Mass-spectrometry-based draft of the Arabidopsis proteome.

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions.

Mass spectrometry-based draft of the mouse proteome.

The laboratory mouse ranks among the most important experimental systems for biomedical research and molecular reference maps of such models are essential informational tools. Here, we present a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues and several lines of analyses exemplify which insights can be gleaned from the data. For instance, tissue- and cell-type resolved profiles provide protein evidence for the expression of 17,000 genes, thousands of isoforms and 50,000 phosphorylation sites in vivo. Proteogenomic comparison of mouse, human and Arabidopsis reveal common and distinct mechanisms of gene expression regulation and, despite many similarities, numerous differentially abundant orthologs that likely serve species-specific functions. We leverage the mouse proteome by integrating phenotypic drug (n > 400) and radiation response data with the proteomes of 66 pancreatic ductal adenocarcinoma (PDAC) cell lines to reveal molecular markers for sensitivity and resistance. This unique atlas complements other molecular resources for the mouse and can be explored online via ProteomicsDB and PACiFIC.

ProteomicsDB: a multi-omics and multi-organism resource for life science research.

ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.

INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results.

Database search engines for bottom-up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these predictions to calculate additional intensity-based scores helps to overcome this drawback. Here, we describe a processing workflow termed INFERYS™ rescoring for the intensity-based rescoring of Sequest HT search engine results in Thermo Scientific™ Proteome Discoverer™ 2.5 software. The workflow is based on the deep learning platform INFERYS capable of predicting fragment ion intensities, which runs on personal computers without the need for graphics processing units. This workflow calculates intensity-based scores comparing peptide spectrum matches from Sequest HT and predicted spectra. Resulting scores are combined with classical search engine scores for input to the false discovery rate estimation tool Percolator. We demonstrate the merits of this approach by analyzing a classical HeLa standard sample and exemplify how this workflow leads to a better separation of target and decoy identifications, in turn resulting in increased peptide spectrum match, peptide and protein identification numbers. On an immunopeptidome dataset, this workflow leads to a 50% increase in identified peptides, emphasizing the advantage of intensity-based scores when analyzing low-intensity spectra or analytes with very similar physicochemical properties that require vast search spaces. Overall, the end-to-end integration of INFERYS rescoring enables simple and easy access to a powerful enhancement to classical database search engines, promising a deeper, more confident and more comprehensive analysis of proteomic data from any organism by unlocking the intensity dimension of tandem mass spectra for identification and more confident scoring.

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues.

Genome-, transcriptome- and proteome-wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein-level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue-specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.

ProteomicsDB.

ProteomicsDB (https://www.ProteomicsDB.org) is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC-MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e.g. NCBI GEO, protein-protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.

Pharmacoproteomic characterisation of human colon and rectal cancer.

Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials.

Ceramide Synthase 5 Is Essential to Maintain C16:0-Ceramide Pools and Contributes to the Development of Diet-induced Obesity.

Ceramides are bioactive sphingolipids, which are composed of sphingoid bases carrying acyl chains of various lengths. Ceramides are synthesized by a family of six ceramide synthases (CerS) in mammals, which produce ceramides with differentN-linked acyl chains. Increased ceramide levels are known to contribute to the development of obesity and insulin resistance. Recently, it has been demonstrated that the ceramide acylation pattern is of particular importance for an organism to maintain energy homeostasis. However, which of theCerSfamily members are involved in this process is not yet completely known. Using newly developedCerS5knock-out mice, we show here thatCerS5is essential to maintain cellular C16:0sphingolipid pools in lung, spleen, muscle, liver, and white adipose tissue. Glycerophospholipid levels inCerS5-deficient mice were not altered. We found a strong impact of CerS5-dependent ceramide synthesis in white adipose tissue after high fat diet feeding. In skeletal muscle, liver, and spleen, C16:0-ceramide levels were altered independent of feeding conditions. The loss ofCerS5is associated with reduced weight gain and improved systemic health, including maintenance of glucose homeostasis and reduced white adipose tissue inflammation after high fat diet challenge. Our findings indicate that reduction of endogenous C16:0-ceramide by genetic inhibition ofCerS5is sufficient to ameliorate obesity and its comorbidities.

moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets.

