, data analysis and statistical work to improve the accuracy of the DANGLE algorithm for predicting protein dihedral angles from chemical shift data
  • 2005
  • Work Experience Student

    Wellcome Trust Sanger Centre, Hinxton (Jun 2005-Jun 2005)

    • 1 week placement with an ENCODE project group
    • 1 week placement in the sequencing centre


Enhancer Journal Club: Enhancer runaway and the evolution of diploid gene expression

By: Rob Beagrie

I came across this very interesting theoretical paper about enhancer evolution a couple of months ago1 and thought it was worth a little discussion. The paper comes from Frederic Fyon and others at the CNRS in Montpellier. The original paper can be found at PLOS Genetics, and it's well worth a read. Here's the abstract: Evidence is mounting that the evolution of gene expression plays a major role in adaptation and speciation. Understanding the evolution of gene regulatory regions is indeed an essential step in linking genotypes and phenotypes and in understanding the molecular mechanisms underlying evolutionary change. The common view is that expression traits (protein folding, expression timing, tissue localization and concentration) are under natural selection at the individual level. Here, we use a theoretical approach to show that, in addition, in dip- loid organisms, enhancer strength (i.e., the ability of enhancers to activate transcription) may increase in a runaway process due to competition for expression between homologous enhancer alleles. These alleles may be viewed as self-promoting genetic elements, as they spread without conferring a benefit at the individual level. They gain a selective advantage by getting associated to better genetic backgrounds: deleterious mutations are more efficiently purged when linked to stronger enhancers. This process, which has been entirely overlooked so far, may help understand the observed overrepresentation of cis-acting regu- latory changes in between-species phenotypic differences, and sheds a new light on investigating the contribution of gene expression evolution to adaptation. In this paper, the authors introduce and explore the concept of "enhancer runaway". The idea here is that in diploid species, the homologous chromosomes can carry different variants of the same enhancer. Selection operates on these two different enhancer alleles and under certain circumstances this can lead to competition for gene expression between the enhancers. In the first model the authors explore, there are two chromosomes each carrying a gene driven by a single enhancer. The authors assume that the overall expression level of the gene is tightly controlled by a feedback loop, such that deleterious mutations in either enhancer do not affect the overall expression level of the gene, and only effect the allelic imbalance (i.e. how much of the overall expression is contributed by the parental or the maternal allele). I recommend browsing through the whole paper for the full results, but the conclusion the authors draw is that, under some circumstances, selection continually favours stronger enhancers. This is in part because stronger enhancers lead to transiently imbalanced expression, with more of the protein product being generated from the gene on the same chromosome as the stron enhancer. During this period of imbalance, deleterious mutations in the linked gene have stronger effects (because they contribute to more than 50% of the total protein level) and are therefore more efficiently purged from the population. The result is that stronger enhancers find themselves more frequently linked to favourable genetic backgrounds. This means that enhancer alleles become locked in an evolutionary arms race where they continually co-evolve to be "stronger", a process that the authors term "enhancer runaway". Fig 1 The strengths of two enhancer alleles are represented by their ability to attract transcription factors. Four genotypes are represented: weaker enhancer homozygote (a), stronger enhancer homozygote (d) and enhancer locus heterozygotes (b) and (c). In enhancer locus heterozygotes, the stronger enhancer is either associated with the deleterious gene allele (b) or with the viable gene allele (c). Note that in this model the total amount of proteins produced is constant. Figure reproduced with modifications from Fyon, F. et al., Enhancer Runaway and the Evolution of Diploid Gene Expression. PLOS Genetics 11 e1005665 (2015). Of course not all genes are embedded in robust feeback loops that so stringently control their expression level. In other cases, mutations in enhancers may alter the overall expression level of a gene in addition to the imbalance between the two alleles. In this case, mutations in enhancers can decrease fitness by causing the gene to be expressed at an inappropriately high or low level. The authors consider two additional models in which gene expression level is allowed to vary and is subject to stabilizing selection (i.e. organisms that express the gene at a level higher or lower than the optimum are less fit). They find that stabilizing selection indeed slows down the process of enhancer runaway, but does not stop it completely. This is because other genes in the same pathway can co-evolve to maintain the correct gene dosage, or regulatory TFs can co-evolve to maintain the correct absolute expression levels. The aspect of the paper I found most interesting was the exploration of recombination rate. The authors show that enhancer runaway is stronger the more close the gene and enhancer lie on the genome, because the closer they lie the less frequently they undergo recombination. This linear distance effect is greater the stronger the selection pressure on the gene. The authors use this to make a prediction that genes undergoing the strongest selection should exhibit larger regulatory regions. This reminded me of some previous work examining the regulatory regions around master developmental transcription factors, which tend to be very large and devoid of other genes2. Fig 3 Ratio of fixation probabilities of mutations altering enhancer strength relative to that of a neutral mutation. In red, the mutant enhancer is three times stronger than the wild type; and in blue, three times weaker. Values are reported for the case where enhancer strength evolution does not alter overall protein expression, for various recombination rates between the enhancer and the gene (x-axis), for weak (squares) or strong selection (circles). Weaker enhancers (blue) are always selected against, while stronger enhancers (red) are selectively favored provided that they are closely linked to the gene, and disfavored otherwise. Figure reproduced from Fyon, F. et al., Enhancer Runaway and the Evolution of Diploid Gene Expression. PLOS Genetics 11 e1005665 (2015). The authors models allow them to explore the effect of different recombination rates on the enhancer runaway process. I think it would be very interesting to explore versions of these models where the recombination rate itself was under selective pressure. Since the authors show that enhancer runaway can actually decrease fitness for the organism, perhaps they could identify conditions in which increased distance between genes and enhancer would be favoured by evolution. In other words, perhaps part of the reason why many enhancers in mammalian genomes lie at fairly large distances from their target genes3 is to reduce these enhancer runaway effects. (Fyon, F. et al., Enhancer Runaway and the Evolution of Diploid Gene Expression. PLOS Genetics 11 e1005665 (2015).) ↩(Akalin A et al. Transcriptional features of genomic regulatory blocks. Genome Biol. 10: R38 (2009)) ↩(Lettice, L. A. et al. Disruption of a long-range cis-acting regulator for Shh causes preaxial polydactyly. PNAS. 99(11) 7548-7553.) ↩

