Experiment-specific estimation of peptide identification probabilities using a randomized database

A transcriptome and proteome of the tick Rhipicephalus microplus shaped by the genetic composition of its hosts and developmental stage

The cattle tick, Rhipicephalus microplus, is a monoxenous tick that co-evolved with indicine cattle on the Indian subcontinent. It causes massive damage to livestock worldwide. Cattle breeds present heritable, contrasting phenotypes of tick loads, taurine breeds carrying higher loads of the parasite than indicine breeds. Thus, a useful model is available to analyze mechanisms that determine outcomes of parasitism. We sought to gain insights on these mechanisms and used RNA sequencing and Multidimensional Protein Identification Technology (MudPIT) to generate a transcriptome from whole larvae and salivary glands from nymphs, males and females feeding on genetically susceptible and resistant bovine hosts and their corresponding proteomes. 931,698 reads were annotated into 11,676 coding sequences (CDS), which were manually curated into 116 different protein families. Male ticks presented the most diverse armamentarium of mediators of parasitism. In addition, levels of expression of many genes encoding mediators of parasitism were significantly associated with the level and stage of host immunity and/or were temporally restricted to developmental stages of the tick. These insights should assist in developing novel, sustainable technologies for tick control.

Accurate Estimation of Context-Dependent False Discovery Rates in Top-Down Proteomics

Within the last several years, top-down proteomics has emerged as a high throughput technique for protein and proteoform identification. This technique has the potential to identify and characterize thousands of proteoforms within a single study, but the absence of accurate false discovery rate (FDR) estimation could hinder the adoption and consistency of top-down proteomics in the future. In automated identification and characterization of proteoforms, FDR calculation strongly depends on the context of the search. The context includes MS data quality, the database being interrogated, the search engine, and the parameters of the search. Particular to top-down proteomics-there are four molecular levels of study: proteoform spectral match (PrSM), protein, isoform, and proteoform. Here, a context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We examined several search contexts and found that an FDR calculated at the PrSM level under-reported the true FDR at the protein level by an average of 24-fold. We present a new open-source tool, the TDCD_FDR_Calculator, which provides a scalable, context-dependent FDR calculation that can be applied post-search to enhance the quality of results in top-down proteomics from any search engine.

Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders

Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.

MOPED enables discoveries through consistently processed proteomics data

The Model Organism Protein Expression Database (MOPED, http://moped.proteinspire.org) is an expanding proteomics resource to enable biological and biomedical discoveries. MOPED aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm, and yeast). The latest version of MOPED adds new estimates of protein abundance and concentration as well as relative (differential) expression data. MOPED provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. MOPED supports the Human Proteome Project's efforts to generate chromosome- and diseases-specific proteomes by providing links from proteins to chromosome and disease information as well as many complementary resources. MOPED supports a new omics metadata checklist to harmonize data integration, analysis, and use. MOPED's development is driven by the user community, which spans 90 countries and guides future development that will transform MOPED into a multiomics resource. MOPED encourages users to submit data in a simple format. They can use the metadata checklist to generate a data publication for this submission. As a result, MOPED will provide even greater insights into complex biological processes and systems and enable deeper and more comprehensive biological and biomedical discoveries.

Unraveling the Complexities of Life Sciences Data

The life sciences have entered into the realm of big data and data-enabled science, where data can either empower or overwhelm. These data bring the challenges of the 5 Vs of big data: volume, veracity, velocity, variety, and value. Both independently and through our involvement with DELSA Global (Data-Enabled Life Sciences Alliance, DELSAglobal.org), the Kolker Lab ( kolkerlab.org ) is creating partnerships that identify data challenges and solve community needs. We specialize in solutions to complex biological data challenges, as exemplified by the community resource of MOPED (Model Organism Protein Expression Database, MOPED.proteinspire.org ) and the analysis pipeline of SPIRE (Systematic Protein Investigative Research Environment, PROTEINSPIRE.org ). Our collaborative work extends into the computationally intensive tasks of analysis and visualization of millions of protein sequences through innovative implementations of sequence alignment algorithms and creation of the Protein Sequence Universe tool (PSU). Pushing into the future together with our collaborators, our lab is pursuing integration of multi-omics data and exploration of biological pathways, as well as assigning function to proteins and porting solutions to the cloud. Big data have come to the life sciences; discovering the knowledge in the data will bring breakthroughs and benefits.