Increasingly, multiple omics approaches are being applied to understand the complexity of biological systems. Yet, computational approaches that enable the efficient integration of such data are not well developed. Here, we describe a novel algorithm, termed moCluster, which discovers joint patterns among multiple omics data. The method first employs a multiblock multivariate analysis to define a set of latent variables representing joint patterns across input data sets, which is further passed to an ordinary clustering algorithm in order to discover joint clusters. Using simulated data, we show that moCluster's performance is not compromised by issues present in iCluster/iCluster+ (notably, the nondeterministic solution) and that it operates 100× to 1000× faster than iCluster/iCluster+. We used moCluster to cluster proteomic and transcriptomic data from the NCI-60 cell line panel. The resulting cluster model revealed different phenotypes across cellular subtypes, such as doubling time and drug response. Applying moCluster to methylation, mRNA, and protein data from a large study on colorectal cancer patients identified four molecular subtypes, including one characterized by microsatellite instability and high expression of genes/proteins involved in immunity, such as PDL1, a target of multiple drugs currently in development. The other three subtypes have not been discovered before using single data sets, which clearly illustrates the molecular complexity of oncogenesis and the need for holistic, multidata analysis strategies.

Phosphoproteome Profiling Reveals Molecular Mechanisms of Growth-Factor-Mediated Kinase Inhibitor Resistance in EGFR-Overexpressing Cancer Cells.

Although substantial progress has been made regarding the use of molecularly targeted cancer therapies, resistance almost invariably develops and presents a major clinical challenge. The tumor microenvironment can rescue cancer cells from kinase inhibitors by growth-factor-mediated induction of pro-survival pathways. Here we show that epidermal growth factor receptor (EGFR) inhibition by Gefitinib is counteracted by growth factors, notably FGF2, and we assessed the global molecular consequences of this resistance at the proteome and phosphoproteome level in A431 cells. Tandem mass tag peptide labeling and quantitative mass spectrometry allowed the identification and quantification of 22 000 phosphopeptides and 8800 proteins in biological triplicates without missing values. The data show that FGF2 protects the cells from the antiproliferative effect of Gefitinib and largely prevents reprogramming of the proteome and phosphoproteome. Simultaneous EGFR/FGFR or EGFR/GSG2 (Haspin) inhibition overcomes this resistance, and the phosphoproteomic experiments further prioritized the RAS/MEK/ERK as well as the PI3K/mTOR axis for combination treatment. Consequently, the MEK inhibitor Trametinib prevented FGF2-mediated survival of EGFR inhibitor-resistant cells when used in combination with Gefitinib. Surprisingly, the PI3K/mTOR inhibitor Omipalisib reversed resistance mediated by all four growth factors tested, making it an interesting candidate for mitigating the effects of the tumor microenvironment.

A fitness assay for comparing RNAi effects across multiple C. elegans genotypes.

BACKGROUND: RNAi technology by feeding of E. coli containing dsRNA in C. elegans has significantly contributed to further our understanding of many different fields, including genetics, molecular biology, developmental biology and functional genomics. Most of this research has been carried out in a single genotype or genetic background. However, RNAi effects in one genotype do not reveal the allelic effects that segregate in natural populations and contribute to phenotypic variation. RESULTS: Here we present a method that allows for rapidly comparing RNAi effects among diverse genotypes at an improved high throughput rate. It is based on assessing the fitness of a population of worms by measuring the rate at which E. coli is consumed. Critically, we demonstrate the analytical power of this method by QTL mapping the loss of RNAi sensitivity (in the germline) in a recombinant inbred population derived from a cross between Bristol and a natural isolate from Hawaii. Hawaii has lost RNAi sensitivity in the germline. We found that polymorphisms in ppw-1 contribute to this loss of RNAi sensitivity, but that other loci are also likely to be important. CONCLUSIONS: In summary, we have established a fast method that improves the throughput of RNAi in liquid, that generates quantitative data, that is easy to implement in most laboratories, and importantly that enables QTL mapping using RNAi.

Chemical Phosphoproteomics Sheds New Light on the Targets and Modes of Action of AKT Inhibitors.

Due to its important roles in oncogenic signaling, AKT has been subjected to extensive drug discovery efforts leading to small molecule inhibitors investigated in advanced clinical trials. To better understand how these drugs exert their therapeutic effects at the molecular level, we combined chemoproteomic target affinity profiling using kinobeads and phosphoproteomics to analyze the five clinical AKT inhibitors AZD5363 (Capivasertib), GSK2110183 (Afuresertib), GSK690693, Ipatasertib, and MK-2206 in BT-474 breast cancer cells. Kinobead profiling identified between four and 29 nM targets for these compounds and showed that AKT1 and AKT2 were the only common targets. Similarly, measuring the response of the phosphoproteome to the same inhibitors identified ∼1700 regulated phosphorylation sites, 276 of which were perturbed by all five compounds. This analysis expanded the known AKT signaling network by 119 phosphoproteins that may represent direct or indirect targets of AKT. Within this new network, 41 regulated phosphorylation sites harbor the AKT substrate motif, and recombinant kinase assays validated 16 as novel AKT substrates. These included CEP170 and FAM83H, suggesting a regulatory function of AKT in mitosis and cytoskeleton organization. In addition, a specific phosphorylation pattern on the ULK1-FIP200-ATG13-VAPB complex was found to determine the active state of ULK1, leading to elevated autophagy in response to AKT inhibition.

