Biology subjects

Tooze, R. M.

Publications and source records attributed to Tooze, R. M..

3 recordsLinked to original sources

bioRxiv2023

Integration of chromatin accessibility and gene expression data with cisREAD reveals a switch from PU.1/SPIB-driven to AP-1-driven gene regulation during B cell activation

Human B cell differentiation into antibody secreting plasma cells is a critical process in the adaptive immune response, whose regulation at the genetic level remains incompletely understood. To reveal the temporal sequence of transcription factor driven cellular changes we generated chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) data from in vitro differentiation of human B cells into plasma cells using a published protocol for differentiation up to the plasma cell stage. Using a new computational method, cisREAD (cis-Regulatory Elements Across Differentiation), we defined a core set of cis-regulatory elements that are confidently linked to dynamic transcription factor binding and changes in gene expression across the mature B lineage. Here we describe how cisREAD identifies regulatory element communities, based on chromatin accessibility and transcription factor co-occupancy, and prioritizes those whose accessibility predicts differential gene expression through regularized regression models. Through downstream analyses of cisREAD-predicted regulation, we show how transcription factors reshape B cell epigenomes and transcriptomes in response to differentiation stimuli. Our results confirm roles for OCT2, IRF4 and PRDM1 in plasma cell differentiation, and reveal that a shift from PU.1/SPIB-driven to AP-1-driven gene regulation is a key determinant of B cell activation. GRAPHICAL ABSTRACT O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY Integration of epigenomic and transcriptomic datasets with the cisREAD method, followed by clustering and network analysis, reveals that gene regulation shifts from PU.1/SPIB to AP-1 upon B cell activation. C_FIG_DISPLAY

bioinformatics↗

bioRxiv2021

APRIL drives a co-ordinated but diverse response as a foundation for plasma cell longevity.

Antibody secreting cells (ASCs) survive in niche microenvironments, but cellular responses driven by particular niche signals are incompletely defined. The TNF superfamily member APRIL provides a niche signal that can support the transition of transitory plasmablasts into long-lived plasma cells. Here we explore how APRIL helps to establish the biological programs that promote life in the niche, by studying the initial response of primary human plasmablast to APRIL. Under conditions allowing the maturation of ex vivo or in vitro generated plasmablasts, we find that APRIL drives activation of ERK, p38 and JNK. This is accompanied by a classical NF{kappa}B response. Under these conditions induction of AKT phosphorylation is also observed with similar kinetics, paralleled by FOXO1 phosphorylation and nuclear exclusion. Time course gene expression data resolve the downstream co-ordinated transcriptional response. The APRIL-signal propagates via immediate early genes and classical NF{kappa}B responsive targets to converge onto modules of MYC- and OCT2-regulated gene expression linked to cell growth, as well as leading to enhanced expression of ICAM1 and SQSTM1 associated with adhesion and metabolic/stress responses. Thus, APRIL drives a combination of multiple transcriptional programs that co-ordinate cell growth, stress response and adhesion in human ASCs, providing a broad foundation to support plasma cell longevity.

immunology↗

bioRxiv2018

Defining common principles of gene co-expression refines molecular stratification in cancer

Cancers converge onto shared patterns that arise from constraints placed by the biology of the originating cell lineage and microenvironment on recurrent programs driven by oncogenic events. This structure should be transferable to molecular stratification. We exploit expression data resources and a parsimonious and computationally efficient network analysis method to define consistent expression modules in colon and breast cancer. Comparison between cancer types identifies principles of gene co-expression: cancer hallmarks, functional and structural gene batteries, copy number variation and biology of originating lineage. Mapping outcome data at gene and module level onto these networks generates a detailed interactive resource. Testing the utility of the resulting modules in TCGA data defines specific associations of module expression with mutation state, identifying striking associations such as mast cell gene expression and mutation pattern in breast cancer. These analyses provide evidence for a generalizable framework to enhance molecular stratification in cancer.

bioinformatics↗