Identification of ADGRE5 as discriminating MYC target between Burkitt lymphoma and diffuse large B-cell lymphoma.

BACKGROUND: MYC is a heterogeneously expressed transcription factor that plays a multifunctional role in many biological processes such as cell proliferation and differentiation. It is also associated with many types of cancer including the malignant lymphomas. There are two types of aggressive B-cell lymphoma, namely Burkitt lymphoma (BL) and a subgroup of diffuse large cell lymphoma (DLBCL), which both carry MYC translocations and overexpress MYC but both differ significantly in their clinical outcome. In DLBCL, MYC translocations are associated with an aggressive behavior and poor outcome, whereas MYC-positive BL show a superior outcome. METHODS: To shed light on this phenomenon, we investigated the different modes of actions of MYC in aggressive B-cell lymphoma cell lines subdivided into three groups: (i) MYC-positive BL, (ii) DLBCL with MYC translocation (DLBCLpos) and (iii) DLBCL without MYC translocation (DLBCLneg) for control. In order to identify genome-wide MYC-DNA binding sites a chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) was performed. In addition, ChIP-Seq for H3K4me3 was used for determination of genomic regions accessible for transcriptional activity. These data were supplemented with gene expression data derived from RNA-Seq. RESULTS: Bioinformatics integration of all data sets revealed different MYC-binding patterns and transcriptional profiles in MYC-positive BL and DLBCL cell lines indicating different functional roles of MYC for gene regulation in aggressive B-cell lymphomas. Based on this multi-omics analysis we identified ADGRE5 (alias CD97) - a member of the EGF-TM7 subfamily of adhesion G protein-coupled receptors - as a MYC target gene, which is specifically expressed in BL but not in DLBCL regardless of MYC translocation. CONCLUSION: Our study describes a diverse genome-wide MYC-DNA binding pattern in BL and DLBCL cell lines with and without MYC translocations. Furthermore, we identified ADREG5 as a MYC target gene able to discriminate between BL and DLBCL irrespectively of the presence of MYC breaks in DLBCL. Since ADGRE5 plays an important role in tumor cell formation, metastasis and invasion, it might also be instrumental to better understand the different pathobiology of BL and DLBCL and help to explain discrepant clinical characteristics of BL and DLBCL.

Cooperative STAT/NF-κB signaling regulates lymphoma metabolic reprogramming and aberrant GOT2 expression.

Knowledge of stromal factors that have a role in the transcriptional regulation of metabolic pathways aside from c-Myc is fundamental to improvements in lymphoma therapy. Using a MYC-inducible human B-cell line, we observed the cooperative activation of STAT3 and NF-κB by IL10 and CpG stimulation. We show that IL10 + CpG-mediated cell proliferation of MYClow cells depends on glutaminolysis. By 13C- and 15N-tracing of glutamine metabolism and metabolite rescue experiments, we demonstrate that GOT2 provides aspartate and nucleotides to cells with activated or aberrant Jak/STAT and NF-κB signaling. A model of GOT2 transcriptional regulation is proposed, in which the cooperative phosphorylation of STAT3 and direct joint binding of STAT3 and p65/NF-κB to the proximal GOT2 promoter are important. Furthermore, high aberrant GOT2 expression is prognostic in diffuse large B-cell lymphoma underscoring the current findings and importance of stromal factors in lymphoma biology.

Comprehensive Metaboproteomics of Burkitt's and Diffuse Large B-Cell Lymphoma Cell Lines and Primary Tumor Tissues Reveals Distinct Differences in Pyruvate Content and Metabolism.