Proteomic and transcriptomic profiling of aerial organ development in Arabidopsis.

Plant growth and development are regulated by a tightly controlled interplay between cell division, cell expansion and cell differentiation during the entire plant life cycle from seed germination to maturity and seed propagation. To explore some of the underlying molecular mechanisms in more detail, we selected different aerial tissue types of the model plant Arabidopsis thaliana, namely rosette leaf, flower and silique/seed and performed proteomic, phosphoproteomic and transcriptomic analyses of sequential growth stages using tandem mass tag-based mass spectrometry and RNA sequencing. With this exploratory multi-omics dataset, development dynamics of photosynthetic tissues can be investigated from different angles. As expected, we found progressive global expression changes between growth stages for all three omics types and often but not always corresponding expression patterns for individual genes on transcript, protein and phosphorylation site level. The biggest difference between proteomic- and transcriptomic-based expression information could be observed for seed samples. Proteomic and transcriptomic data is available via ProteomeXchange and ArrayExpress with the respective identifiers PXD018814 and E-MTAB-7978.

Identification of molecular targets for the targeted treatment of gastric cancer using dasatinib.

Gastric cancer (GC) remains the third leading cause of cancer-related death despite several improvements in targeted therapy. There is therefore an urgent need to investigate new treatment strategies, including the identification of novel biomarkers for patient stratification. In this study, we evaluated the effect of FDA-approved kinase inhibitors on GC. Through a combination of cell growth, migration and invasion assays, we identified dasatinib as an efficient inhibitor of GC proliferation. Mass-spectrometry-based selectivity profiling and subsequent knockdown experiments identified members of the SRC family of kinases including SRC, FRK, LYN and YES, as well as other kinases such as DDR1, ABL2, SIK2, RIPK2, EPHA2, and EPHB2 as dasatinib targets. The expression levels of the identified kinases were investigated on RNA and protein level in 200 classified tumor samples from patients, who had undergone gastrectomy, but had received no treatment. Levels of FRK, DDR1 and SRC expression on both mRNA and protein level were significantly higher in metastatic patient samples regardless of the tumor stage, while expression levels of SIK2 correlated with tumor size. Collectively, our data suggest dasatinib for treatment of GC based on its unique property, inhibiting a small number of key kinases (SRC, FRK, DDR1 and SIK2), highly expressed in GC patients.

Proteome activity landscapes of tumor cell lines determine drug responses.

Integrated analysis of genomes, transcriptomes, proteomes and drug responses of cancer cell lines (CCLs) is an emerging approach to uncover molecular mechanisms of drug action. We extend this paradigm to measuring proteome activity landscapes by acquiring and integrating quantitative data for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 CCLs. These data are used to contextualize proteins and p-sites and predict drug sensitivity. For example, we find that Progesterone Receptor (PGR) phosphorylation is associated with sensitivity to drugs modulating estrogen signaling such as Raloxifene. We also demonstrate that Adenylate kinase isoenzyme 1 (AK1) inactivates antimetabolites like Cytarabine. Consequently, high AK1 levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients, qualifying AK1 as a patient stratification marker and possibly as a drug target. We provide an interactive web application termed ATLANTiC (http://atlantic.proteomics.wzw.tum.de), which enables the community to explore the thousands of novel functional associations generated by this work.

Adaptation to HIF1α Deletion in Hypoxic Cancer Cells by Upregulation of GLUT14 and Creatine Metabolism.

