We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an ...agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein-protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here ...we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world.
Diffuse intrinsic pontine glioma (DIPG) is an aggressive brain tumor that is located in the pons and primarily affects children. Nearly 80% of DIPGs harbor mutations in histone H3 genes, wherein ...lysine 27 is substituted with methionine (H3K27M). H3K27M has been shown to inhibit polycomb repressive complex 2 (PRC2), a multiprotein complex responsible for the methylation of H3 at lysine 27 (H3K27me), by binding to its catalytic subunit EZH2. Although DIPGs with the H3K27M mutation show global loss of H3K27me3, several genes retain H3K27me3. Here we describe a mouse model of DIPG in which H3K27M potentiates tumorigenesis. Using this model and primary patient-derived DIPG cell lines, we show that H3K27M-expressing tumors require PRC2 for proliferation. Furthermore, we demonstrate that small-molecule EZH2 inhibitors abolish tumor cell growth through a mechanism that is dependent on the induction of the tumor-suppressor protein p16
. Genome-wide enrichment analyses show that the genes that retain H3K27me3 in H3K27M cells are strong polycomb targets. Furthermore, we find a highly significant overlap between genes that retain H3K27me3 in the DIPG mouse model and in human primary DIPGs expressing H3K27M. Taken together, these results show that residual PRC2 activity is required for the proliferation of H3K27M-expressing DIPGs, and that inhibition of EZH2 is a potential therapeutic strategy for the treatment of these tumors.
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IJS, NUK, SBMB, UL, UM, UPUK
DNA methylation is tightly regulated throughout mammalian development, and altered DNA methylation patterns are a general hallmark of cancer. The methylcytosine dioxygenase TET2 is frequently mutated ...in hematological disorders, including acute myeloid leukemia (AML), and has been suggested to protect CG dinucleotide (CpG) islands and promoters from aberrant DNA methylation. In this study, we present a novel Tet2-dependent leukemia mouse model that closely recapitulates gene expression profiles and hallmarks of human AML1-ETO-induced AML. Using this model, we show that the primary effect of Tet2 loss in preleukemic hematopoietic cells is progressive and widespread DNA hypermethylation affecting up to 25% of active enhancer elements. In contrast, CpG island and promoter methylation does not change in a Tet2-dependent manner but increases relative to population doublings. We confirmed this specific enhancer hypermethylation phenotype in human AML patients with TET2 mutations. Analysis of immediate gene expression changes reveals rapid deregulation of a large number of genes implicated in tumorigenesis, including many down-regulated tumor suppressor genes. Hence, we propose that TET2 prevents leukemic transformation by protecting enhancers from aberrant DNA methylation and that it is the combined silencing of several tumor suppressor genes in TET2 mutated hematopoietic cells that contributes to increased stem cell proliferation and leukemogenesis.
We currently have limited knowledge of the involvement of long non-coding RNAs (lncRNAs) in normal cellular processes and pathologies. Here, we identify and characterize SNHG5 as a stable cytoplasmic ...lncRNA with up-regulated expression in colorectal cancer. Depletion of SNHG5 induces cell cycle arrest and apoptosis in vitro and limits tumour outgrowth in vivo, whereas SNHG5 overexpression counteracts oxaliplatin-induced apoptosis. Using an unbiased approach, we identify 121 transcript sites interacting with SNHG5 in the cytoplasm. Importantly, knockdown of key SNHG5 target transcripts, including SPATS2, induces apoptosis and thus mimics the effect seen following SNHG5 depletion. Mechanistically, we suggest that SNHG5 stabilizes the target transcripts by blocking their degradation by STAU1. Accordingly, depletion of STAU1 rescues the apoptosis induced after SNHG5 knockdown. Hence, we characterize SNHG5 as a lncRNA promoting tumour cell survival in colorectal cancer and delineate a novel mechanism in which a cytoplasmic lncRNA functions through blocking the action of STAU1.
Cancer sequencing studies have implicated regulators of pre-mRNA splicing as important disease determinants in acute myeloid leukemia (AML), but the underlying mechanisms have remained elusive. We ...hypothesized that "non-mutated" splicing regulators may also play a role in AML biology and therefore conducted an in vivo shRNA screen in a mouse model of CEBPA mutant AML. This has led to the identification of the splicing regulator RBM25 as a novel tumor suppressor. In multiple human leukemic cell lines, knockdown of RBM25 promotes proliferation and decreases apoptosis. Mechanistically, we show that RBM25 controls the splicing of key genes, including those encoding the apoptotic regulator BCL-X and the MYC inhibitor BIN1. This mechanism is also operative in human AML patients where low RBM25 levels are associated with high MYC activity and poor outcome. Thus, we demonstrate that RBM25 acts as a regulator of MYC activity and sensitizes cells to increased MYC levels.
Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional ...programs that differ between cancer and corresponding normal cells and has not been efficient in identifying expression changes fundamental to disease etiology. Here we present a method that facilitates the comparison of any cancer sample to its nearest normal cellular counterpart, using acute myeloid leukemia (AML) as a model. We first generated a gene expression-based landscape of the normal hematopoietic hierarchy, using expression profiles from normal stem/progenitor cells, and next mapped the AML patient samples to this landscape. This allowed us to identify the closest normal counterpart of individual AML samples and determine gene expression changes between cancer and normal. We find the cancer vs normal method (CvN method) to be superior to conventional methods in stratifying AML patients with aberrant karyotype and in identifying common aberrant transcriptional programs with potential importance for AML etiology. Moreover, the CvN method uncovered a novel poor-outcome subtype of normal-karyotype AML, which allowed for the generation of a highly prognostic survival signature. Collectively, our CvN method holds great potential as a tool for the analysis of gene expression profiles of cancer patients.
•This study describes a method for the comparison of gene expression data of any type of cancer cells with their corresponding normal cells.•Our analyses reveal novel disease entities, identify common deregulated transcriptional networks, and predict survival.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Inflammatory bowel disease (IBD) is a chronic intestinal disorder, with two main types: Crohn's disease (CD) and ulcerative colitis (UC), whose molecular pathology is not well understood. The ...majority of IBD-associated SNPs are located in non-coding regions and are hard to characterize since regulatory regions in IBD are not known. Here we profile transcription start sites (TSSs) and enhancers in the descending colon of 94 IBD patients and controls. IBD-upregulated promoters and enhancers are highly enriched for IBD-associated SNPs and are bound by the same transcription factors. IBD-specific TSSs are associated to genes with roles in both inflammatory cascades and gut epithelia while TSSs distinguishing UC and CD are associated to gut epithelia functions. We find that as few as 35 TSSs can distinguish active CD, UC, and controls with 85% accuracy in an independent cohort. Our data constitute a foundation for understanding the molecular pathology, gene regulation, and genetics of IBD.
Dynamic shifts in transcription factor binding are central to the regulation of biological processes by allowing rapid changes in gene transcription. However, very few genome-wide studies have ...examined how transcription factor occupancy is coordinated temporally in vivo in higher animals. Here, we quantified the genome-wide binding patterns of two key hepatocyte transcription factors, CEBPA and CEBPB (also known as C/EBPalpha and C/EBPbeta), at multiple time points during the highly dynamic process of liver regeneration elicited by partial hepatectomy in mouse. Combining these profiles with RNA polymerase II binding data, we find three temporal classes of transcription factor binding to be associated with distinct sets of regulated genes involved in the acute phase response, metabolic/homeostatic functions, or cell cycle progression. Moreover, we demonstrate a previously unrecognized early phase of homeostatic gene expression prior to S-phase entry. By analyzing the three classes of CEBP bound regions, we uncovered mutually exclusive sets of sequence motifs, suggesting temporal codes of CEBP recruitment by differential cobinding with other factors. These findings were validated by sequential ChIP experiments involving a panel of central transcription factors and/or by comparison to external ChIP-seq data. Our quantitative investigation not only provides in vivo evidence for the involvement of many new factors in liver regeneration but also points to similarities in the circuitries regulating self-renewal of differentiated cells. Taken together, our work emphasizes the power of global temporal analyses of transcription factor occupancy to elucidate mechanisms regulating dynamic biological processes in complex higher organisms.
Transcription factors PU.1 and CEBPA are required for the proper coordination of enhancer activity during granulocytic-monocytic (GM) lineage differentiation to form myeloid cells. However, precisely ...how these factors control the chronology of enhancer establishment during differentiation is not known. Through integrated analyses of enhancer dynamics, transcription factor binding, and proximal gene expression during successive stages of murine GM-lineage differentiation, we unravel the distinct kinetics by which PU.1 and CEBPA coordinate GM enhancer activity. We find no evidence of a pioneering function of PU.1 during late GM-lineage differentiation. Instead, we delineate a set of enhancers that gain accessibility in a CEBPA-dependent manner, suggesting a pioneering function of CEBPA. Analyses of Cebpa null bone marrow demonstrate that CEBPA controls PU.1 levels and, unexpectedly, that the loss of CEBPA results in an early differentiation block. Taken together, our data provide insights into how PU.1 and CEBPA functionally interact to drive GM-lineage differentiation.
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•Characterization of the enhancer landscape during GM-lineage differentiation•CEBPA controls PU.1 levels•PU.1 binding to enhancers is diminished in Cebpa null GM progenitors•Loss of CEBPA leads to a differentiation block upstream of CMPs/preGMs
Granulocytic-monocytic differentiation is governed by the PU.1 and CEBPA transcription factors (TFs). Here, Pundhir et al. use histone and TF ChIP-seq to demonstrate an unexpected role for CEBPA in controlling PU.1 function. They show that loss of CEBPA results in an early GM lineage differentiation block upstream of CMPs/preGMs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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