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  • Other
    Spear S, Le Saux O, Mirza HB, Iyer N, Tyson K, Grundland Freile F, Walton JB, Woodman C, Jarvis S, Ennis DP, Aguirre Hernandez C, Xu Y, Spiliopoulou P, Brenton JD, Costa-Pereira AP, Cook DP, Vanderhyden BC, Keun HC, Triantafyllou E, Arnold JN, McNeish IAet al., 2024,

    Supplementary Figure legends from <i>PTEN</i> Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma

    <jats:p>&lt;p&gt;Legends to supplementary figures&lt;/p&gt;</jats:p>
  • Other
    Spear S, Le Saux O, Mirza HB, Iyer N, Tyson K, Grundland Freile F, Walton JB, Woodman C, Jarvis S, Ennis DP, Aguirre Hernandez C, Xu Y, Spiliopoulou P, Brenton JD, Costa-Pereira AP, Cook DP, Vanderhyden BC, Keun HC, Triantafyllou E, Arnold JN, McNeish IAet al., 2024,

    Table S1 Antibodies from &lt;i&gt;PTEN&lt;/i&gt; Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma

    <jats:p>&lt;p&gt;Table of antibodies used in flow cytometry and immunohistochemistry.&lt;/p&gt;</jats:p>
  • Other
    Spear S, Le Saux O, Mirza HB, Iyer N, Tyson K, Grundland Freile F, Walton JB, Woodman C, Jarvis S, Ennis DP, Aguirre Hernandez C, Xu Y, Spiliopoulou P, Brenton JD, Costa-Pereira AP, Cook DP, Vanderhyden BC, Keun HC, Triantafyllou E, Arnold JN, McNeish IAet al., 2024,

    Supplementary Figures from &lt;i&gt;PTEN&lt;/i&gt; Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma

    <jats:p>&lt;p&gt;8 supplementary figures&lt;/p&gt;</jats:p>
  • Other
    Spear S, Le Saux O, Mirza HB, Iyer N, Tyson K, Grundland Freile F, Walton JB, Woodman C, Jarvis S, Ennis DP, Aguirre Hernandez C, Xu Y, Spiliopoulou P, Brenton JD, Costa-Pereira AP, Cook DP, Vanderhyden BC, Keun HC, Triantafyllou E, Arnold JN, McNeish IAet al., 2024,

    Table S3 HMOX1-hi gene expression from &lt;i&gt;PTEN&lt;/i&gt; Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma

    <jats:p>&lt;p&gt;Up- and down-regulated differentially expressed genes in HMOX1-hi human macrophages&lt;/p&gt;</jats:p>
  • Journal article
    Cooper N, Bussel JB, Kazmierczak M, Miyakawa Y, Cluck S, Garcia RL, Haier B, Lavrov A, Singh P, Snipes R, Kuter DJet al., 2024,

    Inhibition of FcRn with rozanolixizumab in adults with immune thrombocytopenia: Two randomised, double-blind, placebo-controlled phase 3 studies and their open-label extension

    , BRITISH JOURNAL OF HAEMATOLOGY, ISSN: 0007-1048
  • Journal article
    Antariksa NF, Di Antonio M, 2024,

    The emerging roles of multimolecular G-quadruplexes in transcriptional regulation and chromatin organization

    , Accounts of Chemical Research, Vol: 57, Pages: 3397-3406, ISSN: 0001-4842

    ConspectusThe ability of genomic DNA to adopt non-canonical secondary structures known as G-quadruplexes (G4s) under physiological conditions has been recognized for its potential regulatory function of various biological processes. Among those, transcription has recently emerged as a key process that can be heavily affected by G4 formation, particularly when these structures form at gene promoters. While the presence of G4s within gene promoters has been traditionally associated with transcriptional inhibition, in a model whereby G4s act as roadblocks to polymerase elongation, recent genomics experiments have revealed that the regulatory role of G4s in transcription is more complex than initially anticipated. Indeed, earlier studies linking G4-formation and transcription mainly relied on small-molecule ligands to stabilize and promote G4s, which might lead to disruption of protein–DNA interactions and local environments and, therefore, does not necessarily reflect the endogenous function of G4s at gene promoters. There is now strong evidence pointing toward G4s being associated with transcriptional enhancement, rather than repression, through multifaceted mechanisms such as recruitment of key transcriptional proteins, molding of chromatin architecture, and mode of phase separation.In this Account, we explore pivotal findings from our research on a particular subset of G4s, namely, those formed through interactions between distant genomic locations or independent nucleic acid strands, referred to as multimolecular G4s (mG4s), and discuss their active role in transcriptional regulation. We present our recent studies suggesting that the formation of mG4s may positively regulate transcription by inducing phase-separation and selectively recruiting chromatin-remodeling proteins. Our work highlighted how mG4-forming DNA and RNA sequences can lead to liquid–liquid phase separation (LLPS) in the absence of any protein. This discovery provided new insights into
  • Journal article
    Chowdhury SR, Sirotich E, Guyatt G, Gill D, Modi D, Venier LM, Mahamad S, Chowdhury MR, Eisa K, Beck CE, Breakey VR, de Wit K, Porter S, Webert KE, Cuker A, O'Connor C, MacWhirter-DiRaimo J, Yan JW, Manski C, Kelton JG, Kang M, Strachan G, Hassan Z, Pruitt B, Pai M, Grace RF, Paynter D, Charness J, Cooper N, Fein S, Agarwal A, Nazaryan H, Siddiqui I, Leong R, Pallapothu S, Wen A, Xu E, Liu B, Shafiee A, Rathod P, Kwon H, Dookie J, Zeraatkar D, Thabane L, Couban R, Arnold DMet al., 2024,

    Treatment of Critical Bleeds in Patients With Immune Thrombocytopenia: A Systematic Review

    , EUROPEAN JOURNAL OF HAEMATOLOGY, ISSN: 0902-4441
  • Journal article
    Turner P, 2024,

    Time to take it 'out' side: delabelling allergy to penicillin and other beta-lactams in children and young people

    , ARCHIVES OF DISEASE IN CHILDHOOD, ISSN: 0003-9888
  • Journal article
    Troiani A, Martinez M, Ward C, Benartzi CW, Pinato DJ, Sharma Ret al., 2024,

    Safety and efficacy of itacitinib, a selective JAK1 inhibitor, in advanced hepatocellular cancer: Phase 1b trial (JAKAL)

    , FUTURE ONCOLOGY, Vol: 20, Pages: 2839-2847, ISSN: 1479-6694
  • Journal article
    Murphy A, Beardall W, Rei M, Phuycharoen M, Skene Net al., 2024,

    Predicting cell type-specific epigenomic profiles accounting for distal genetic effects

    , Nature Communications, Vol: 15, ISSN: 2041-1723

    Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we introduce Enformer Celltyping, a deep learning model which incorporates distal effects of DNA interactions, up to 100,000 base-pairs away, to predict epigenetic signals in previously unseen cell types. Using DNA and chromatin accessibility data for epigenetic imputation, Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits.

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