Global detection of human variants and isoforms by deep proteome sequencing.

An average shotgun proteomics experiment detects approximately 10,000 human proteins from a single sample. However, individual proteins are typically identified by peptide sequences representing a small fraction of their total amino acids. Hence, an average shotgun experiment fails to distinguish different protein variants and isoforms. Deeper proteome sequencing is therefore required for the global discovery of protein isoforms. Using six different human cell lines, six proteases, deep fractionation and three tandem mass spectrometry fragmentation methods, we identify a million unique peptides from 17,717 protein groups, with a median sequence coverage of approximately 80%. Direct comparison with RNA expression data provides evidence for the translation of most nonsynonymous variants. We have also hypothesized that undetected variants likely arise from mutation-induced protein instability. We further observe comparable detection rates for exon-exon junction peptides representing constitutive and alternative splicing events. Our dataset represents a resource for proteoform discovery and provides direct evidence that most frame-preserving alternatively spliced isoforms are translated.

A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies.

Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association.

A Bioinformatics Guide to Plant Microbiome Analysis.

Recent evidence for intimate relationship of plants with their microbiota shows that plants host individual and diverse microbial communities that are essential for their survival. Understanding their relatedness using genome-based and high-throughput techniques remains a hot topic in microbiome research. Molecular analysis of the plant holobiont necessitates the application of specific sampling and preparatory steps that also consider sources of unwanted information, such as soil, co-amplified plant organelles, human DNA, and other contaminations. Here, we review state-of-the-art and present practical guidelines regarding experimental and computational aspects to be considered in molecular plant-microbiome studies. We discuss sequencing and "omics" techniques with a focus on the requirements needed to adapt these methods to individual research approaches. The choice of primers and sequence databases is of utmost importance for amplicon sequencing, while the assembly and binning of shotgun metagenomic sequences is crucial to obtain quality data. We discuss specific bioinformatic workflows to overcome the limitation of genome database resources and for covering large eukaryotic genomes such as fungi. In transcriptomics, it is necessary to account for the separation of host mRNA or dual-RNAseq data. Metaproteomics approaches provide a snapshot of the protein abundances within a plant tissue which requires the knowledge of complete and well-annotated plant genomes, as well as microbial genomes. Metabolomics offers a powerful tool to detect and quantify small molecules and molecular changes at the plant-bacteria interface if the necessary requirements with regard to (secondary) metabolite databases are considered. We highlight data integration and complementarity which should help to widen our understanding of the interactions among individual players of the plant holobiont in the future.

MS-Helios: a Circos wrapper to visualize multi-omic datasets.

BACKGROUND: Advances in high-resolution mass spectrometry facilitate the identification of hundreds of metabolites, thousands of proteins and their post-translational modifications. This remarkable progress poses a challenge to data analysis and visualization, requiring methods to reduce dimensionality and represent the data in a compact way. To provide a more holistic view, we recently introduced circular proteome maps (CPMs). However, the CPM construction requires prior data transformation and extensive knowledge of the Perl-based tool, Circos. RESULTS: We present MS-Helios, an easy to use command line tool with multiple built-in data processing functions, allowing non-expert users to construct CPMs or in general terms circular plots with a non-genomic basis. MS-Helios automatically generates data and configuration files to create high quality and publishable circular plots with Circos. We showcase the software on large-scale multi-omic datasets to visualize global trends and/or to contextualize specific features. CONCLUSIONS: MS-Helios provides the means to easily map and visualize multi-omic data in a comprehensive way. The software, datasets, source code, and tutorial are available at https://sourceforge.net/projects/ms-helios/ .

Annotation of the Domestic Pig Genome by Quantitative Proteogenomics.

The pig is one of the earliest domesticated animals in the history of human civilization and represents one of the most important livestock animals. The recent sequencing of the Sus scrofa genome was a major step toward the comprehensive understanding of porcine biology, evolution, and its utility as a promising large animal model for biomedical and xenotransplantation research. However, the functional and structural annotation of the Sus scrofa genome is far from complete. Here, we present mass spectrometry-based quantitative proteomics data of nine juvenile organs and six embryonic stages between 18 and 39 days after gestation. We found that the data provide evidence for and improve the annotation of 8176 protein-coding genes including 588 novel and 321 refined gene models. The analysis of tissue-specific proteins and the temporal expression profiles of embryonic proteins provides an initial functional characterization of expressed protein interaction networks and modules including as yet uncharacterized proteins. Comparative transcript and protein expression analysis to human organs reveal a moderate conservation of protein translation across species. We anticipate that this resource will facilitate basic and applied research on Sus scrofa as well as its porcine relatives.

Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling.

Mitochondrial dysfunction is associated with many human diseases, including cancer and neurodegeneration, that are often linked to proteins and pathways that are not well-characterized. To begin defining the functions of such poorly characterized proteins, we used mass spectrometry to map the proteomes, lipidomes, and metabolomes of 174 yeast strains, each lacking a single gene related to mitochondrial biology. 144 of these genes have human homologs, 60 of which are associated with disease and 39 of which are uncharacterized. We present a multi-omic data analysis and visualization tool that we use to find covariance networks that can predict molecular functions, correlations between profiles of related gene deletions, gene-specific perturbations that reflect protein functions, and a global respiration deficiency response. Using this multi-omic approach, we link seven proteins including Hfd1p and its human homolog ALDH3A1 to mitochondrial coenzyme Q (CoQ) biosynthesis, an essential pathway disrupted in many human diseases. This Resource should provide molecular insights into mitochondrial protein functions.

A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium meliloti.

