Proteomic maps of breast cancer subtypes

Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40 oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were also reflected on the mRNA level. These breast cancer features revealed by our work provide novel insights that may ultimately translate to development of subtype-specific therapeutics

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

The Perseus computational platform for comprehensive analysis of (prote)omics data

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical toots for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. ALL activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets

A cross-species spatiotemporal proteomic analysis identifies UBE3A-dependent signaling pathways and targets

Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by the loss of neuronal E3 ligase UBE3A. Restoring UBE3A levels is a potential disease-modifying therapy for AS and has recently entered clinical trials. There is paucity of data regarding the molecular changes downstream of UBE3A hampering elucidation of disease therapeutics and biomarkers. Notably, UBE3A plays an important role in the nucleus but its targets have yet to be elucidated. Using proteomics, we assessed changes during postnatal cortical development in an AS mouse model. Pathway analysis revealed dysregulation of proteasomal and tRNA synthetase pathways at all postnatal brain developmental stages, while synaptic proteins were altered in adults. We confirmed pathway alterations in an adult AS rat model across multiple brain regions and highlighted region-specific differences. UBE3A reinstatement in AS model mice resulted in near complete and partial rescue of the proteome alterations in adolescence and adults, respectively, supporting the notion that restoration of UBE3A expression provides a promising therapeutic option. We show that the nuclear enriched transketolase (TKT), one of the most abundantly altered proteins, is a novel direct UBE3A substrate and is elevated in the neuronal nucleus of rat brains and human iPSC-derived neurons. Taken together, our study provides a comprehensive map of UBE3A-driven proteome remodeling in AS across development and species, and corroborates an early UBE3A reinstatement as a viable therapeutic option. To support future disease and biomarker research, we present an accessible large-scale multi-species proteomic resource for the AS community (https://www.angelman-proteome-project.org/)

Proteomic maps of breast cancer subtypes

Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40 oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were also reflected on the mRNA level. These breast cancer features revealed by our work provide novel insights that may ultimately translate to development of subtype-specific therapeutics.</p

Microbiota derived short chain fatty acids promote histone crotonylation in the colon through histone deacetylases

The recently discovered histone post-translational modification crotonylation connects cellular metabolism to gene regulation. Its regulation and tissue-specific functions are poorly understood. We characterize histone crotonylation in intestinal epithelia and find that histone H3 crotonylation at lysine 18 is a surprisingly abundant modification in the small intestine crypt and colon, and is linked to gene regulation. We show that this modification is highly dynamic and regulated during the cell cycle. We identify class I histone deacetylases, HDAC1, HDAC2, and HDAC3, as major executors of histone decrotonylation. We show that known HDAC inhibitors, including the gut microbiota-derived butyrate, affect histone decrotonylation. Consistent with this, we find that depletion of the gut microbiota leads to a global change in histone crotonylation in the colon. Our results suggest that histone crotonylation connects chromatin to the gut microbiota, at least in part, via short-chain fatty acids and HDACs

Maternal Larp6 controls oocyte development, chorion formation and elevation.

La-related protein 6 (Larp6) is a conserved RNA-binding protein found across eukaryotes that has been suggested to regulate collagen biogenesis, muscle development, ciliogenesis, and various aspects of cell proliferation and migration. Zebrafish have two Larp6 family genes: larp6a and larp6b Viable and fertile single and double homozygous larp6a and larp6b zygotic mutants revealed no defects in muscle structure, and were indistinguishable from heterozygous or wild-type siblings. However, larp6a mutant females produced eggs with chorions that failed to elevate fully and were fragile. Eggs from larp6b single mutant females showed minor chorion defects, but chorions from eggs laid by larp6a;larp6b double mutant females were more defective than those from larp6a single mutants. Electron microscopy revealed defective chorionogenesis during oocyte development. Despite this, maternal zygotic single and double mutants were viable and fertile. Mass spectrometry analysis provided a description of chorion protein composition and revealed significant reductions in a subset of zona pellucida and lectin-type proteins between wild-type and mutant chorions that paralleled the severity of the phenotype. We conclude that Larp6 proteins are required for normal oocyte development, chorion formation and egg activation

Expression of Siglec-E Alters the proteome of lipopolysaccharide (LPS)-activated macrophages but does not affect LPS-driven cytokine production or toll-like receptor 4 endocytosis

Siglec-E is a murine CD33-related siglec that functions as an inhibitory receptor and is expressed mainly on neutrophils and macrophage populations. Recent studies have suggested that siglec-E is an important negative regulator of lipopolysaccharide (LPS)-toll-like receptor 4 (TLR4) signaling and one report (1) claimed that siglec-E is required for TLR4 endocytosis following uptake of Escherichia coli by macrophages and dendritic cells (DCs). Our attempts to reproduce these observations using cells from wild-type (WT) and siglec-E-deficient mice were unsuccessful. We used a variety of assays to determine if siglec-E expressed by different macrophage populations can regulate TLR4 signaling in response to LPS, but found no consistent differences in cytokine secretion in vitro and in vivo, comparing three different strains of siglec-E-deficient mice with matched WT controls. No evidence was found that the siglec-E deficiency was compensated by expression of siglecs-F and -G, the other murine inhibitory CD33-related siglecs. Quantitative proteomics was used as an unbiased approach and provided additional evidence that siglec-E does not suppress inflammatory TLR4 signaling. Interestingly, proteomics revealed a siglec-E-dependent alteration in macrophage protein composition that could be relevant to functional responses in host defense. In support of this, siglec-E-deficient mice exhibited enhanced growth of Salmonella enterica serovar Typhimurium in the liver following intravenous infection, but macrophages lacking siglec-E did not show altered uptake or killing of bacteria in vitro. Using various cell types including bone marrow-derived DCs (BMDCs), splenic DCs, and macrophages from WT and siglec-E-deficient mice, we showed that siglec-E is not required for TLR4 endocytosis following E. coli uptake or LPS challenge. We failed to see expression of siglec-E by BMDC even after LPS-induced maturation, but confirmed previous studies that splenic DCs express low levels of siglec-E. Taken together, our findings do not support a major role of siglec-E in regulation of TLR4 signaling functions or TLR4 endocytosis in macrophages or DCs. Instead, they reveal that induction of siglec-E by LPS can modulate the phenotype of macrophages, the functional significance of which is currently unclear

A Mass Spectrometry-Based Approach for Mapping Protein Subcellular Localization Reveals the Spatial Proteome of Mouse Primary Neurons

We previously developed a mass spectrometry-based method, Dynamic Organellar Maps, for the determination of protein subcellular localisation, and the identification of translocation events in comparative experiments. The use of metabolic labelling for quantification (SILAC) renders the method best suited to cells grown in culture. Here we have adapted the workflow to both label-free quantification (LFQ) and chemical labelling/multiplexing strategies (Tandem Mass Tagging, TMT). Both new methods are highly effective for generation of organellar maps and capture of protein translocations. Furthermore, application of label-free organellar mapping to acutely isolated mouse primary neurons provided subcellular localisation and copy number information for over 8,000 proteins, allowing a detailed analysis of organellar organisation. Our study extends the scope of Dynamic Organellar Maps to any cell type or tissue, and also to high throughput screening.This work was funded by the German Research Foundation (DFG/Gottfried Wilhelm Leibniz Prize MA 1764/2-1), the Louis-Jeantet Foundation, the Max Planck Society for the Advancement of Science, a Wellcome Trust Senior Clinical Research Fellowship 108070/Z/15/Z (to M.P.W.), and a strategic award to Cambridge Institute for Medical Research from the Wellcome Trust (100140)