Integrative features of the yeast phosphoproteome and protein-protein interaction map

Following recent advances in high-throughput mass spectrometry (MS)-based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ～6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein-protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other.     

Investigation on PLK2 and PLK3 substrate recognition

Analyses of human phosphoproteome based on primary structure of the aminoacids surrounding the phosphor Ser/Thr suggest that a significant proportion of phosphosites is generated by a restricted number of acidophilic kinases, among which protein kinase CK2 plays a prominent role. Recently, new acidophilic kinases belonging to the Polo like kinase family have been characterized, with special reference to PLK1, PLK2, and PLK3 kinases. While some progress has been made in deciphering the PLK1-dependent phosphoproteome, very little is known about the targets of PLK2 and PLK3 kinases. In this report by using an in vitro approach, consisting of cell lysate phosphorylation, phosphoprotein separation by 2D gel electrophoresis and mass spectrometry, we describe the identification of new potential substrates of PLK2 and PLK3 kinases. We have identified and validated as in vitro PLK2 and PLK3 substrates HSP90, GRP-94, β-tubulin, calumenin, and 14-3-3 epsilon. The phosphosites generated by PLK3 in these proteins have been identified by mass spectrometry analysis to get new insights about PLKs specificity determinants. These latter have been further corroborated by an in silico analysis of the PLKs substrate binding region.     

Assessing and improving the transferability of current global spatial prediction models

Aim

Global-scale maps of the environment are an important source of information for researchers and decision makers. Often, these maps are created by training machine learning algorithms on field-sampled reference data using remote sensing information as predictors. Since field samples are often sparse and clustered in geographic space, model prediction requires a transfer of the trained model to regions where no reference data are available. However, recent studies question the feasibility of predictions far beyond the location of training data.

Innovation

We propose a novel workflow for spatial predictive mapping that leverages recent developments in this field and combines them in innovative ways with the aim of improved model transferability and performance assessment. We demonstrate, evaluate and discuss the workflow with data from recently published global environmental maps.

Main conclusions

Reducing predictors to those relevant for spatial prediction leads to an increase of model transferability and map accuracy without a decrease of prediction quality in areas with high sampling density. Still, reliable gap-free global predictions were not possible, highlighting that global maps and their evaluation are hampered by limited availability of reference data.     

Large‐scale phosphorylation mapping reveals the extent of tyrosine phosphorylation in Arabidopsis

Protein phosphorylation regulates a wide range of cellular processes. Here, we report the proteome‐wide mapping of in vivo phosphorylation sites in Arabidopsis by using complementary phosphopeptide enrichment techniques coupled with high‐accuracy mass spectrometry. Using unfractionated whole cell lysates of Arabidopsis, we identified 2597 phosphopeptides with 2172 high‐confidence, unique phosphorylation sites from 1346 proteins. The distribution of phosphoserine, phosphothreonine, and phosphotyrosine sites was 85.0, 10.7, and 4.3%. Although typical tyrosine‐specific protein kinases are absent in Arabidopsis, the proportion of phosphotyrosines among the phospho‐residues in Arabidopsis is similar to that in humans, where over 90 tyrosine‐specific protein kinases have been identified. In addition, the tyrosine phosphoproteome shows features distinct from those of the serine and threonine phosphoproteomes. Taken together, we highlight the extent and contribution of tyrosine phosphorylation in plants.     

Evaluating experimental bias and completeness in comparative phosphoproteomics analysis

Unraveling the functional dynamics of phosphorylation networks is a crucial step in understanding the way in which biological networks form a living cell. Recently there has been an enormous increase in the number of measured phosphorylation events. Nevertheless, comparative and integrative analysis of phosphoproteomes is confounded by incomplete coverage and biases introduced by different experimental workflows. As a result, we cannot differentiate whether phosphosites indentified in only one or two samples are the result of condition or species specific phosphorylation, or reflect missing data. Here, we evaluate the impact of incomplete phosphoproteomics datasets on comparative analysis, and we present bioinformatics strategies to quantify the impact of different experimental workflows on measured phosphoproteomes. We show that plotting the saturation in observed phosphosites in replicates provides a reproducible picture of the extent of a particular phosphoproteome. Still, we are still far away from a complete picture of the total human phosphoproteome. The impact of different experimental techniques on the similarity between phosphoproteomes can be estimated by comparing datasets from different experimental pipelines to a common reference. Our results show that comparative analysis is most powerful when datasets have been generated using the same experimental workflow. We show this experimentally by measuring the tyrosine phosphoproteome from Caenorhabditis elegans and comparing it to the tyrosine phosphoproteome of HeLa cells, resulting in an overlap of about 4%. This overlap between very different organisms represents a three-fold increase when compared to dataset of older studies, wherein different workflows were used. The strategies we suggest enable an estimation of the impact of differences in experimental workflows on the overlap between datasets. This will allow us to perform comparative analyses not only on datasets specifically generated for this purpose, but also to extract insights through comparative analysis of the ever-increasing wealth of publically available phosphorylation data.     

