Network integration and modelling of dynamic drug responses at multi-omics levels.

Uncovering cellular responses from heterogeneous genomic data is crucial for molecular medicine in particular for drug safety. This can be realized by integrating the molecular activities in networks of interacting proteins. As proof-of-concept we challenge network modeling with time-resolved proteome, transcriptome and methylome measurements in iPSC-derived human 3D cardiac microtissues to elucidate adverse mechanisms of anthracycline cardiotoxicity measured with four different drugs (doxorubicin, epirubicin, idarubicin and daunorubicin). Dynamic molecular analysis at in vivo drug exposure levels reveal a network of 175 disease-associated proteins and identify common modules of anthracycline cardiotoxicity in vitro, related to mitochondrial and sarcomere function as well as remodeling of extracellular matrix. These in vitro-identified modules are transferable and are evaluated with biopsies of cardiomyopathy patients. This to our knowledge most comprehensive study on anthracycline cardiotoxicity demonstrates a reproducible workflow for molecular medicine and serves as a template for detecting adverse drug responses from complex omics data.

Defective glycosylation and multisystem abnormalities characterize the primary immunodeficiency XMEN disease.

X-linked immunodeficiency with magnesium defect, EBV infection, and neoplasia (XMEN) disease are caused by deficiency of the magnesium transporter 1 (MAGT1) gene. We studied 23 patients with XMEN, 8 of whom were EBV naive. We observed lymphadenopathy (LAD), cytopenias, liver disease, cavum septum pellucidum (CSP), and increased CD4-CD8-B220-TCRαß+ T cells (αßDNTs), in addition to the previously described features of an inverted CD4/CD8 ratio, CD4+ T lymphocytopenia, increased B cells, dysgammaglobulinemia, and decreased expression of the natural killer group 2, member D (NKG2D) receptor. EBV-associated B cell malignancies occurred frequently in EBV-infected patients. We studied patients with XMEN and patients with autoimmune lymphoproliferative syndrome (ALPS) by deep immunophenotyping (32 immune markers) using time-of-flight mass cytometry (CyTOF). Our analysis revealed that the abundance of 2 populations of naive B cells (CD20+CD27-CD22+IgM+HLA-DR+CXCR5+CXCR4++CD10+CD38+ and CD20+CD27-CD22+IgM+HLA-DR+CXCR5+CXCR4+CD10-CD38-) could differentially classify XMEN, ALPS, and healthy individuals. We also performed glycoproteomics analysis on T lymphocytes and show that XMEN disease is a congenital disorder of glycosylation that affects a restricted subset of glycoproteins. Transfection of MAGT1 mRNA enabled us to rescue proteins with defective glycosylation. Together, these data provide new clinical and pathophysiological foundations with important ramifications for the diagnosis and treatment of XMEN disease.

Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles.

Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but this requires accurate and robust tools. Here, we develop a state of the art quantitative mass spectrometric pipeline to characterize formalin-fixed paraffin-embedded tissues of patients with closely related subtypes of diffuse large B-cell lymphoma. We combined a super-SILAC approach with label-free quantification (hybrid LFQ) to address situations where the protein is absent in the super-SILAC standard but present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9,000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of diffuse large B-cell lymphoma patients according to their cell of origin using both their global protein expression patterns and the 55-protein signature obtained previously from patient-derived cell lines (Deeb, S. J., D'Souza, R. C., Cox, J., Schmidt-Supprian, M., and Mann, M. (2012) Mol. Cell. Proteomics 11, 77-89). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistently with known trends between the subtypes. We used machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8, and TBC1D4) is predicted to classify patients with low error rates. Highly ranked proteins from the support vector analysis revealed differential expression of core signaling molecules between the subtypes, elucidating aspects of their pathobiology.

N-linked glycosylation enrichment for in-depth cell surface proteomics of diffuse large B-cell lymphoma subtypes.

