What's driving false discovery rates?

Computational approaches to identify sites of phosphorylation

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

An efficient solution for resolving iTRAQ and TMT channel cross-talk

Isobaric tagging reagents such as isobaric tag for relative and absolute quantitation (iTRAQ) and tandem mass tag (TMT) typically have isotopic impurities that cause significant cross-talk between channels. Here, we present an efficient solution to compensate for channel cross-talk using linear algebra and find that it is between 20× and 120× faster than previous methods. We also find that the effects of channel cross-talk are as important to manage as the effects of ratio compression because of precursor impurities, and we have released an open-source tool to perform both types of calculations.

Analysis of the uterine lumen in fertility-classified heifers: II. Proteins and metabolites†

Survival and growth of the bovine conceptus is dependent on endometrial secretions or histotroph. Previously, serial blastocyst transfer was used to classify heifers as high fertile (HF), subfertile (SF), or infertile (IF). Here, we investigated specific histotroph components (proteins and metabolites) in the uterine lumen of day 17 fertility-classified heifers. Interferon tau (IFNT) was more abundant in uterine lumenal fluid (ULF) of pregnant HF than SF animals as the conceptus was longer in HF heifers. However, no differences in endometrial expression of selected classical and nonclassical interferon-stimulated genes (ISGs) were observed, suggesting that IFNT signaling in the endometrium of pregnant HF and SF heifers was similar. Pregnancy significantly increased the abundance of several proteins in ULF. Based on functional annotation, the abundance of a number of proteins involved in energy metabolism, oxidative stress, amino acid metabolism, and cell proliferation and differentiation were greater in the ULF of pregnant HF than SF heifers. Metabolomics analysis found that pregnancy only changed the metabolome composition of ULF from HF heifers. The majority of the metabolites that increased in the ULF of pregnant HF as compared to SF heifers were associated with energy and amino acid metabolism. The observed differences in ULF proteome and metabolome are hypothesized to influence uterine receptivity with consequences on conceptus development and survival in fertility-classified heifers.

miR-192-5p Silencing by Genetic Aberrations Is a Key Event in Hepatocellular Carcinomas with Cancer Stem Cell Features

Various cancer stem cell (CSC) biomarkers have been identified for hepatocellular carcinoma (HCC), but little is known about the implications of heterogeneity and shared molecular networks within the CSC population. Through miRNA profile analysis in an HCC cohort (n = 241) for five groups of CSC⁺ HCC tissues, i.e., EpCAM⁺, CD90⁺, CD133⁺, CD44⁺, and CD24⁺ HCC, we identified a 14-miRNA signature commonly altered among these five groups of CSC⁺ HCC. miR-192-5p, the top-ranked CSC miRNA, was liver-abundant and -specific and markedly downregulated in all five groups of CSC⁺ HCC from two independent cohorts (n = 613). Suppressing miR-192-5p in HCC cells significantly increased multiple CSC populations and CSC-related features through targeting PABPC4. Both TP53 mutation and hypermethylation of the mir-192 promoter impeded transcriptional activation of miR-192-5p in HCC cell lines and primary CSC⁺ HCC. This study reveals the circuit from hypermethylation of the mir-192 promoter through the increase in PABPC4 as a shared genetic regulatory pathway in various groups of primary CSC⁺ HCC. This circuit may be the driver that steers liver cells toward hepatic CSC cells, leading to hepatic carcinogenesis. SIGNIFICANCE: miR-192-5p and its regulatory pathway is significantly abolished in multiple groups of HCC expressing high levels of CSC markers, which may represent a key event for hepatic carcinogenesis.

Redox-based reagents for chemoselective methionine bioconjugation

Cysteine can be specifically functionalized by a myriad of acid-base conjugation strategies for applications ranging from probing protein function to antibody-drug conjugates and proteomics. In contrast, selective ligation to the other sulfur-containing amino acid, methionine, has been precluded by its intrinsically weaker nucleophilicity. Here, we report a strategy for chemoselective methionine bioconjugation through redox reactivity, using oxaziridine-based reagents to achieve highly selective, rapid, and robust methionine labeling under a range of biocompatible reaction conditions. We highlight the broad utility of this conjugation method to enable precise addition of payloads to proteins, synthesis of antibody-drug conjugates, and identification of hyperreactive methionine residues in whole proteomes.

Uterine glands impact uterine receptivity, luminal fluid homeostasis and blastocyst implantation

Uterine glands are essential for pregnancy in mice and likely humans, because they secrete or transport bioactive substances that regulate uterine receptivity for blastocyst implantation. In mice, the uterus becomes receptive to blastocyst implantation on day 4, but is refractory by day 5. Here, blastocysts could be recovered from progesterone-induced uterine gland (PUGKO) but not wildtype (WT) mice on day 5 post-mating. Anti-adhesive Muc1 protein and microvilli were present on the luminal epithelium of PUGKO but not WT uteri. A number of known uterine receptivity genes and gland-specific genes were altered in the PUGKO uterus. Next, the uterus and uterine luminal fluid (ULF) were obtained from WT and PUGKO mice on day 3, 4 and 5. Transcriptome analysis revealed that 580 genes were decreased in the PUGKO uterus, however ULF secrotome analysis revealed that many proteins and several amino acids were increased in the PUGKO ULF. Of note, many proteins encoded by many gland-specific genes were not identified in the ULF of WT mice. These results support the ideas that uterine glands secrete factors that regulate ULF homeostasis and interact with other cell types in the uterus to influence uterine receptivity and blastocyst implantation for the establishment of pregnancy.

Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC-ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, our analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.

Warburg effect linked to cognitive-executive deficits in FMR1 premutation

A 55-200 CGG repeat expansion in the 5'-UTR of the fragile X mental retardation 1 (FMR1) gene is known as a premutation. Some carriers are affected by the neurodegenerative disorder fragile X-associated tremor/ataxia syndrome (FXTAS), primary ovarian insufficiency, and neurobehavioral impairments. Based on the mitochondrial dysfunction observed in fibroblasts and brain samples from carriers, as well as in neurons and brains from a mouse model of the premutation, we evaluated the presence of the Warburg effect in peripheral blood mononuclear cells (PBMCs) from 30 premutation carriers with either a rebalance of the metabolism [increasing glycolysis while decreasing oxidative phosphorylation (oxphos)] or a metabolic amplification (increasing glycolysis while maintaining/increasing oxphos). Deficits in oxphos-more pronounced in FXTAS-affected subjects-were accompanied by a shift toward glycolysis, suggesting increased glycolysis despite aerobic conditions. Differential proteomics extended these findings, unveiling a decreased antioxidant response, translation, and disrupted extracellular matrix and cytoskeleton organization with activation of prosenescence pathways. Lower bioenergetics segregated with increased incidence of low executive function, tremors, below-average IQ, and FXTAS. The combination of functional and proteomic data unveiled new mechanisms related to energy production in the premutation, showing the potential of being applicable to other psychiatric disorders to identify endophenotype-specific responses relevant to neurobiology.-Napoli, E., Song, G., Schneider, A., Hagerman, R., Eldeeb, M. A. A. A., Azarang, A., Tassone, F., Giulivi, C. Warburg effect linked to cognitive-executive deficits in FMR1 premutation.

Proteome and pathway effects of chronic haloperidol treatment in mouse hippocampus

Proteomic exploration of the effects of psychotropic drugs on specific brain areas in rodents has the potential to uncover novel molecular networks and pathways affected by psychotropic medications, and may inform etiologic hypotheses on mental disorders. Haloperidol, a widely used first-generation antipsychotic, has been shown to produce structural and functional changes of the hippocampus, a brain region also implicated in the neuropathology of disorders such as schizophrenia and bipolar disorder. Seven adult male C57BL/6 mice were injected daily intraperitoneally with 0.5 mg/kg of haloperidol, for 28 days. A control group of six animals was injected with vehicle only (saline). Protein levels of postmortem hippocampus homogenate were determined using label-free LC/MS/MS. In the treatment group, 216 differentially expressed hippocampal proteins were identified as compared to controls. Ingenuity pathway analysis implicated oxidative phosphorylation and mitochondrial function as top canonical pathways, and local networks involved in tubulin-mediated cytoskeleton dynamics, clathrin-mediated endocytosis, and extracellular signal-regulated kinase and c-Jun N-terminal kinase signaling. The findings of this study could stimulate further research into the cellular mechanisms associated with haloperidol treatment and the pathophysiology of psychotic disorders, assisting treatment biomarker discovery. All MS data have been deposited in the ProteomeXchange with identifier PXD002250 (http://proteomecentral.proteomexchange.org/dataset/PXD002250).

Proteomics in the Assessment of the Therapeutic Response of Antineoplastic Drugs: Strategies and Practical Applications

Uncovering unknown pathological mechanisms and body response to applied medication are the driving forces toward personalized medicine. In this post-genomic era, all eyes are turned to the proteomics field, searching for answers and explanations by investigating the gene end point functional units-proteins and their proteoforms. The development of cutting-edge mass spectrometric technologies and bioinformatics tools have allowed the life-science community to discover disease-specific proteins as biomarkers, which are often concealed by high sample complexity and dynamic range of abundance. Currently, there are several proteomics-based approaches to investigate the proteome. This chapter focuses on gold standard proteomics strategies and related issues toward candidate biomarker discovery, which may have diagnostic/prognostic as well as mechanistic utility in cancer drug resistance.

False Discovery Rate Estimation in Proteomics

With the advancement in proteomics separation techniques and improvements in mass analyzers, the data generated in a mass-spectrometry based proteomics experiment is rising exponentially. Such voluminous datasets necessitate automated computational tools for high-throughput data analysis and appropriate statistical control. The data is searched using one or more of the several popular database search algorithms. The matches assigned by these tools can have false positives and statistical validation of these false matches is necessary before making any biological interpretations. Without such procedures, the biological inferences do not hold true and may be outright misleading. There is a considerable overlap between true and false positives. To control the false positives amongst a set of accepted matches, there is a need for some statistical estimate that can reflect the amount of false positives present in the data processed. False discovery rate (FDR) is the metric for global confidence assessment of a large-scale proteomics dataset. This chapter covers the basics of FDR, its application in proteomics, and methods to estimate FDR.