Lapatinib-binding protein kinases in the African trypanosome: identification of cellular targets for kinase-directed chemical scaffolds

Human African trypanosomiasis is caused by the eukaryotic microbe Trypanosoma brucei. To discover new drugs against the disease, one may use drugs in the clinic for other indications whose chemical scaffolds can be optimized via a medicinal chemistry campaign to achieve greater potency against the trypanosome. Towards this goal, we tested inhibitors of human EGFR and/or VEGFR as possible anti-trypanosome compounds. The 4-anilinoquinazolines canertinib and lapatinib, and the pyrrolopyrimidine AEE788 killed bloodstream T. brucei in vitro with GI₅₀ in the low micromolar range. Curiously, the genome of T. brucei does not encode EGFR or VEGFR, indicating that the drugs recognize alternate proteins. To discover these novel targets, a trypanosome lysate was adsorbed to an ATP-sepharose matrix and washed with a high salt solution followed by nicotinamide adenine dinucleotide (NAD⁺). Proteins that remained bound to the column were eluted with drugs, and identified by mass spectrometry/bioinformatics. Lapatinib bound to Tb927.4.5180 (termed T. brucei lapatinib-binding protein kinase-1 (TbLBPK1)) while AEE788 bound Tb927.5.800 (TbLBPK2). When the NAD⁺ wash was omitted from the protocol, AEE788, canertinib and lapatinib eluted TbLBPK1, TbLBPK2, and Tb927.3.1570 (TbLBPK3). In addition, both canertinib and lapatinib eluted Tb10.60.3140 (TbLBPK4), whereas only canertinib desorbed Tb10.61.1880 (TbCBPK1). Lapatinib binds to a unique conformation of protein kinases. To gain insight into the structural basis for lapatinib interaction with TbLBPKs, we constructed three-dimensional models of lapatinib•TbLBPK complexes, which confirmed that TbLBPKs can adopt lapatinib-compatible conformations. Further, lapatinib, AEE788, and canertinib were docked to TbLBPKs with favorable scores. Our studies (a) present novel targets of kinase-directed drugs in the trypanosome, and (b) offer the 4-anilinoquinazoline and pyrrolopyrimidines as scaffolds worthy of medicinal chemistry and structural biology campaigns to develop them into anti-trypanosome drugs.

Paramecium bursaria chlorella virus 1 proteome reveals novel architectural and regulatory features of a giant virus

The 331-kbp chlorovirus Paramecium bursaria chlorella virus 1 (PBCV-1) genome was resequenced and annotated to correct errors in the original 15-year-old sequence; 40 codons was considered the minimum protein size of an open reading frame. PBCV-1 has 416 predicted protein-encoding sequences and 11 tRNAs. A proteome analysis was also conducted on highly purified PBCV-1 virions using two mass spectrometry-based protocols. The mass spectrometry-derived data were compared to PBCV-1 and its host Chlorella variabilis NC64A predicted proteomes. Combined, these analyses revealed 148 unique virus-encoded proteins associated with the virion (about 35% of the coding capacity of the virus) and 1 host protein. Some of these proteins appear to be structural/architectural, whereas others have enzymatic, chromatin modification, and signal transduction functions. Most (106) of the proteins have no known function or homologs in the existing gene databases except as orthologs with proteins of other chloroviruses, phycodnaviruses, and nuclear-cytoplasmic large DNA viruses. The genes encoding these proteins are dispersed throughout the virus genome, and most are transcribed late or early-late in the infection cycle, which is consistent with virion morphogenesis.

DIGE and iTRAQ as biomarker discovery tools in aquatic toxicology

Molecular approaches in ecotoxicology have greatly enhanced mechanistic understanding of the impact of aquatic pollutants in organisms. These methods have included high throughput Omics technologies, including quantitative proteomics methods such as 2D differential in-gel electrophoresis (DIGE) and isobaric tagging for relative and absolute quantitation (iTRAQ). These methods are becoming more widely used in ecotoxicology studies to identify and characterize protein bioindicators of adverse effect. In teleost fish, iTRAQ has been used successfully in different fish species (e.g. fathead minnow, goldfish, largemouth bass) and tissues (e.g. hypothalamus and liver) to quantify relative protein abundance. Of interest for ecotoxicology is that many proteins commonly utilized as bioindicators of toxicity or stress are quantifiable using iTRAQ on a larger scale, providing a global baseline of biological effect from which to assess changes in the proteome. This review highlights the successes to date for high throughput quantitative proteomics using DIGE and iTRAQ in aquatic toxicology. Current challenges for the iTRAQ method for biomarker discovery in fish are the high cost and the lack of complete annotated genomes for teleosts. However, the use of protein homology from teleost fishes in protein databases and the introduction of hybrid LTQ-FT (Linear ion trap-Fourier transform) mass spectrometers with high resolution, increased sensitivity, and high mass accuracy are able to improve significantly the protein identification rates. Despite these challenges, initial studies utilizing iTRAQ for ecotoxicoproteomics have exceeded expectations and it is anticipated that the use of non-gel based quantitative proteomics will increase for protein biomarker discovery and for characterization of chemical mode of action.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