Legumes are essential components of agricultural systems because they enrich the soil in nitrogen and require little environmentally deleterious fertilizers. A complex symbiotic association between legumes and nitrogen-fixing soil bacteria called rhizobia culminates in the development of root nodules, where rhizobia fix atmospheric nitrogen and transfer it to their plant host. Here we describe a quantitative proteomic atlas of the model legume Medicago truncatula and its rhizobial symbiont Sinorhizobium meliloti, which includes more than 23,000 proteins, 20,000 phosphorylation sites, and 700 lysine acetylation sites. Our analysis provides insight into mechanisms regulating symbiosis. We identify a calmodulin-binding protein as a key regulator in the host and assign putative roles and targets to host factors (bioactive peptides) that control gene expression in the symbiont. Further mining of this proteomic resource may enable engineering of crops and their microbial partners to increase agricultural productivity and sustainability.

Translatomics combined with transcriptomics and proteomics reveals novel functional, recently evolved orphan genes in Escherichia coli O157:H7 (EHEC).

BACKGROUND: Genomes of E. coli, including that of the human pathogen Escherichia coli O157:H7 (EHEC) EDL933, still harbor undetected protein-coding genes which, apparently, have escaped annotation due to their small size and non-essential function. To find such genes, global gene expression of EHEC EDL933 was examined, using strand-specific RNAseq (transcriptome), ribosomal footprinting (translatome) and mass spectrometry (proteome). RESULTS: Using the above methods, 72 short, non-annotated protein-coding genes were detected. All of these showed signals in the ribosomal footprinting assay indicating mRNA translation. Seven were verified by mass spectrometry. Fifty-seven genes are annotated in other enterobacteriaceae, mainly as hypothetical genes; the remaining 15 genes constitute novel discoveries. In addition, protein structure and function were predicted computationally and compared between EHEC-encoded proteins and 100-times randomly shuffled proteins. Based on this comparison, 61 of the 72 novel proteins exhibit predicted structural and functional features similar to those of annotated proteins. Many of the novel genes show differential transcription when grown under eleven diverse growth conditions suggesting environmental regulation. Three genes were found to confer a phenotype in previous studies, e.g., decreased cattle colonization. CONCLUSIONS: These findings demonstrate that ribosomal footprinting can be used to detect novel protein coding genes, contributing to the growing body of evidence that hypothetical genes are not annotation artifacts and opening an additional way to study their functionality. All 72 genes are taxonomically restricted and, therefore, appear to have evolved relatively recently de novo.

Ion mobility tandem mass spectrometry enhances performance of bottom-up proteomics.

One of the limiting factors in determining the sensitivity of tandem mass spectrometry using hybrid quadrupole orthogonal acceleration time-of-flight instruments is the duty cycle of the orthogonal ion injection system. As a consequence, only a fraction of the generated fragment ion beam is collected by the time-of-flight analyzer. Here we describe a method utilizing postfragmentation ion mobility spectrometry of peptide fragment ions in conjunction with mobility time synchronized orthogonal ion injection leading to a substantially improved duty cycle and a concomitant improvement in sensitivity of up to 10-fold for bottom-up proteomic experiments. This enabled the identification of 7500 human proteins within 1 day and 8600 phosphorylation sites within 5 h of LC-MS/MS time. The method also proved powerful for multiplexed quantification experiments using tandem mass tags exemplified by the chemoproteomic interaction analysis of histone deacetylases with Trichostatin A.

Mass-spectrometry-based draft of the human proteome.

Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.

MScDB: a mass spectrometry-centric protein sequence database for proteomics.

Protein sequence databases are indispensable tools for life science research including mass spectrometry (MS)-based proteomics. In current database construction processes, sequence similarity clustering is used to reduce redundancies in the source data. Albeit powerful, it ignores the peptide-centric nature of proteomic data and the fact that MS is able to distinguish similar sequences. Therefore, we introduce an approach that structures the protein sequence space at the peptide level using theoretical and empirical information from large-scale proteomic data to generate a mass spectrometry-centric protein sequence database (MScDB). The core modules of MScDB are an in-silico proteolytic digest and a peptide-centric clustering algorithm that groups protein sequences that are indistinguishable by mass spectrometry. Analysis of various MScDB uses cases against five complex human proteomes, resulting in 69 peptide identifications not present in UniProtKB as well as 79 putative single amino acid polymorphisms. MScDB retains ~99% of the identifications in comparison to common databases despite a 3-48% increase in the theoretical peptide search space (but comparable protein sequence space). In addition, MScDB enables cross-species applications such as human/mouse graft models, and our results suggest that the uncertainty in protein assignments to one species can be smaller than 20%.

A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics.

We present a peptide library and data resource of >100,000 synthetic, unmodified peptides and their phosphorylated counterparts with known sequences and phosphorylation sites. Analysis of the library by mass spectrometry yielded a data set that we used to evaluate the merits of different search engines (Mascot and Andromeda) and fragmentation methods (beam-type collision-induced dissociation (HCD) and electron transfer dissociation (ETD)) for peptide identification. We also compared the sensitivities and accuracies of phosphorylation-site localization tools (Mascot Delta Score, PTM score and phosphoRS), and we characterized the chromatographic behavior of peptides in the library. We found that HCD identified more peptides and phosphopeptides than did ETD, that phosphopeptides generally eluted later from reversed-phase columns and were easier to identify than unmodified peptides and that current computational tools for proteomics can still be substantially improved. These peptides and spectra will facilitate the development, evaluation and improvement of experimental and computational proteomic strategies, such as separation techniques and the prediction of retention times and fragmentation patterns.