Anopheles stephensi p38 MAPK signaling regulates innate immunity and bioenergetics during Plasmodium falciparum infection

Background

Fruit flies and mammals protect themselves against infection by mounting immune and metabolic responses that must be balanced against the metabolic needs of the pathogens. In this context, p38 mitogen-activated protein kinase (MAPK)-dependent signaling is critical to regulating both innate immunity and metabolism during infection. Accordingly, we asked to what extent the Asian malaria mosquito Anopheles stephensi utilizes p38 MAPK signaling during infection with the human malaria parasite Plasmodium falciparum.

Methods

A. stephensi p38 MAPK (AsP38 MAPK) was identified and patterns of signaling in vitro and in vivo (midgut) were analyzed using phospho-specific antibodies and small molecule inhibitors. Functional effects of AsP38 MAPK inhibition were assessed using P. falciparum infection, quantitative real-time PCR, assays for reactive oxygen species and survivorship under oxidative stress, proteomics, and biochemical analyses.

Results

The genome of A. stephensi encodes a single p38 MAPK that is activated in the midgut in response to parasite infection. Inhibition of AsP38 MAPK signaling significantly reduced P. falciparum sporogonic development. This phenotype was associated with AsP38 MAPK regulation of mitochondrial physiology and stress responses in the midgut epithelium, a tissue critical for parasite development. Specifically, inhibition of AsP38 MAPK resulted in reduction in mosquito protein synthesis machinery, a shift in glucose metabolism, reduced mitochondrial metabolism, enhanced production of mitochondrial reactive oxygen species, induction of an array of anti-parasite effector genes, and decreased resistance to oxidative stress-mediated damage. Hence, P. falciparum-induced activation of AsP38 MAPK in the midgut facilitates parasite infection through a combination of reduced anti-parasite immune defenses and enhanced host protein synthesis and bioenergetics to minimize the impact of infection on the host and to maximize parasite survival, and ultimately, transmission.

Conclusions

These observations suggest that, as in mammals, innate immunity and mitochondrial responses are integrated in mosquitoes and that AsP38 MAPK-dependent signaling facilitates mosquito survival during parasite infection, a fact that may attest to the relatively longer evolutionary relationship of these parasites with their invertebrate compared to their vertebrate hosts. On a practical level, improved understanding of the balances and trade-offs between resistance and metabolism could be leveraged to generate fit, resistant mosquitoes for malaria control.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-015-1016-x) contains supplementary material, which is available to authorized users.

'Conceptualizing' the Endometrium: Identification of Conceptus-Derived Proteins During Early Pregnancy in Cattle

The aim of this study was to identify conceptus-derived proteins, in addition to IFNT, that may facilitate pregnancy recognition in cattle. Analysis of the protein content of the uterine luminal fluid (ULF) from cyclic heifers on Day 16 by nano liquid chromatography tandem mass spectrometry identified 334 proteins. Comparison of these data with 299 proteins identified in the ULF of pregnant heifers on Day 16 identified 85 proteins only present in the ULF of pregnant heifers. Analysis of Day 16 conceptus-conditioned culture medium revealed the presence of 1005 proteins of which 30 proteins were unique to ULF from Day 16 pregnant heifers. Of these 30 proteins, 12 had mRNA expression values at least 2-fold higher in abundance (P < 0.05) in the conceptus compared to the endometrium (ARPC5L, CAPG, CKMT1, CSTB, HSPA8, HSPE1, LGALS3, MSN, NUTF2, P4HB, PRKAR2A, TKT) as determined by RNA sequencing. In addition, genes that have a significant biological interaction with the proteins (ACO2, CKMT1, CSTB, EEF2, GDI1, GLB1, GPLD1, HNRNPA1, HNRNPA2B1, HNRNPF, HSPA8, HSPE1, IDH2, KRT75, LGALS3, MSN, NUTF2, P4HB, PRKAR2A, PSMA4, PSMB5, PSMC4, SERPINA3, TKT) were differentially expressed in the endometrium of pregnant compared to cyclic heifers during the pregnancy recognition period (Days 16-18). These results indicate that 30 proteins unique to ULF from pregnant heifers and produced by short-term in vitro cultured Day 16 conceptuses could potentially be involved in facilitating the interactions between the conceptus and the endometrium during the pregnancy recognition period.

Shotgun proteomic analysis unveils survival and detoxification strategies by Caulobacter crescentus during exposure to uranium, chromium, and cadmium

The ubiquitous bacterium Caulobacter crescentus holds promise to be used in bioremediation applications due to its ability to mineralize U(VI) under aerobic conditions. Here, cell free extracts of C. crescentus grown in the presence of uranyl nitrate [U(VI)], potassium chromate [Cr(VI)], or cadmium sulfate [Cd(II)] were used for label-free proteomic analysis. Proteins involved in two-component signaling and amino acid metabolism were up-regulated in response to all three metals, and proteins involved in aerobic oxidative phosphorylation and chemotaxis were down-regulated under these conditions. Clustering analysis of proteomic enrichment revealed that the three metals also induce distinct patterns of up- or down-regulated expression among different functional classes of proteins. Under U(VI) exposure, a phytase enzyme and an ABC transporter were up-regulated. Heat shock and outer membrane responses were found associated with Cr(VI), while efflux pumps and oxidative stress proteins were up-regulated with Cd(II). Experimental validations were performed on select proteins. We found that a phytase plays a role in U(VI) and Cr(VI) resistance and detoxification and that a Cd(II)-specific transporter confers Cd(II) resistance. Interestingly, analysis of promoter regions in genes associated with differentially expressed proteins suggests that U(VI) exposure affects cell cycle progression.