SPIRE: Systematic protein investigative research environment

The SPIRE (Systematic Protein Investigative Research Environment) provides web-based experiment-specific mass spectrometry (MS) proteomics analysis (https://www.proteinspire.org). Its emphasis is on usability and integration of the best analytic tools. SPIRE provides an easy to use web-interface and generates results in both interactive and simple data formats. In contrast to run-based approaches, SPIRE conducts the analysis based on the experimental design. It employs novel methods to generate false discovery rates and local false discovery rates (FDR, LFDR) and integrates the best and complementary open-source search and data analysis methods. The SPIRE approach of integrating X!Tandem, OMSSA and SpectraST can produce an increase in protein IDs (52-88%) over current combinations of scoring and single search engines while also providing accurate multi-faceted error estimation. One of SPIRE's primary assets is combining the results with data on protein function, pathways and protein expression from model organisms. We demonstrate some of SPIRE's capabilities by analyzing mitochondrial proteins from the wild type and 3 mutants of C. elegans. SPIRE also connects results to publically available proteomics data through its Model Organism Protein Expression Database (MOPED). SPIRE can also provide analysis and annotation for user supplied protein ID and expression data.

Shotgun proteomics analysis reveals new unsuspected molecular effectors of nitrogen-containing bisphosphonates in osteocytes

Nitrogen-containing bisphosphonates (N-BPs) are therapeutic agents used to treat osteoporosis and promote osteoblast and osteocyte survival. The molecular mechanisms underlying this effect have been extensively studied, but the global changes induced by N-BPs at the protein level are not known. In this context, we investigated the effect of 10(-7)M Risedronate for 1h and 48h on MLO-Y4 osteocytic cells, through a quantitative, label free shotgun proteomic analysis. We described herein a preliminary proteome map of untreated MLO-Y4 cells, composed of 353 protein species. Moreover, we identified 10 and 15 differentially expressed proteins after 1h and 48h of Risedronate treatment, respectively. Among these, PARK7/DJ-1 protein levels were induced up to 3 times and this event was associated with the activation of the pro-survival Akt pathway that we propose as a novel player in the effect of N-BPs on osteocytes. Risedronate was also able to induce the expression and the secretion of the growth factor pro-granulin. In addition, protein prenylation inhibition appeared to be involved in the modulation of MLO-Y4 proteome by RIS in a protein-specific manner. In conclusion, these findings unveil novel functions targeted by N-BPs in osteocytes and could be useful to design novel pharmaceutical compounds.

A proteomics approach to decipher the molecular nature of planarian stem cells

Background

In recent years, planaria have emerged as an important model system for research into stem cells and regeneration. Attention is focused on their unique stem cells, the neoblasts, which can differentiate into any cell type present in the adult organism. Sequencing of the Schmidtea mediterranea genome and some expressed sequence tag projects have generated extensive data on the genetic profile of these cells. However, little information is available on their protein dynamics.

Results

We developed a proteomic strategy to identify neoblast-specific proteins. Here we describe the method and discuss the results in comparison to the genomic high-throughput analyses carried out in planaria and to proteomic studies using other stem cell systems. We also show functional data for some of the candidate genes selected in our proteomic approach.

Conclusions

We have developed an accurate and reliable mass-spectra-based proteomics approach to complement previous genomic studies and to further achieve a more accurate understanding and description of the molecular and cellular processes related to the neoblasts.

Design and initial characterization of the SC-200 proteomics standard mixture

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.

Competition between PARP-1 and Ku70 control the decision between high-fidelity and mutagenic DNA repair

Affinity maturation of antibodies requires a unique process of targeted mutation that allows changes to accumulate in the antibody genes while the rest of the genome is protected from off-target mutations that can be oncogenic. This targeting requires that the same deamination event be repaired either by a mutagenic or a high-fidelity pathway depending on the genomic location. We have previously shown that the BRCT domain of the DNA-damage sensor PARP-1 is required for mutagenic repair occurring in the context of IgH and IgL diversification in the chicken B cell line DT40. Here we show that immunoprecipitation of the BRCT domain of PARP-1 pulls down Ku70 and the DNA-PK complex although the BRCT domain of PARP-1 does not bind DNA, suggesting that this interaction is not DNA dependent. Through sequencing the IgL variable region in PARP-1(-/-) cells that also lack Ku70 or Lig4, we show that Ku70 or Lig4 deficiency restores GCV to PARP-1(-/-) cells and conclude that the mechanism by which PARP-1 is promoting mutagenic repair is by inhibiting high-fidelity repair which would otherwise be mediated by Ku70 and Lig4.

