John Lambourne
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View article: Minimal activation of the p53 DNA damage response by a modular cytosine base editor enables effective multiplexed gene knockout in induced pluripotent stem cells
Minimal activation of the p53 DNA damage response by a modular cytosine base editor enables effective multiplexed gene knockout in induced pluripotent stem cells Open
Precise genome editing of induced pluripotent stem cells (iPSC) holds great promise for engineering advanced cell therapies. CRISPR-Cas systems have been widely adopted in genome engineering applications, however their dependence on genoto…
View article: An aptamer-mediated base editing platform for simultaneous knock-in and multiple gene knockout for allogeneic CAR-T cells generation
An aptamer-mediated base editing platform for simultaneous knock-in and multiple gene knockout for allogeneic CAR-T cells generation Open
Gene editing technologies hold promise for enabling the next generation of adoptive cellular therapies. Conventional gene editing platforms that rely on nuclease activity, such as Clustered regularly interspaced short palindromic repeats-C…
View article: Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes
Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes Open
Background This work is aimed at improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis by generating a multi-omic disease signature. Methods/results We combined classic plasma biochemi…
View article: Development and Characterization of a Modular CRISPR and RNA Aptamer Mediated Base Editing System
Development and Characterization of a Modular CRISPR and RNA Aptamer Mediated Base Editing System Open
Conventional CRISPR approaches for precision genome editing rely on the introduction of DNA double-strand breaks (DSB) and activation of homology-directed repair (HDR), which is inherently genotoxic and inefficient in somatic cells. The de…
View article: Cell type-specific novel long non-coding RNA and circular RNA in the BLUEPRINT hematopoietic transcriptomes atlas
Cell type-specific novel long non-coding RNA and circular RNA in the BLUEPRINT hematopoietic transcriptomes atlas Open
Transcriptional profiling of hematopoietic cell subpopulations has helped to characterize the developmental stages of the hematopoietic system and the molecular bases of malignant and non-malignant blood diseases. Previously, only the gene…
View article: Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes
Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes Open
Improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis are ongoing healthcare challenges. Using plasma biomarkers analysis coupled with the transcriptional and epigenetic characterisati…
View article: Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Background Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4 + T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates i…
View article: Additional file 11: Table S8a. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 11: Table S8a. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
GTF file with definitions for all Ensembl 75 genomic features plus CD4-specific regulatory regions inferred from chromatin states. These regulatory regions have been named with identifiers containing a CD4R prefix, assigned a regulatory bi…
View article: Additional file 13: Table S8c. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 13: Table S8c. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Whole-genome segmentation of non-activated and activated CD4 T cells into 15 states obtained from a CHROMHMM analysis using ChIP-seq data for non-activated CD4+ T cells. (GZ 1520 kb)
View article: Additional file 10: Table S7b. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 10: Table S7b. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
As above, complete results. (GZ 37 kb)
View article: Additional file 14: Table S9. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 14: Table S9. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Genotypes for donors in the IL2RA ASE experiment across SNP groups A, C, D, E, F. (XLSX 12 kb)
View article: Additional file 8: Table S6b. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 8: Table S6b. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Results of GWAS summary statistic fine-mapping. (GZ 2833 kb)
View article: Additional file 12: Table S8b. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 12: Table S8b. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Whole-genome segmentation of non-activated and activated CD4 T cells into 15 states obtained from a CHROMHMM analysis using ChIP-seq data for activated CD4+ T cells. (GZ 1551 kb)
View article: Additional file 4: Table S3. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 4: Table S3. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Baited HindIII fragments used for capture of Hi-C libraries, annotated with Ensembl annotated genes. (GZ 572 kb)
View article: Additional file 3: Table S2. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 3: Table S2. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Results of differential expression analysis on RNA-seq data. Features are defined in the GTF file in Additional file 11: Table S8a. (GZ 835 kb)
View article: Additional file 1: Table S1. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 1: Table S1. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Gene modules inferred from WGCNA analysis of microarray time-course. Expression fold changes and associated false discovery rates (adjusted p values) are from RNA-seq data at the 4-h timepoint. (GZ 278 kb)
View article: Additional file 7: Table S6a. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 7: Table S6a. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Results of ImmunoChip fine-mapping by GUESSFM. (GZ 2833 kb)
View article: Additional file 9: Table S7a. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 9: Table S7a. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Autoimmune disease COGS gene prioritisation. Overall COGS gene scores (COGS_Overall_Gene_Score) for each gene and autoimmune disease are shown together with the prioritised category and score associated with that category (COGS_Category_Ge…
View article: Additional file 6: Table S5. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 6: Table S5. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Summary of GWAS data used. ‘type’ indicates whether the trait was quantitative (QUANT) or case/control (CC). For CC, ‘cases’ and ‘controls’ columns represent the number of individuals included in the study, while for QUANT, the number of i…
View article: Additional file 15: Table S10. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 15: Table S10. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
Read counts for each allele at the IL2RA ASE experiment. The column Expt denotes sample id; time, the timepoint (0, 120, 240Â min); stim, the condition (genomic DNA, time0 cDNA, stimulated or unstimulated cells cDNA). (GZ 4 kb)
View article: Additional file 5: Table S4. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Additional file 5: Table S4. of Chromosome contacts in activated T cells identify autoimmune disease candidate genes Open
PCHi-C interactions called with the CHiCAGO pipeline. Annotation for baited fragments is given in Additional file 4: Table S3. PIRs are called other ends (‘oe’). CHICAGO scores for activated (‘Total_CD4_Activated’) and non-activated (‘Tota…
View article: Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells Open
Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in t…
View article: Bacterial infection remodels the DNA methylation landscape of human dendritic cells
Bacterial infection remodels the DNA methylation landscape of human dendritic cells Open
DNA methylation is an epigenetic mark thought to be robust to environmental perturbations on a short time scale. Here, we challenge that view by demonstrating that the infection of human dendritic cells (DCs) with a live pathogenic bacteri…
View article: Erratum: Characterization of functional methylomes by next-generation capture sequencing identifies novel disease-associated variants
Erratum: Characterization of functional methylomes by next-generation capture sequencing identifies novel disease-associated variants Open
Nature Communications 6: Article number: 7211 (2015); Published 29 May 2015; Updated 29 July 2015. The HTML version of this Article incorrectly included all members of The Multiple Tissue Human Expression Resource Consortium in the list of…
View article: Bacterial Infection Remodels the DNA Methylation Landscape of Human Dendritic Cells
Bacterial Infection Remodels the DNA Methylation Landscape of Human Dendritic Cells Open
DNA methylation is thought to be robust to environmental perturbations on a short time scale. Here, we challenge that view by demonstrating that the infection of human dendritic cells (DCs) with a pathogenic bacteria is associated with rap…