Mitochondrial proteome remodeling in ischemic heart failure

AIMS

Mitochondrial dysfunction is an important part of the decline in cardiac function in heart failure. We hypothesized for hypothesized that there would be specific abnormalities in mitochondrial function and proteome with the progression of ischemic heart failure (HF).

MAIN METHODS

We used a high left anterior descending artery (LAD) ligation in 3-4month old male rats to generate HF. Rats were studied 9weeks post-ligation.

KEY FINDINGS

Electron microscopy of left ventricle samples showed mitochondrial changes including decreased size, increased number, abnormal distribution, and cristae loss. Mitochondria in ischemic HF exhibited decreased total ATP, impaired mitochondrial respiration, as well as reduced complex I activity. Analysis of LV mitochondrial proteins by mass spectrometry was performed, and 31 differentially expressed proteins (p<0.05) of more than 500 total proteins were identified. Of these proteins, 15 were up-regulated and 16 were down-regulated in the failing heart. A set of complex I proteins was significantly decreased, consistent with the impairment of complex I activity. There were distinct changes in mitochondrial function and proteome in ischemic HF. Although there were similarities, the distinction between the reported proteomic changed with TAC pressure overload induced HF and ischemic HF in the current study suggested different pathological mechanisms.

SIGNIFICANCE

Specific changes in mitochondrial protein expression, which correlate with changes in mitochondrial function, have been identified in ischemic HF for the first time.

Stable isotope metabolic labeling-based quantitative phosphoproteomic analysis of Arabidopsis mutants reveals ethylene-regulated time-dependent phosphoproteins and putative substrates of constitutive triple response 1 kinase

Ethylene is an important plant hormone that regulates numerous cellular processes and stress responses. The mode of action of ethylene is both dose- and time-dependent. Protein phosphorylation plays a key role in ethylene signaling, which is mediated by the activities of ethylene receptors, constitutive triple response 1 (CTR1) kinase, and phosphatase. To address how ethylene alters the cellular protein phosphorylation profile in a time-dependent manner, differential and quantitative phosphoproteomics based on (15)N stable isotope labeling in Arabidopsis was performed on both one-minute ethylene-treated Arabidopsis ethylene-overly-sensitive loss-of-function mutant rcn1-1, deficient in PP2A phosphatase activity, and a pair of long-term ethylene-treated wild-type and loss-of-function ethylene signaling ctr1-1 mutants, deficient in mitogen-activated kinase kinase kinase activity. In total, 1079 phosphopeptides were identified, among which 44 were novel. Several one-minute ethylene-regulated phosphoproteins were found from the rcn1-1. Bioinformatic analysis of the rcn1-1 phosphoproteome predicted nine phosphoproteins as the putative substrates for PP2A phosphatase. In addition, from CTR1 kinase-enhanced phosphosites, we also found putative CTR1 kinase substrates including plastid transcriptionally active protein and calcium-sensing receptor. These regulatory proteins are phosphorylated in the presence of ethylene. Analysis of ethylene-regulated phosphosites using the group-based prediction system with a protein-protein interaction filter revealed a total of 14 kinase-substrate relationships that may function in both CTR1 kinase- and PP2A phosphatase-mediated phosphor-relay pathways. Finally, several ethylene-regulated post-translational modification network models have been built using molecular systems biology tools. It is proposed that ethylene regulates the phosphorylation of arginine/serine-rich splicing factor 41, plasma membrane intrinsic protein 2A, light harvesting chlorophyll A/B binding protein 1.1, and flowering bHLH 3 proteins in a dual-and-opposing fashion.

Peppy: proteogenomic search software

Proteogenomic searching is a useful method for identifying novel proteins, annotating genes and detecting peptides unique to an individual genome. The approach, however, can be laborious, as it often requires search segmentation and the use of several unintegrated tools. Furthermore, many proteogenomic efforts have been limited to small genomes, as large genomes can prove impractical due to the required amount of computer memory and computation time. We present Peppy, a software tool designed to perform every necessary task of proteogenomic searches quickly, accurately and automatically. The software generates a peptide database from a genome, tracks peptide loci, matches peptides to MS/MS spectra and assigns confidence values to those matches. Peppy automatically performs a decoy database generation, search and analysis to return identifications at the desired false discovery rate threshold. Written in Java for cross-platform execution, the software is fully multithreaded for enhanced speed. The program can run on regular desktop computers, opening the doors of proteogenomic searching to a wider audience of proteomics and genomics researchers. Peppy is available at http://geneffects.com/peppy .