Strategies for metagenomic-guided whole-community proteomics of complex microbial environments

Accurate protein identification in large-scale proteomics experiments relies upon a detailed, accurate protein catalogue, which is derived from predictions of open reading frames based on genome sequence data. Integration of mass spectrometry-based proteomics data with computational proteome predictions from environmental metagenomic sequences has been challenging because of the variable overlap between proteomic datasets and corresponding short-read nucleotide sequence data. In this study, we have benchmarked several strategies for increasing microbial peptide spectral matching in metaproteomic datasets using protein predictions generated from matched metagenomic sequences from the same human fecal samples. Additionally, we investigated the impact of mass spectrometry-based filters (high mass accuracy, delta correlation), and de novo peptide sequencing on the number and robustness of peptide-spectrum assignments in these complex datasets. In summary, we find that high mass accuracy peptide measurements searched against non-assembled reads from DNA sequencing of the same samples significantly increased identifiable proteins without sacrificing accuracy.     

Integrative analysis of the mitochondrial proteome in yeast

In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans.     

Some practical suggestions for improving engagement between researchers and policy‐makers in natural resource management

Summary Policy‐makers and managers in natural resource management (NRM) often complain that researchers are out of touch. Researchers often complain that policy‐makers and managers make poorly informed decisions. In this article, we report on a meeting between researchers, policy‐makers and managers convened to identify practical solutions to improve engagement between these camps. A necessary starting point is that every researcher and policy‐maker should understand, and tap into, the motivations and reward systems of the other when seeking engagement. For example, researchers can be motivated to engage in policy development if there is a promise of outputs that align with their reward systems such as co‐authored publications. Successful research–policy partnerships are built around personal relationships. As a researcher, you cannot therefore expect your results to inform policy by only publishing in journals. As a policy‐maker, you cannot guarantee engagement from researchers by publicly inviting comment on a document. Actively building and maintaining relationships with key individuals through discussions, meetings, workshops or field days will increase the likelihood that research outcomes will inform policy decisions. We identified secondments, sabbaticals, fellowships and ‘buddies’, an annual national NRM conference and ‘contact mapping’ (a Facebook‐type network) as forums that can catalyse new relationships between researchers and policy‐makers. We challenge every researcher, policy‐maker and manager in NRM to build one new cross‐cultural relationship each year.     

Accurate prediction of kinase-substrate networks using knowledge graphs

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).     

Integrative Proteomic Analysis of Multiple Posttranslational Modifications in Inflammatory Response

Posttranslational modifications(PTMs) of proteins,particularly acetylation,phosphorylation,and ubiquitination,play critical roles in the host innate immune response.PTMs’ dynamic changes and the crosstalk among them are complicated.To build a comprehensive dynamic network of inflammation-related proteins,we integrated data from the whole-cell proteome(WCP),acetylome,phosphoproteome,and ubiquitinome of human and mouse macrophages.Our datasets of acetylation,phosphorylation,and ubiquitination site...     

X-MATE: a flexible system for mapping short read data

SUMMARY: Accurate and complete mapping of short-read sequencing to a reference genome greatly enhances the discovery of biological results and improves statistical predictions. We recently presented RNA-MATE, a pipeline for the recursive mapping of RNA-Seq datasets. With the rapid increase in genome re-sequencing projects, progression of available mapping software and the evolution of file formats, we now present X-MATE, an updated version of RNA-MATE, capable of mapping both RNA-Seq and DNA datasets and with improved performance, output file formats, configuration files, and flexibility in core mapping software. AVAILABILITY: Executables, source code, junction libraries, test data and results and the user manual are available from http://grimmond.imb.uq.edu.au/X-MATE/.     

Dynamic changes of the phosphoproteome in postmortem mouse brains

Protein phosphorylation is deeply involved in the pathological mechanism of various neurodegenerative disorders. However, in human pathological samples, phosphorylation can be modified during preservation by postmortem factors such as time and temperature. Postmortem changes may also differ among proteins. Unfortunately, there is no comprehensive database that could support the analysis of protein phosphorylation in human brain samples from the standpoint of postmortem changes. As a first step toward addressing the issue, we performed phosphoproteome analysis with brain tissue dissected from mouse bodies preserved under different conditions. Quantitative whole proteome mass analysis showed surprisingly diverse postmortem changes in phosphoproteins that were dependent on temperature, time and protein species. Twelve hrs postmortem was a critical time point for preservation at room temperature. At 4°C, after the body was cooled down, most phosphoproteins were stable for 72 hrs. At either temperature, increase greater than 2-fold was exceptional during this interval. We found several standard proteins by which we can calculate the postmortem time at room temperature. The information obtained in this study will be indispensable for evaluating experimental data with human as well as mouse brain samples.     

