Anomaly detection in mixed high-dimensional molecular data.

MOTIVATION: Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. RESULTS: We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest. AVAILABILITY AND IMPLEMENTATION: All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire.

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.

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.

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.

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.

Th17 cytokine differentiation and loss of plasticity after SOCS1 inactivation in a cutaneous T-cell lymphoma.

We propose that deregulated T-helper-cell (Th) signaling underlies evolving Th17 cytokine expression seen during progression of cutaneous T-cell lymphoma (CTCL). Accordingly, we developed a lymphoma progression model comprising cell lines established at indolent (MAC-1) and aggressive (MAC-2A) CTCL stages. We discovered activating JAK3 (V722I) mutations present at indolent disease, reinforced in aggressive disease by novel compound heterozygous SOCS1 (G78R/D105N) JAK-binding domain inactivating mutations. Though isogenic, indolent and aggressive-stage cell lines had diverged phenotypically, the latter expressing multiple Th17 related cytokines, the former a narrower profile. Importantly, indolent stage cells remained poised for Th17 cytokine expression, readily inducible by treatment with IL-2 - a cytokine which mitigates Th17 differentiation in mice. In indolent stage cells JAK3 expression was boosted by IL-2 treatment. Th17 conversion of MAC-1 cells by IL-2 was blocked by pharmacological inhibition of JAK3 or STAT5, implicating IL2RG - JAK3 - STAT5 signaling in plasticity responses. Like IL-2 treatment, SOCS1 knockdown drove indolent stage cells to mimic key aggressive stage properties, notably IL17F upregulation. Co-immunoprecipitation experiments showed that SOCS1 mutations abolished JAK3 binding, revealing a key role for SOCS1 in regulating JAK3/STAT5 signaling. Collectively, our results show how JAK/STAT pathway mutations contribute to disease progression in CTCL cells by potentiating inflammatory cytokine signaling, widening the potential therapeutic target range for this intractable entity. MAC-1/2A cells also provide a candidate human Th17 laboratory model for identifying potentally actionable CTCL markers or targets and testing their druggability in vitro.

Identification of a new gene regulatory circuit involving B cell receptor activated signaling using a combined analysis of experimental, clinical and global gene expression data.

To discover new regulatory pathways in B lymphoma cells, we performed a combined analysis of experimental, clinical and global gene expression data. We identified a specific cluster of genes that was coherently expressed in primary lymphoma samples and suppressed by activation of the B cell receptor (BCR) through αIgM treatment of lymphoma cells in vitro. This gene cluster, which we called BCR.1, includes numerous cell cycle regulators. A reduced expression of BCR.1 genes after BCR activation was observed in different cell lines and also in CD10+ germinal center B cells. We found that BCR activation led to a delayed entry to and progression of mitosis and defects in metaphase. Cytogenetic changes were detected upon long-term αIgM treatment. Furthermore, an inverse correlation of BCR.1 genes with c-Myc co-regulated genes in distinct groups of lymphoma patients was observed. Finally, we showed that the BCR.1 index discriminates activated B cell-like and germinal centre B cell-like diffuse large B cell lymphoma supporting the functional relevance of this new regulatory circuit and the power of guided clustering for biomarker discovery.