Enhancer Journal Club: Functional annotation of native enhancers with a Cas9-histone demethylase fusion

By: Rob Beagrie

I really enjoyed Eric Mendenhall's 2013 paper in Nature Biotechnology1, where he was able to target LSD1 histone demethylase activity to endogenous enhancers using TALE fusion proteins (read my thoughts on that paper here). So I was very interested to see a similar approach published earlier this year in Nature Methods2. In this new paper, René Maehr's group at UMass attempt to identify functional endogenous enhancers using a Cas9:LSD1 fusion protein. Here's the abstract: Understanding of mammalian enhancers is limited by the lack of a technology to rapidly and thoroughly test the cell type-specific function. Here, we use a nuclease-deficient Cas9 (dCas9)-histone demethylase fusion to functionally characterize previously described and new enhancer elements for their roles in the embryonic stem cell state. Further, we distinguish the mechanism of action of dCas9-LSD1 at enhancers from previous dCas9-effectors. In the paper, the authors develop a Mouse ES cell line stably expressing their dCas9:LSD1, then target the LSD1 demethylase activity either to the Oct4 promoter or to the Oct4 distal enhancer by transfecting cells with guide RNAs specific to those locations. They find that targeting of LSD1 to the Oct4 promoter has no effect on Oct4 expression, whereas targeting to the Oct4 distal enhancer reduces Oct4 expression (as measured by immunofluorescence). This is in contrast to the activity of a Cas9:KRAB repressor fusion, which supresses Oct4 expression regardless of whether it is recruited to the promoter or the enhancer. They then go on on examine eight other putative enhancers of pluripotency related genes. Targeting LSD1 to these enhancers resulted in loss of pluripotency in four cases, including in an enhancer of Tbx3. Fig 1a Genomic organization of the Oct4 locus: ODE, distal enhancer; OPE, proximal enhancer; OPP, proximal promoter; ATAC-seq signal, accessible genome; red lines indicate sgRNAs binding sites. b, Percentage of colonies that do not contain OCT4-expressing cells (negative) or that contain a mixture of OCT4-negative and OCT4-positive cells (mixed) after sgRNA delivery. c, Luciferase activity either of the ODE or of enhancer 1 (Enh1). d, Genomic organization of the Tbx3 locus including H3K27ac. e, qPCR analysis for Tbx3 expression in Nm dCas9-effector mESCs treated with sgRNAs specific to an unrelated control genomic region (Ctrl), the putative Tbx3 distal enhancer (TDE) or the Tbx3 promoter (TPP). f, Heat map of gene expression microarray data from dCas9-effector mESCs with indicated sgRNAs. Reprinted by permission from Macmillan Publishers Ltd: Nature Methods 12, 401-403 (2015), copyright (2015). In the Mendenhall et al. 2013 paper, one question I had was whether the enzymatic activity of the LSD1 is required for enhancer repression. In this paper, the authors show that simultaneously treating cells with an LSD1 inhibitor called TCP abrogates the effect of the Tbx3 sgRNA on Tbx3 expression. I still think using a Cas9 fusion to catalytically inactive LSD1 is a cleaner control, but in the end I still find the data relatively convincing. The assumption is that these LSD1 fusions (both the TALE fusions from Mendenhall et al. and the Cas9 fusion presented here) are repressing enhancers by removing H3K4me1 and H3K4me2. Although reduced H3K4 methylation and H3K27 acetylation are observed in both cases, it still remains formally possible that LSD1 is actually demethylating some other target, which is upstream (or independent of) the histone demethylation. In the introduction, the authors hote that: a large number of genomic regions identified by genome-wide association studies of human disease fall within enhancer regions. Thus, there is a pressing need for technologies to functionally annotate cell type-specific enhancer elements that control cellular function. I fully agree with the authors on this point, and there is a lot of momentum within the field to develop reliable approaches which link sequence changes in enhancer regions to changes in the expression of target genes. In this light, the approach presented here is an interesting alternative, because the authors use functional assays (like looking for loss of alkaline phosphatase) rather than directly linking enhancers with target genes. I think the idea that they are getting at is that one could take a large number putative enhancers which overlap SNPs associated with e.g. Type II diabetes, and validate them by recruiting LSD1 and looking at some functional assay like glucose response. In this sense one might be able to validate that the enhancer is causally related to the disease in question without having to know exactly what gene or genes are the targets. Of course identifying the target gene is still important for developing possible therapeutic interventions. There have been some extremely successful screening approaches developed in the past few years based on sequencing of shRNAs. For example, in Kagey et al. (2010)3 Rick Young's lab treated cells with a pool of shRNAs, used FACS to sort them for Oct4 expression and then identified shRNAs over-represented in cells with low Oct4 expression. A similar approach could be applied to the Cas9:LSD1 system, where by transfecting with a pool of sgRNAs against thousands of putative enhancers, one could systematically identify essentially all enhancers of a particular gene. I think this could be a very powerful tool for really high-throughput enhancer identification, although it would of course be limited to cell types which are amenable to both transfection and FACS sorting. (Mendenhall, E. M. et al., Locus-specific editing of histone modifications at endogenous enhancers. Nat. Biotechnol. 31: 1133-1136 (2013).) ↩(Kearns N. A. et al., Functional annotation of native enhancers with a Cas9-histone demethylase fusion. Nat. Methods 12:401-403 (2015).) ↩(Kagey M. H. et al., Mediator and cohesin connect gene expression and chromatin architecture. Nature 467: 430-435 (2010).) ↩