STEPS: a grid search methodology for optimized peptide identification filtering of MS/MS database search results

For bottom-up proteomics, there are wide variety of database-searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid-search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection--referred to as STEPS--utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true-positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types.

Proteomics investigations of HDL: challenges and promise

High density lipoprotein (HDL) is recognized as the major negative risk factor of cardiovascular disease and number of anti-atherogenic functions has been ascribed to HDL. HDL is an assembly of a neutral lipid core and an outer shell consisting of polar lipids and proteins. It has been defined many different ways based on various distinct properties including density flotation, protein composition, molecular size, and electrophoretic migration. Overall the studies characterizing HDL clearly demonstrate that it is a complex heterogeneous mixture of particles. Furthermore several studies convincingly demonstrated that certain populations of HDL particles have a distinct functionality suggesting that HDL may serve as a platform for assembly of protein complexes with very specific biological functions. Indeed recent proteomics studies described over 100 proteins associated with HDL. Here we review approaches to isolation and proteomic analysis of HDL and discuss potential problems associated with isolation methods which may confound our understanding of the relation of the HDL composition and its biological function.

Peptide reranking with protein-peptide correspondence and precursor peak intensity information

Searching tandem mass spectra against a protein database has been a mainstream method for peptide identification. Improving peptide identification results by ranking true Peptide-Spectrum Matches (PSMs) over their false counterparts leads to the development of various reranking algorithms. In peptide reranking, discriminative information is essential to distinguish true PSMs from false PSMs. Generally, most peptide reranking methods obtain discriminative information directly from database search scores or by training machine learning models. Information in the protein database and MS1 spectra (i.e., single stage MS spectra) is ignored. In this paper, we propose to use information in the protein database and MS1 spectra to rerank peptide identification results. To quantitatively analyze their effects to peptide reranking results, three peptide reranking methods are proposed: PPMRanker, PPIRanker, and MIRanker. PPMRanker only uses Protein-Peptide Map (PPM) information from the protein database, PPIRanker only uses Precursor Peak Intensity (PPI) information, and MIRanker employs both PPM information and PPI information. According to our experiments on a standard protein mixture data set, a human data set and a mouse data set, PPMRanker and MIRanker achieve better peptide reranking results than PetideProphet, PeptideProphet+NSP (number of sibling peptides) and a score regularization method SRPI. The source codes of PPMRanker, PPIRanker, and MIRanker, and all supplementary documents are available at our website: http://bioinformatics.ust.hk/pepreranking/. Alternatively, these documents can also be downloaded from: http://sourceforge.net/projects/pepreranking/.

Proteomic characterization of specific minor proteins in the human milk casein fraction

Human milk contains many bioactive proteins that are likely to support the early development of the newborn. The aim of this study was to identify whether there are specific minor proteins associated with the human milk casein micelle prepared by the acid precipitation method. Protein identification was performed by liquid chromatography tandem mass spectrometry analysis. Eighty-two proteins were identified in the casein micelle, 18 of which are not present in their whey compartment. Thirty-two of these proteins specifically associated with the casein micelle have not previously been identified in human milk or colostrum. Proteins involved in immune function comprised the major part (28%) of total proteins, and another significant part is involved in metabolism/energy production (22%). Most of the proteins were of extracellular or cytoplasmic origin (accounting for 50 and 29%, respectively). This study indicates that various soluble proteins should be considered as part of the casein compartment, prepared by the acid precipitation method. The data provide new insight not only into the proteomic profile of the human milk casein micelle and its physiological significance, but also into the proper proportion of casein and casein-associated proteins to use in infant formula.

Characterization of the MDSC proteome associated with metastatic murine mammary tumors using label-free mass spectrometry and shotgun proteomics

Expansion of Gr-1+/CD11b+ myeloid derived suppressor cells (MDSCs) is governed by the presence of increasingly metastatic, malignant primary tumors. Metastasis, not the primary tumor, is often the cause of mortality. This study sought to fully characterize the MDSC proteome in response to metastatic and non-metastatic mammary tumors using label-free mass spectrometry shotgun proteomics in a mouse model with tumor cell lines, 67NR and 4T1, derived from the same tumor. 67NR cells form only primary mammary tumors, whereas 4T1 cells readily metastasize to the lungs, lymph nodes, and blood. Overall analysis identified a total of 2825 protein groups with a 0.78% false discovery rate. Of the 2814 true identifications, 43 proteins were exclusive to the 67NR group, 153 were exclusive to the 4T1 group, and 2618 were shared. Among the shared cohort, 26 proteins were increased and 31 were decreased in the metastatic 4T1 cohort compared to non-metastatic 67NR controls after filtering. MDSCs selectively express proteins involved in the γ-glutamyl transferase, glutathione synthase pathways, CREB transcription factor signaling, and other pathways involved in platelet aggregation, as well as lipid and amino acid metabolism, in response to highly metastatic 4T1 tumors. Cell cycle regulation dominated protein pathways and ontological groups of the 67NR non-metastatic group. Not only does this study provide a starting point to identify potential biomarkers of metastasis expressed by MDSCs; it identifies critical pathways that are unique to non-metastatic and metastatic conditions. Therapeutic interventions aimed at these pathways in MDSC may offer a new route to control malignancy and metastasis.

