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

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

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

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

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)

Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status

A cell line representative of human high-grade serous ovarian cancer (HGSOC) should not only resemble its tumour of origin at the molecular level, but also demonstrate functional utility in pre-clinical investigations. Here, we report the integrated proteomic analysis of 26 ovarian cancer cell lines, HGSOC tumours, immortalized ovarian surface epithelial cells and fallopian tube epithelial cells via a single-run mass spectrometric workflow. The in-depth quantification of >10,000 proteins results in three distinct cell line categories: epithelial (group I), clear cell (group II) and mesenchymal (group III). We identify a 67-protein cell line signature, which separates our entire proteomic data set, as well as a confirmatory publicly available CPTAC/TCGA tumour proteome data set, into a predominantly epithelial and mesenchymal HGSOC tumour cluster. This proteomics-based epithelial/mesenchymal stratification of cell lines and human tumours indicates a possible origin of HGSOC either from the fallopian tube or from the ovarian surface epithelium

Proteomics reveals the effects of sustained weight loss on the human plasma proteome

Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of −16% and +37%, respectively (P < 10(−13)), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten‐protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases

C9ORF72 interaction with cofilin modulates actin dynamics in motor neurons.

Intronic hexanucleotide expansions in C9ORF72 are common in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, but it is unknown whether loss of function, toxicity by the expanded RNA or dipeptides from non-ATG-initiated translation are responsible for the pathophysiology. We determined the interactome of C9ORF72 in motor neurons and found that C9ORF72 was present in a complex with cofilin and other actin binding proteins. Phosphorylation of cofilin was enhanced in C9ORF72-depleted motor neurons, in patient-derived lymphoblastoid cells, induced pluripotent stem cell-derived motor neurons and post-mortem brain samples from ALS patients. C9ORF72 modulates the activity of the small GTPases Arf6 and Rac1, resulting in enhanced activity of LIM-kinases 1 and 2 (LIMK1/2). This results in reduced axonal actin dynamics in C9ORF72-depleted motor neurons. Dominant negative Arf6 rescues this defect, suggesting that C9ORF72 acts as a modulator of small GTPases in a pathway that regulates axonal actin dynamics

An effector from the Huanglongbing-associated pathogen targets citrus proteases

The citrus industry is facing an unprecedented challenge from Huanglongbing (HLB). All cultivars can be affected by the HLB-associated bacterium ‘Candidatus Liberibacter asiaticus’ (CLas) and there is no known resistance. Insight into HLB pathogenesis is urgently needed in order to develop effective management strategies. Here, we use Sec-delivered effector 1 (SDE1), which is conserved in all CLas isolates, as a molecular probe to understand CLas virulence. We show that SDE1 directly interacts with citrus papain-like cysteine proteases (PLCPs) and inhibits protease activity. PLCPs are defense-inducible and exhibit increased protein accumulation in CLas-infected trees, suggesting a role in citrus defense responses. We analyzed PLCP activity in field samples, revealing specific members that increase in abundance but remain unchanged in activity during infection. SDE1-expressing transgenic citrus also exhibit reduced PLCP activity. These data demonstrate that SDE1 inhibits citrus PLCPs, which are immune-related proteases that enhance defense responses in plants