Burkitt's lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL) are pathologically and clinically distinct subtypes of aggressive non-Hodgkin B-cell lymphoma. To learn more about their biology, we employed metabolomic and proteomic methods to study both established cell lines as well as cryopreserved and formalin-fixed paraffin-embedded (FFPE) tissue sections of BL and DLBCL. Strikingly, NMR analyses revealed DLBCL cell lines to produce and secrete significantly (padj = 1.72 × 10-22) more pyruvic acid than BL cell lines. This finding could be reproduced by targeted GC/MS analyses of cryopreserved tissue sections of BL and DLBCL cases. Enrichment analysis of an overlapping set of N = 2315 proteins, that had been quantified by nanoLC-SWATH-MS in BL and DLBCL cultured cells and cryosections, supported the observed difference in pyruvic acid content, as glycolysis and pyruvate metabolism were downregulated, while one-carbon metabolism was upregulated in BL compared to DLBCL. Furthermore, 92.1% of the overlapping significant proteins showed the same direction of regulation in cryopreserved and FFPE material. Proteome data are available via ProteomeXchange with identifier PXD004936.

External calibration with Drosophila whole-cell spike-ins delivers absolute mRNA fold changes from human RNA-Seq and qPCR data.

Gene expression measurements are typically performed on a fixed-weight aliquot of RNA, which assumes that the total number of transcripts per cell stays nearly constant across all conditions. In cases where this assumption does not hold (e.g., when comparing cell types with different cell sizes) the expression data provide a distorted view of cellular events. Assuming constant numbers of total transcripts, increases in expression of some RNAs must be compensated for by decreases in expression of others. Therefore, we propose calibrating gene expression data to an external reference point, the number of cells in the sample, using whole-cell spike-ins. In a systematic dilution experiment, we mixed varying numbers of human cells with fixed numbers of Drosophila melanogaster cells and scaled the expression levels of the human genes relative to those of the Drosophila genes. This approach restored the original gene expression ratios generated by the dilutions. We then used Drosophila whole-cell spike-ins to uncover non-symmetric gene expression changes, in this case much larger numbers of induced than repressed genes, under perturbations of the human cell line P493-6. Drosophila whole-cell spike-ins are an experimentally and computationally easy and low-priced method to derive mRNA fold changes of absolute abundances from RNA sequencing (RNA-Seq) and quantitative real-time PCR (qPCR) data.

Synergy of interleukin 10 and toll-like receptor 9 signalling in B cell proliferation: Implications for lymphoma pathogenesis.

A network of autocrine and paracrine signals defines B cell homeostasis and is thought to be involved in transformation processes. Investigating interactions of these microenvironmental factors and their relation to proto-oncogenes as c-Myc (MYC) is fundamental to understand the biology of B cell lymphoma. Therefore, B cells with conditional MYC expression were stimulated with CD40L, insulin-like growth factor 1, α-IgM, Interleukin-10 (IL10) and CpG alone or in combination. The impact of forty different interventions on cell proliferation was investigated in MYC deprived cells and calculated by linear regression. Combination of CpG and IL10 led to a strong synergistic activation of cell proliferation (S-phase/doubling of total cell number) comparable to cells with high MYC expression. A synergistic up-regulation of CDK4, CDK6 and CCND3 expression by IL10 and CpG treatment was causal for this proliferative effect as shown by qRT-PCR analysis and inhibition of the CDK4/6 complex by PD0332991. Furthermore, treatment of stimulated MYC deprived cells with MLN120b, ACHP, Pyridone 6 or Ruxolitinib showed that IL10/CpG induced proliferation and CDK4 expression were JAK/STAT3 and IKK/NF-κB dependent. This was further supported by STAT3 and p65/RELA knockdown experiments, showing strongest effects on cell proliferation and CDK4 expression after double knockdown. Additionally, chromatin immunoprecipitation revealed a dual binding of STAT3 and p65 to the proximal promotor of CDK4 after IL10/CpG treatment. Therefore, the observed synergism of IL10R and TLR9 signalling was able to induce proliferation in a comparable way as aberrant MYC and might play a role in B cell homeostasis or transformation.

Natural variation of root exudates in Arabidopsis thaliana-linking metabolomic and genomic data.