Using ion mobility data to improve peptide identification: intrinsic amino acid size parameters

A new method for enhancing peptide ion identification in proteomics analyses using ion mobility data is presented. Ideally, direct comparisons of experimental drift times (t(D)) with a standard mobility database could be used to rank candidate peptide sequence assignments. Such a database would represent only a fraction of sequences in protein databases and significant difficulties associated with the verification of data for constituent peptide ions would exist. A method that employs intrinsic amino acid size parameters to obtain ion mobility predictions that can be used to rank candidate peptide ion assignments is proposed. Intrinsic amino acid size parameters have been determined for doubly charged peptide ions from an annotated yeast proteome. Predictions of ion mobilities using the intrinsic size parameters are more accurate than those obtained from a polynomial fit to t(D) versus molecular weight data. More than a 2-fold improvement in prediction accuracy has been observed for a group of arginine-terminated peptide ions 12 residues in length. The use of this predictive enhancement as a means to aid peptide ion identification is discussed, and a simple peptide ion scoring scheme is presented.

REVEILLE8 and PSEUDO-REPONSE REGULATOR5 form a negative feedback loop within the Arabidopsis circadian clock

Circadian rhythms provide organisms with an adaptive advantage, allowing them to regulate physiological and developmental events so that they occur at the most appropriate time of day. In plants, as in other eukaryotes, multiple transcriptional feedback loops are central to clock function. In one such feedback loop, the Myb-like transcription factors CCA1 and LHY directly repress expression of the pseudoresponse regulator TOC1 by binding to an evening element (EE) in the TOC1 promoter. Another key regulatory circuit involves CCA1 and LHY and the TOC1 homologs PRR5, PRR7, and PRR9. Purification of EE-binding proteins from plant extracts followed by mass spectrometry led to the identification of RVE8, a homolog of CCA1 and LHY. Similar to these well-known clock genes, expression of RVE8 is circadian-regulated with a dawn phase of expression, and RVE8 binds specifically to the EE. However, whereas cca1 and lhy mutants have short period phenotypes and overexpression of either gene causes arrhythmia, rve8 mutants have long-period and RVE8-OX plants have short-period phenotypes. Light input to the clock is normal in rve8, but temperature compensation (a hallmark of circadian rhythms) is perturbed. RVE8 binds to the promoters of both TOC1 and PRR5 in the subjective afternoon, but surprisingly only PRR5 expression is perturbed by overexpression of RVE8. Together, our data indicate that RVE8 promotes expression of a subset of EE-containing clock genes towards the end of the subjective day and forms a negative feedback loop with PRR5. Thus RVE8 and its homologs CCA1 and LHY function close to the circadian oscillator but act via distinct molecular mechanisms.

Mass spectrometry in high-throughput proteomics: ready for the big time

Mass spectrometry has evolved and matured to a level where it is able to assess the complexity of the human proteome. We discuss some of the expected challenges ahead and promising strategies for success.

OVNIp: an open source application facilitating the interpretation, the validation and the edition of proteomics data generated by MS analyses and de novo sequencing

Several academic software are available to help the validation and reporting of proteomics data generated by MS analyses. However, to our knowledge, none of them have been conceived to meet the particular needs generated by the study of organisms whose genomes are not sequenced. In that context, we have developed OVNIp, an open-source application which facilitates the whole process of proteomics results interpretation. One of its unique attributes is its capacity to compile multiple results (from several search engines and/or several databank searches) with a resolution of conflicting interpretations. Moreover, OVNIp enables automated exploitation of de novo sequences generated from unassigned MS/MS spectra leading to higher sequence coverage and enhancing confidence in the identified proteins. The exploitation of these additional spectra might also identify novel proteins through a MS-BLAST search, which can be easily ran from the OVNIp interface. Beyond this primary scope, OVNIp can also benefit to users who look for a simple standalone application to both visualize and confirm MS/MS result interpretations through a simple graphical interface and generate reports according to user-defined forms which may integrate the prerequisites for publication. Sources, documentation and a stable release for Windows are available at http://wwwappli.nantes.inra.fr:8180/OVNIp.

Proteome analysis of mouse model systems: A tool to model human disease and for the investigation of tissue-specific biology

The molecular dissections of the mechanistic pathways involved in human disease have always relied on the use of model organisms. Among the higher mammalian organisms, the laboratory mouse (Mus musculus) is the most widely used model. A large number of commercially-available, inbred strains are available to the community, including an ever growing collection of transgenic, knock-out, and disease models. Coupled to availability is the fact that animal colonies can be kept under standardized housing condition at most major universities and research institutes, with relative ease and cost efficiency (compared to larger vertebrates). As such, mouse models to study human biology and disease remains extremely attractive. In the current review we will provide an historic overview of the use of mouse models in proteome research with a focus on general tissue and organelle biology, comparative proteomics of human and mouse and the use of mouse models to study cardiac disease.