Modeling forest above-ground biomass using freely available satellite and multisource datasets

Achieving the 2 °C global warming target will require all stakeholders, from local to global, to accelerate carbon reduction plans. To properly estimate carbon amounts sequestered and stored by forests, these stakeholders need accurate maps of forest above-ground biomass (AGB). However, AGB maps are developed solely by researchers using methodologies that are typically difficult for non-researchers to understand. To ensure that non-researchers can prepare such maps, freely available open-source datasets are needed along with a simple AGB estimation method. Remote sensing data have become widely used, and some satellite images are provided as open-source data. However, few AGB estimation studies have relied exclusively on freely available datasets. This study, therefore, examined the capability of using open-source satellite and multisource datasets to estimate AGB for two contrasting test sites in Hokkaido, Japan, and for the two sites combined (the unified sites). Test site 1 contained three forest types on a hilly terrain, while test site 2 contained one dominant forest type on a flat terrain. The datasets used were L-band backscatter, a canopy height model, slope, aspect, annual mean temperature, annual precipitation, and forest type. Three machine-learning algorithms and one parametric algorithm were compared. The results showed that the machine-learning models outperformed the parametric model in all instances, reproducing low and high AGB values properly with reduced saturation issues. A variable importance analysis revealed that in the best-performing machine-learning models, forest type and annual precipitation were the two most important variables for test site 1 and the unified sites, and another climatic factor, annual mean temperature, rose in rank for the unified sites. These variables had relatively large intra-variability and inter-variability in common, both of which appear to have offered advantages in modeling AGB for the sites where AGB variability was large. In contrast, the synthetic aperture radar (SAR)-based variables (the backscatter and the canopy height model) ranked lower in relative importance. This may reflect the negative influence of hilly terrain of test site 1 (and the unified sites) on the precision of SAR-based datasets. For test site 2, with its flat terrain, these SAR-based variables were the two dominant variables. Our results show the promise of freely available satellite and multisource datasets for modeling AGB and the important roles that non-SAR-based variables may play in large-scale AGB mapping that includes varied terrain.     

RNAcentral: A vision for an international database of RNA sequences

During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor.     

RAPID: Fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale

Recent research in the protein intrinsic disorder was stimulated by the availability of accurate computational predictors. However, most of these methods are relatively slow, especially considering proteome-scale applications, and were shown to produce relatively large errors when estimating disorder at the protein- (in contrast to residue-) level, which is defined by the fraction/content of disordered residues. To this end, we propose a novel support vector Regression-based Accurate Predictor of Intrinsic Disorder (RAPID). Key advantages of RAPID are speed (prediction of an average-size eukaryotic proteome takes < 1 h on a modern desktop computer); sophisticated design (multiple, complementary information sources that are aggregated over an input chain are combined using feature selection); and high-quality and robust predictive performance. Empirical tests on two diverse benchmark datasets reveal that RAPID's predictive performance compares favorably to a comprehensive set of state-of-the-art disorder and disorder content predictors. Drawing on high speed and good predictive quality, RAPID was used to perform large-scale characterization of disorder in 200 + fully sequenced eukaryotic proteomes. Our analysis reveals interesting relations of disorder with structural coverage and chain length, and unusual distribution of fully disordered chains. We also performed a comprehensive (using 56000+ annotated chains, which doubles the scope of previous studies) investigation of cellular functions and localizations that are enriched in the disorder in the human proteome. RAPID, which allows for batch (proteome-wide) predictions, is available as a web server at http://biomine.ece.ualberta.ca/RAPID/.     

Reference-free detection of isolated SNPs

Detecting single nucleotide polymorphisms (SNPs) between genomes is becoming a routine task with next-generation sequencing. Generally, SNP detection methods use a reference genome. As non-model organisms are increasingly investigated, the need for reference-free methods has been amplified. Most of the existing reference-free methods have fundamental limitations: they can only call SNPs between exactly two datasets, and/or they require a prohibitive amount of computational resources. The method we propose, discoSnp, detects both heterozygous and homozygous isolated SNPs from any number of read datasets, without a reference genome, and with very low memory and time footprints (billions of reads can be analyzed with a standard desktop computer). To facilitate downstream genotyping analyses, discoSnp ranks predictions and outputs quality and coverage per allele. Compared to finding isolated SNPs using a state-of-the-art assembly and mapping approach, discoSnp requires significantly less computational resources, shows similar precision/recall values, and highly ranked predictions are less likely to be false positives. An experimental validation was conducted on an arthropod species (the tick Ixodes ricinus) on which de novo sequencing was performed. Among the predicted SNPs that were tested, 96% were successfully genotyped and truly exhibited polymorphism.