Many metabolomics studies focus on aboveground parts of the plant, while metabolism within roots and the chemical composition of the rhizosphere, as influenced by exudation, are not deeply investigated. In this study, we analysed exudate metabolic patterns of Arabidopsis thaliana and their variation in genetically diverse accessions. For this project, we used the 19 parental accessions of the Arabidopsis MAGIC collection. Plants were grown in a hydroponic system, their exudates were harvested before bolting and subjected to UPLC/ESI-QTOF-MS analysis. Metabolite profiles were analysed together with the genome sequence information. Our study uncovered distinct metabolite profiles for root exudates of the 19 accessions. Hierarchical clustering revealed similarities in the exudate metabolite profiles, which were partly reflected by the genetic distances. An association of metabolite absence with nonsense mutations was detected for the biosynthetic pathways of an indolic glucosinolate hydrolysis product, a hydroxycinnamic acid amine and a flavonoid triglycoside. Consequently, a direct link between metabolic phenotype and genotype was detected without using segregating populations. Moreover, genomics can help to identify biosynthetic enzymes in metabolomics experiments. Our study elucidates the chemical composition of the rhizosphere and its natural variation in A. thaliana, which is important for the attraction and shaping of microbial communities.

A statistical approach to virtual cellular experiments: improved causal discovery using accumulation IDA (aIDA).

MOTIVATION: We address the following question: Does inhibition of the expression of a gene X in a cellular assay affect the expression of another gene Y? Rather than inhibiting gene X experimentally, we aim at answering this question computationally using as the only input observational gene expression data. Recently, a new statistical algorithm called Intervention calculus when the Directed acyclic graph is Absent (IDA), has been proposed for this problem. For several biological systems, IDA has been shown to outcompete regression-based methods with respect to the number of true positives versus the number of false positives for the top 5000 predicted effects. Further improvements in the performance of IDA have been realized by stability selection, a resampling method wrapped around IDA that enhances the discovery of true causal effects. Nevertheless, the rate of false positive and false negative predictions is still unsatisfactorily high. RESULTS: We introduce a new resampling approach for causal discovery called accumulation IDA (aIDA). We show that aIDA improves the performance of causal discoveries compared to existing variants of IDA on both simulated and real yeast data. The higher reliability of top causal effect predictions achieved by aIDA promises to increase the rate of success of wet lab intervention experiments for functional studies. AVAILABILITY AND IMPLEMENTATION: R code for aIDA is available in the Supplementary material. CONTACT: franziska.taruttis@ur.de, julia.engelmann@ur.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data Normalization of (1)H NMR Metabolite Fingerprinting Data Sets in the Presence of Unbalanced Metabolite Regulation.

Data normalization is an essential step in NMR-based metabolomics. Conducted properly, it improves data quality and removes unwanted biases. The choice of the appropriate normalization method is critical and depends on the inherent properties of the data set in question. In particular, the presence of unbalanced metabolic regulation, where the different specimens and cohorts under investigation do not contain approximately equal shares of up- and down-regulated features, may strongly influence data normalization. Here, we demonstrate the suitability of the Shapiro-Wilk test to detect such unbalanced regulation. Next, employing a Latin-square design consisting of eight metabolites spiked into a urine specimen at eight different known concentrations, we show that commonly used normalization and scaling methods fail to retrieve true metabolite concentrations in the presence of increasing amounts of glucose added to simulate unbalanced regulation. However, by learning the normalization parameters on a subset of nonregulated features only, Linear Baseline Normalization, Probabilistic Quotient Normalization, and Variance Stabilization Normalization were found to account well for different dilutions of the samples without distorting the true spike-in levels even in the presence of marked unbalanced metabolic regulation. Finally, the methods described were applied successfully to a real world example of unbalanced regulation, namely, a set of plasma specimens collected from patients with and without acute kidney injury after cardiac surgery with cardiopulmonary bypass use.

BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology.

BACKGROUND: Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis. RESULTS: We describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology. CONCLUSIONS: BiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.