Estimating false discovery rates for peptide and protein identification using randomized databases

MS-based proteomics characterizes protein contents of biological samples. The most common approach is to first match observed MS/MS peptide spectra against theoretical spectra from a protein sequence database and then to score these matches. The false discovery rate (FDR) can be estimated as a function of the score by searching together the protein sequence database and its randomized version and comparing the score distributions of the randomized versus nonrandomized matches. This work introduces a straightforward isotonic regression-based method to estimate the cumulative FDRs and local FDRs (LFDRs) of peptide identification. Our isotonic method not only performed as well as other methods used for comparison, but also has the advantages of being: (i) monotonic in the score, (ii) computationally simple, and (iii) not dependent on assumptions about score distributions. We demonstrate the flexibility of our approach by using it to estimate FDRs and LFDRs for protein identification using summaries of the peptide spectra scores. We reconfirmed that several of these methods were superior to a two-peptide rule. Finally, by estimating both the FDRs and LFDRs, we showed for both peptide and protein identification, moderate FDR values (5%) corresponded to large LFDR values (53 and 60%).

Meta-analysis for protein identification: a case study on yeast data

Large amounts of mass spectrometry (MS) proteomics data are now publicly available; however, little attention has been given to how to best combine these data and assess the error rates for protein identification. The objective of this article is to show how variation in the type and amount of data included with each study impacts coverage of the yeast proteome and estimation of the false discovery rate (FDR). Our analysis of a subset of the publicly available yeast data showed that failure to reevaluate the FDR when combining protein IDs from different experiments resulted in an underestimation of the FDR by approximately threefold. A worst-case approximation of the FDR was only slightly larger than estimating the FDR by randomized database matches. The use of a weighted model to emphasize the most informative experimental data provided an increase in the number of IDs at a 1% FDR when compared to other meta-analysis approaches. Also, using an FDR higher than 1% results in a very high rate of false discoveries for IDs above the 1% threshold. Ideally, raw MS data will be made publicly available for complete and consistent reanalysis. In the circumstance that raw data is not available, determining a combined FDR on the basis of the worst-case estimation provides a reasonable approximation of the FDR. When combining experimental results, adding additional experiments results in diminishing and in some cases negative returns on protein identifications. It may be beneficial to include only those experiments generating the most unique identifications due to solid experimental design and sensitive instrumentation.

Decoy methods for assessing false positives and false discovery rates in shotgun proteomics

The potential of getting a significant number of false positives (FPs) in peptide-spectrum matches (PSMs) obtained by proteomic database search has been well-recognized. Among the attempts to assess FPs, the concomitant use of target and decoy databases is widely practiced. By adjusting filtering criteria, FPs and false discovery rate (FDR) can be controlled at a desired level. Although the target-decoy approach is gaining in popularity, subtle differences in decoy construction (e.g., reversing vs stochastic methods), rate calculation (e.g., total vs unique PSMs), or searching (separate vs composite) do exist among various implementations. In the present study, we evaluated the effects of these differences on FP and FDR estimations using a rat kidney protein sample and the SEQUEST search engine as an example. On the effects of decoy construction, we found that, when a single scoring filter (XCorr) was used, stochastic methods generated a higher estimation of FPs and FDR than sequence reversing methods, likely due to an increase in unique peptides. This higher estimation could largely be attenuated by creating decoy databases similar in effective size but not by a simple normalization with a unique-peptide coefficient. When multiple filters were applied, the differences seen between reversing and stochastic methods significantly diminished, suggesting multiple filterings reduce the dependency on how a decoy is constructed. For a fixed set of filtering criteria, FDR and FPs estimated by using unique PSMs were almost twice those using total PSMs. The higher estimation seemed to be dependent on data acquisition setup. As to the differences between performing separate or composite searches, in general, FDR estimated from the separate search was about three times that from the composite search. The degree of difference gradually decreased as the filtering criteria became more stringent. Paradoxically, the estimated true positives in separate search were higher when multiple filters were used. By analyzing a standard protein mixture, we demonstrated that the higher estimation of FDR and FPs in the separate search likely reflected an overestimation, which could be corrected with a simple merging procedure. Our study illustrates the relative merits of different implementations of the target-decoy strategy, which should be worth contemplating when large-scale proteomic biomarker discovery is to be attempted.