Enhancer Journal Club: Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma

By: Rob Beagrie

I read a neat little paper in Nature just before Christmas about enhancer hijacking, which I think is a particularly interesting topic. The original paper can be found at: http://www.nature.com/nature/journal/v511/n7510/full/nature13379.html The abstract is as follows: Medulloblastoma is a highly malignant paediatric brain tumour currently treated with a combination of surgery, radiation and chemotherapy, posing a considerable burden of toxicity to the developing child. Genomics has illuminated the extensive intertumoral heterogeneity of medulloblastoma, identifying four distinct molecular subgroups. Group 3 and group 4 subgroup medulloblastomas account for most paediatric cases; yet, oncogenic drivers for these subtypes remain largely unidentified. Here we describe a series of prevalent, highly disparate genomic structural variants, restricted to groups 3 and 4, resulting in specific and mutually exclusive activation of the growth factor independent 1 family proto-oncogenes, GFI1 and GFI1B. Somatic structural variants juxtapose GFI1 or GFI1B coding sequences proximal to active enhancer elements, including super-enhancers, instigating oncogenic activity. Our results, supported by evidence from mouse models, identify GFI1 and GFI1B as prominent medulloblastoma oncogenes and implicate ‘enhancer hijacking’ as an efficient mechanism driving oncogene activation in a childhood cancer. In this paper, Northcott, Lee, Zichner and colleagues explore potential molecular drivers of medulloblastoma. Medulloblastomas are though to be divisible into four main subgroups. Groups 1 and 2 generally involve upregulation of sonic hedgehog or wingless pathways, whereas groups 3 and 4 have no known cause. By sequencing tumour DNA from medulloblastoma patients, they are able to find a particular region near of chromosome 9 which is frequently involved in structural variation (i.e. deletion, duplications, inversions). These structural variants appear to lead to a consistent upregulation of the GFI1B gene, occurring specifically in group 3 or group 4 tumours. Fig 1. a,b, Positions of recurrent structural variants in medulloblastoma. c, Expression of genes in affected region. d, GFI1B expression by medulloblastoma class. e, Class 3 & 4 medulloblastomas ranked by GFI1B expression. Reprinted by permission from Macmillan Publishers Ltd: Nature 511, 428–434 (2014), copyright (2014). For me, the most interesting part of this paper is the finding that these chromosomal rearrangments frequently juxtapose the GFI1B gene with a series of enhancers in or around the DDX31 gene. The authors suggest that this might be a case of "enhancer hijacking", where an enhancer that normally activates the expression one gene changes its target and causes expression of an unrelated gene in the wrong tissue or the wrong developmental stage. Fig 3. a, Epigenetic enhancer marks over the GFI1B/DDX31 locus. Adapted by permission from Macmillan Publishers Ltd: Nature 511, 428–434 (2014), copyright (2014). An interesting point to note in this figure: as the authors point out, the level of H3K27ac over the DDX31 enhancers actually seems to be higher in samples with a rearrangement in the region. Of course, this needs to be taken with a pinch of salt as you can't really compare the levels of enrichment in ChIP-seq data unless you're using some sort of fancy spike-in ChIP-seq 1 Assuming this quantitative difference is true, there may be an additional local factor which is increasing the activity of these enhancers before or after rearrangement. Interestingly, there