Role of spectral counting in quantitative proteomics

Spectral count, defined as the total number of spectra identified for a protein, has gained acceptance as a practical, label-free, semiquantitative measure of protein abundance in proteomic studies. In this review, we discuss issues affecting the performance of spectral counting relative to other label-free methods, as well as its limitations. Possible consequences of modifications, which are commonly applied to raw spectral counts to improve abundance estimations, are considered. The use of spectral counting for different types of quantitation studies is explored and critiqued. Different statistical methods and underlying frameworks that have been applied to spectral count analysis are described and compared, and problem areas that undermine confident statistical analysis are considered. Finally, the issue of accurate estimation of false-discovery rates is addressed and identified as a major current challenge in quantitative proteomics.

Two-phase Filtering Strategy for Efficient Peptide Identification from Mass Spectrometry

Peptide identification by tandem mass spectrometry (MS/MS) is one of the most important problems in proteomics. Recent advances in high throughput MS/MS experiments result in huge amount of spectra, and the peptide identification process should keep pace. In this paper, we strive to achieve high accuracy and efficiency for peptide identification with the presence of noise by a two-phase filtering strategy. Our algorithm transforms spectra to high dimensional vectors, and then uses self-organizing map (SOM) and multi-point range query (MPRQ) as very efficient coarse filters to select a number of candidate peptides from database. These candidate peptides are subsequently scored and ranked by an accurate tag-based scoring function S(λ). Experiments showed that our approach is both fast and accurate for peptide identification.

Computational approaches to peptide identification via tandem MS

The peptide identification problem lies at the heart of modern proteomic methodology, from which the presence of a particular protein or proteins in a sample may be inferred. The challenge is to find the most likely amino acid sequence, which corresponds to each tandem mass spectrum that has been collected, and produce some kind of score and associated statistical measure that the putative identification is correct. This approach assumes that the peptide (and parent protein) sequence in question is known and is present in the database which is to be searched, as opposed to de novo methods, which seek to identify the peptide ab initio. This chapter will provide an overview of the methods that common, popular software tools employ to search protein sequence databases to provide the non-expert reader with sufficient background to appreciate the choices they can make. This will cover the approaches used to compare experimental and theoretical spectra and some of the methods used to validate and provide higher confidence in the assignments.

Assessment of metabolome annotation quality: a method for evaluating the false discovery rate of elemental composition searches

BACKGROUND

In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed.

METHODOLOGY/PRINCIPAL FINDINGS

The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30-50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries.

CONCLUSIONS/SIGNIFICANCE

High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR <10%). In addition, a small, customized compound database is preferable for high-quality annotation of metabolome data.

A refined method to calculate false discovery rates for peptide identification using decoy databases

Using decoy databases to estimate the number of false positive assignations is one of the most widely used methods to calculate false discovery rates in large-scale peptide identification studies. However, in spite of their widespread use, the decoy approach has not been fully standardized. In conjunction with target databases, decoy databases may be used separately or in the form of concatenated databases, allowing a competition strategy; depending on the method used, two alternative formulations are possible to calculate error rates. Although both methods are conservative, the separate database approach overestimates the number of false positive assignations due to the presence of MS/MS spectra produced by true peptides, while the concatenated approach calculates the error rate in a population that has a higher size than that obtained after searching a target database. In this work, we demonstrate that by analyzing as a whole the joint distribution of matches obtained after performing a separate database search, and applying the competition strategy, it is possible to make a more accurate calculation of false discovery rates. We show that both separate and concatenated approaches clearly overestimate error rates with respect to those calculated by the new algorithm, using several kinds of scores. We conclude that the new indicator provides a more sensitive alternative, and establishes for the first time a unique and integrated framework to calculate error rates in large-scale peptide identification studies.

Mining gene functional networks to improve mass-spectrometry-based protein identification

Motivation: High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly.

Results: We develop a method that analyzes MS/MS experiments in the larger context of the biological processes active in a cell. Our method, MSNet, improves protein identification in shotgun proteomics experiments by considering information on functional associations from a gene functional network. MSNet substantially increases the number of proteins identified in the sample at a given error rate. We identify 8–29% more proteins than the original MS experiment when applied to yeast grown in different experimental conditions analyzed on different MS/MS instruments, and 37% more proteins in a human sample. We validate up to 94% of our identifications in yeast by presence in ground-truth reference sets.

Availability and Implementation: Software and datasets are available at http://aug.csres.utexas.edu/msnet

Contact:miranker@cs.utexas.edu, marcotte@icmb.utexas.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Rapid validation of Mascot search results via stable isotope labeling, pair picking, and deconvolution of fragmentation patterns