Hypoxia-inducible factor 1α is a key regulator of the hypoxia response in normal and cancer tissues. It is well recognized to regulate glycolysis and is a target for therapy. However, how tumor cells adapt to grow in the absence of HIF1α is poorly understood and an important concept to understand for developing targeted therapies is the flexibility of the metabolic response to hypoxia via alternative pathways. We analyzed pathways that allow cells to survive hypoxic stress in the absence of HIF1α, using the HCT116 colon cancer cell line with deleted HIF1α versus control. Spheroids were used to provide a 3D model of metabolic gradients. We conducted a metabolomic, transcriptomic, and proteomic analysis and integrated the results. These showed surprisingly that in three-dimensional growth, a key regulatory step of glycolysis is Aldolase A rather than phosphofructokinase. Furthermore, glucose uptake could be maintained in hypoxia through upregulation of GLUT14, not previously recognized in this role. Finally, there was a marked adaptation and change of phosphocreatine energy pathways, which made the cells susceptible to inhibition of creatine metabolism in hypoxic conditions. Overall, our studies show a complex adaptation to hypoxia that can bypass HIF1α, but it is targetable and it provides new insight into the key metabolic pathways involved in cancer growth. IMPLICATIONS: Under hypoxia and HIF1 blockade, cancer cells adapt their energy metabolism via upregulation of the GLUT14 glucose transporter and creatine metabolism providing new avenues for drug targeting.

Challenges in Clinical Metaproteomics Highlighted by the Analysis of Acute Leukemia Patients with Gut Colonization by Multidrug-Resistant Enterobacteriaceae.

The microbiome has a strong impact on human health and disease and is, therefore, increasingly studied in a clinical context. Metaproteomics is also attracting considerable attention, and such data can be efficiently generated today owing to improvements in mass spectrometry-based proteomics. As we will discuss in this study, there are still major challenges notably in data analysis that need to be overcome. Here, we analyzed 212 fecal samples from 56 hospitalized acute leukemia patients with multidrug-resistant Enterobactericeae (MRE) gut colonization using metagenomics and metaproteomics. This is one of the largest clinical metaproteomic studies to date, and the first metaproteomic study addressing the gut microbiome in MRE colonized acute leukemia patients. Based on this substantial data set, we discuss major current limitations in clinical metaproteomic data analysis to provide guidance to researchers in the field. Notably, the results show that public metagenome databases are incomplete and that sample-specific metagenomes improve results. Furthermore, biological variation is tremendous which challenges clinical study designs and argues that longitudinal measurements of individual patients are a valuable future addition to the analysis of patient cohorts.

Optimized Enrichment of Phosphoproteomes by Fe-IMAC Column Chromatography.

Phosphorylation is among the most important post-translational modifications of proteins and has numerous regulatory functions across all domains of life. However, phosphorylation is often substoichiometric, requiring selective and sensitive methods to enrich phosphorylated peptides from complex cellular digests. Various methods have been devised for this purpose and we have recently described a Fe-IMAC HPLC column chromatography setup which is capable of comprehensive, reproducible, and selective enrichment of phosphopeptides out of complex peptide mixtures. In contrast to other formats such as StageTips or batch incubations using TiO2 or Ti-IMAC beads, Fe-IMAC HPLC columns do not suffer from issues regarding incomplete phosphopeptide binding or elution and enrichment efficiency scales linearly with the amount of starting material. Here, we provide a step-by-step protocol for the entire phosphopeptide enrichment procedure including sample preparation (lysis, digestion, desalting), Fe-IMAC column chromatography (column setup, operation, charging), measurement by LC-MS/MS (nHPLC gradient, MS parameters) and data analysis (MaxQuant). To increase throughput, we have optimized several key steps such as the gradient time of the Fe-IMAC separation (15 min per enrichment), the number of consecutive enrichments possible between two chargings (>20) and the column recharging itself (<1 h). We show that the application of this protocol enables the selective (>90 %) identification of more than 10,000 unique phosphopeptides from 1 mg of HeLa digest within 2 h of measurement time (Q Exactive Plus).

THADA Regulates the Organismal Balance between Energy Storage and Heat Production.

Human susceptibility to obesity is mainly genetic, yet the underlying evolutionary drivers causing variation from person to person are not clear. One theory rationalizes that populations that have adapted to warmer climates have reduced their metabolic rates, thereby increasing their propensity to store energy. We uncover here the function of a gene that supports this theory. THADA is one of the genes most strongly selected during evolution as humans settled in different climates. We report here that THADA knockout flies are obese, hyperphagic, have reduced energy production, and are sensitive to the cold. THADA binds the sarco/ER Ca2+ ATPase (SERCA) and acts on it as an uncoupler. Reducing SERCA activity in THADA mutant flies rescues their obesity, pinpointing SERCA as a key effector of THADA function. In sum, this identifies THADA as a regulator of the balance between energy consumption and energy storage, which was selected during human evolution.