Global analysis of lymphoma genome integrity and transcriptomes tremendously advanced our understanding of their biology. Technological advances in mass spectrometry-based proteomics promise to complete the picture by allowing the global quantification of proteins and their post-translational modifications. Here we use N-glyco FASP, a recently developed mass spectrometric approach using lectin-enrichment, in conjunction with a super-SILAC approach to quantify N-linked glycoproteins in lymphoma cells. From patient-derived diffuse large B-cell lymphoma cell lines, we mapped 2383 glycosites on 1321 protein groups, which were highly enriched for cell membrane proteins. This N-glyco subproteome alone allowed the segregation of the ABC from the GCB subtypes of diffuse large B-cell lymphoma, which before gene expression studies had been considered one disease entity. Encouragingly, many of the glycopeptides driving the segregation belong to proteins previously characterized as segregators in a deep proteome study of these subtypes (S. J. Deeb et al. MCP 2012 PMID 22442255). This conforms to the high correlation that we observed between the expression level of the glycosites and their corresponding proteins. Detailed examination of glycosites and glycoprotein expression levels uncovered, among other interesting findings, enrichment of transcription factor binding motifs, including known NF-kappa-B related ones. Thus, enrichment of a class of post-translationally modified peptides can classify cancer types as well as reveal cancer specific mechanistic changes.

Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles.

Correct classification of cancer patients into subtypes is a prerequisite for acute diagnosis and effective treatment. Currently this classification relies mainly on histological assessment, but gene expression analysis by microarrays has shown great promise. Here we show that high accuracy, quantitative proteomics can robustly segregate cancer subtypes directly at the level of expressed proteins. We investigated two histologically indistinguishable subtypes of diffuse large B-cell lymphoma (DLBCL): activated B-cell-like (ABC) and germinal-center B-cell-like (GCB) subtypes, by first developing a general lymphoma stable isotope labeling with amino acids in cell culture (SILAC) mix from heavy stable isotope-labeled cell lines. This super-SILAC mix was combined with cell lysates from five ABC-DLBCL and five GCB-DLBCL cell lines. Shotgun proteomic analysis on a linear ion trap Orbitrap mass spectrometer with high mass accuracy at the MS and MS/MS levels yielded a proteome of more than 7,500 identified proteins. High accuracy of quantification allowed robust separation of subtypes by principal component analysis. The main contributors to the classification included proteins known to be differentially expressed between the subtypes such as the transcription factors IRF4 and SPI1/PU.1, cell surface markers CD44 and CD27, as well as novel candidates. We extracted a signature of 55 proteins that segregated subtypes and contained proteins connected to functional differences between the ABC and GCB-DLBCL subtypes, including many NF-κB-regulated genes. Shortening the analysis time to single-shot analysis combined with use of the new linear quadrupole Orbitrap analyzer (Q Exactive) also clearly differentiated between the subtypes. These results show that high resolution shotgun proteomics combined with super-SILAC-based quantification is a promising new technology for tumor characterization and classification.

Sage components enhance cell death through nuclear factor kappa-B signaling.

The sage components linalyl acetate (Ly) and alpha-terpineol (Te) exhibit synergistic anti-proliferative effects. We investigated the effects of Ly and Te on NF-kappaB signaling in HCT-116 colon cancer cells. Ly and Te combinations dose-dependently reduced HCT-116 viability at non-cytotoxic concentrations. Combination treatment induced 30%-60% increase in PreG1 through induction of apoptosis and necrosis. DNA binding assays revealed that combination treatment suppressed both basal and TNF-alpha-induced NF-kappaB activation. This suppression correlated with the inhibition of p65 nuclear translocation and IkappaB-alpha degradation. The lack of change in IKK expression levels or inhibition in IkappaB-alpha phosphorylation suggest the involvement of an IKK-independent mechanism. Ly and Te combination was found to downregulate the expression of NF-kappaB-regulated antiapoptotic and proliferative gene products. Separate treatments and drug combinations significantly decreased DNA binding activity of NF-kappaB which led to the potentiation of cell death induced by the colon cancer drugs oxaliplatin and 5-FU. These results indicate that Ly and Te anticancer activities are partly mediated through the suppression of NF-kappaB activation, suggesting their use in combination with chemotherapeutic agents to induce apoptosis.