Conventional LC-MS/MS data analysis matches each precursor ion and fragmentation pattern to their best fit within databases of theoretical spectra, yielding a peptide identification. Confidence is estimated by a score but can be validated by statistics, false discovery rates, and/or manual validation. A weakness is that each ion is evaluated independently, discarding potentially useful cross-correlations. In a classical approach to de novo sequence analysis, mixtures of peptides differing only in a carboxyl-terminal isotopic label yield fragmentation spectra with single, unlabeled b-type ions but pairs of isotope-labeled y-type ions, facilitating confident assignments. To apply this principle to identification by fragmentation pattern matching, we developed Validator, software that recognizes isotopic peptide pairs and compares their identifications and fragmentation patterns. Testing Validator 1 on a Mascot results file from FT-ICR LC-MS/MS of (16)O/(18)O-labeled yeast cell lysate peptides yielded 2,775 peptide pairs sharing a common identification but differing in carboxyl-terminal label. Comparing observed b- and y-ions with the predicted fragmentation pattern improved the threshold Mascot score for 5% false discovery from 36 to 22, significantly increasing both sensitivity and specificity. Validator 2, which identifies pairs by precursor mass difference alone before comparing observed fragmentation with that predicted by Mascot, found 2,021 isotopic pairs, similarly achieving improved sensitivity and specificity. Finally Validator 3, which finds pairs based on mass difference alone and then deconvolutes fragmentation patterns independently of Mascot, found 964 predicted peptides. Validator 3 allowed raw mass spectrometry data to be mined not only to validate Mascot results but also to discover peptides missed by Mascot. Using standard desktop hardware, the Validator 1-3 software processed the 11,536 spectra in the 93-MB Mascot .DAT file in less than 6 min (32 spectra/s), revealing high confidence peptide identifications without regard to Mascot score, far faster than manual or other independent validation methods.

Statistical model to analyze quantitative proteomics data obtained by 18O/16O labeling and linear ion trap mass spectrometry: application to the study of vascular endothelial growth factor-induced angiogenesis in endothelial cells

Statistical models for the analysis of protein expression changes by stable isotope labeling are still poorly developed, particularly for data obtained by 16O/18O labeling. Besides large scale test experiments to validate the null hypothesis are lacking. Although the study of mechanisms underlying biological actions promoted by vascular endothelial growth factor (VEGF) on endothelial cells is of considerable interest, quantitative proteomics studies on this subject are scarce and have been performed after exposing cells to the factor for long periods of time. In this work we present the largest quantitative proteomics study to date on the short term effects of VEGF on human umbilical vein endothelial cells by 18O/16O labeling. Current statistical models based on normality and variance homogeneity were found unsuitable to describe the null hypothesis in a large scale test experiment performed on these cells, producing false expression changes. A random effects model was developed including four different sources of variance at the spectrum-fitting, scan, peptide, and protein levels. With the new model the number of outliers at scan and peptide levels was negligible in three large scale experiments, and only one false protein expression change was observed in the test experiment among more than 1000 proteins. The new model allowed the detection of significant protein expression changes upon VEGF stimulation for 4 and 8 h. The consistency of the changes observed at 4 h was confirmed by a replica at a smaller scale and further validated by Western blot analysis of some proteins. Most of the observed changes have not been described previously and are consistent with a pattern of protein expression that dynamically changes over time following the evolution of the angiogenic response. With this statistical model the 18O labeling approach emerges as a very promising and robust alternative to perform quantitative proteomics studies at a depth of several thousand proteins.

Proteomic Study of Hepatic Nuclear Extracts in an Adaptive Acetaminophen Tolerance Model

Introduction

Variability in response to acetaminophen (APAP)-induced aseptic inflammation and tolerance to the impending hepatic damage has been described. To understand the mechanism of adaptive tolerance, we investigated the proteomic profiles of crude nuclear lysates in a mouse model. We hypothesized that pretreatment with low doses of APAP prior to a toxic dose results in differential protein expression.

Materials and Methods

Mice (BALB/C) were separated into three groups: the pretreated (PT) group received incremental doses of APAP while the last dose only (LD) and naïve groups were given saline vehicle. A toxic dose of APAP was administered on the seventh day to the PT and LD animals only and all groups were euthanized 3 h postdose. Total protein from crude hepatic nuclear lysates were applied to protein arrays and analyzed by immunoaffinity mass spectrometry.

Results and Discussion

Comparative data analyses of protein peaks revealed a protein that was significantly increased at m/z of 60,030 (p60) in the LD animals vs the other two groups. The closest match for the preliminary identification of the p60 protein based on a Swiss-Prot/TagIdent database search using the approximate isoelectric point and molecular weight information was Ccr4–Not complex subunit-2. This protein is a subunit of a multiprotein complex and serves as a transcriptional suppressor involved in controlling mRNA synthesis and degradation. Preliminary identification was also supported by Western blot analysis using anti-CNOT2 antibody.

Conclusion

Considering the APAP tolerance model, we conclude that toxicogenomic approaches such as nuclear profiling are useful tools in assessing differential expression of transcriptional factors involved in inflammatory response and adaptive tolerance to toxins.

DirecTag: accurate sequence tags from peptide MS/MS through statistical scoring

In shotgun proteomics, tandem mass spectra of peptides are typically identified through database search algorithms such as Sequest. We have developed DirecTag, an open-source algorithm to infer partial sequence tags directly from observed fragment ions. This algorithm is unique in its implementation of three separate scoring systems to evaluate each tag on the basis of peak intensity, m/ z fidelity, and complementarity. In data sets from several types of mass spectrometers, DirecTag reproducibly exceeded the accuracy and speed of InsPecT and GutenTag, two previously published algorithms for this purpose. The source code and binaries for DirecTag are available from http://fenchurch.mc.vanderbilt.edu.