doesn't seem to be much difference in the DNA methylation state of the enhancers in affected vs. non-affected samples. This could indicate that the enhancers are primed for further activation even in the absence of genomic rearrangements. It's possible that the increased acetylation of the enhancers could be downstream of GFI1B activation, partiularly if they are directly bound by GFI1B or one of its targets. Another alternative is that the increased activity of these enhancers is sufficient for GFI1B activation, in other words that the fully activated enhancers can activate GFI1B expression even in the absence of a genomic rearrangement. Before rearrangement, the enhancers are only separated from GFI1B by 370 kb, which is well within the range of activity seen for other enhancers. In the light of a few recent papers, perhaps the most interesting explanation is that DDX31 and GFI1B are normally separated by a topological domain boundary that gets removed or repositioned when the region is rearranged. There is some Hi-C data from an in vitro differentiation of hESCs to neural precursors, but as far as I can tell nobody has called topological domain positions from those datasets. In hESC datasets, GFI1B and DDX31 are within the same topological domain, which doesn't really support this interpretation: Hi-C data from human ES cells suggests that GFI1B and the DDX31 enhancers may occupy the same topological domain. Image modified from the 3D genome browser - http://www.3dgenome.org On the other hand, the whole region is syntenic between Human and Mouse, and in mouse NPCs there is a TAD boundary separating DDX31 and GFI1b. Perhaps someone could use CRISPR to delete that domain boundary in a mouse or mESC cell line and see if that causes overexpression of GFI1B. In the second half of the paper, the authors look at GFI1, which is a paralogue of GFI1B and find that GFI1 is also upregulated in a subset of medulloblastomas, and that this can be accompanied by various different chromosomal rearrangements juxtaposing GFI1 with active enhancers. One of the interesting things about these two genes is that they are both marked by H3K27me3 in unaffected samples. This means that their expression is likely to be repressed by Polycomb proteins. An interesting possibility, then, is that positioning of these genes near active enhancers is actually clearing Polycomb proteins from the GFI1/GFI1B promoters, which has been suggested as an mechanism of enhancer action in other systems 2 Fig 6 Model for enhancer hijacking in medulloblastoma. Genomic rearrangements juxtapose the GFI1B or GFI1 genes with either local or distal enhancer clusters, repressive H3K27me3 marks are lost from the respective gene promoters and GFI1/GFI1B are ectopically activated in inappropriate tissues. Adapted by permission from Macmillan Publishers Ltd: Nature 511, 428–434 (2014), copyright (2014). I think the idea of enhancer hijacking (or enhancer adoption, as it's also been called) is a very interesting one. The challenge remains to predict which enhancers might cause gene dysregulation when they are involved in genomic rearrangements, and crucially to determine which genes they are likely to target. (Orlando, D. A. et al. Quantitative ChIP-Seq Normalization Reveals Global Modulation of the Epigenome. Cell Rep. 9, 1163–1170 (2014).) ↩(Vernimmen, D. et al. Polycomb eviction as a new distant enhancer function. Genes Dev. 25, 1583–8 (2011).) ↩



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Email me at rob{at}beagrie.com

Rob Beagrie

MRC Clinical Sciences Centre

Hammersmith Hospital


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