A fast SEQUEST cross correlation algorithm

Unsupervised Reconstruction of Analyte-Specific Mass Spectra Based on Time-Domain Morphology with a Modified Cross-Correlation Approach

Concomitant species that appear at the same or very similar times in a mass-spectral analysis can clutter a spectrum because of the coexistence of many analyte-related ions (e.g., molecular ions, adducts, fragments). One method to extract ions stemming from the same origin is to exploit the chemical information encoded in the time domain, where the individual temporal appearances inside the complex structures of chronograms or chromatograms differ with respect to analytes. By grouping ions with very similar or identical time-domain structures, single-component mass spectra can be reconstructed, which are much easier to interpret and are library-searchable. While many other approaches address similar objectives through the Pearson's correlation coefficient, we explore an alternative method based on a modified cross-correlation algorithm to compute a metric that describes the degree of similarity between features inside any two ion chronograms. Furthermore, an automatic workflow was devised to be capable of categorizing thousands of mass-spectral peaks into different groups within a few seconds. This approach was tested with direct mass-spectrometric analyses as well as with a simple, fast, and poorly resolved LC-MS analysis. Single-component mass spectra were extracted in both cases and were identified based on accurate mass and a mass-spectral library search.

Characterization of SARS-CoV-2 proteins reveals Orf6 pathogenicity, subcellular localization, host interactions and attenuation by Selinexor

BACKGROUND

SARS-CoV-2 causes COVID-19 which has a widely diverse disease profile. The mechanisms underlying its pathogenicity remain unclear. We set out to identify the SARS-CoV-2 pathogenic proteins that through host interactions cause the cellular damages underlying COVID-19 symptomatology.

METHODS

We examined each of the individual SARS-CoV-2 proteins for their cytotoxicity in HEK 293 T cells and their subcellular localization in COS-7 cells. We also used Mass-Spec Affinity purification to identify the host proteins interacting with SARS-CoV-2 Orf6 protein and tested a drug that could inhibit a specific Orf6 and host protein interaction.

RESULTS

We found that Orf6, Nsp6 and Orf7a induced the highest toxicity when over-expressed in human 293 T cells. All three proteins showed membrane localization in COS-7 cells. We focused on Orf6, which was most cytotoxic and localized to the endoplasmic reticulum, autophagosome and lysosomal membranes. Proteomics revealed Orf6 interacts with nucleopore proteins (RAE1, XPO1, RANBP2 and nucleoporins). Treatment with Selinexor, an FDA-approved inhibitor for XPO1, attenuated Orf6-induced cellular toxicity in human 293 T cells.

CONCLUSIONS

Our study revealed Orf6 as a highly pathogenic protein from the SARS-CoV-2 genome, identified its key host interacting proteins, and Selinexor as a drug candidate for directly targeting Orf6 host protein interaction that leads to cytotoxicity.

μ-Crystallin in Mouse Skeletal Muscle Promotes a Shift from Glycolytic toward Oxidative Metabolism

μ-Crystallin, encoded by the CRYM gene, binds the thyroid hormones, T3 and T4. Because T3 and T4 are potent regulators of metabolism and gene expression, and CRYM levels in human skeletal muscle can vary widely, we investigated the effects of overexpression of Crym. We generated transgenic mice, Crym tg, that expressed Crym protein specifically in skeletal muscle at levels 2.6-147.5 fold higher than in controls. Muscular functions, Ca2+ transients, contractile force, fatigue, running on treadmills or wheels, were not significantly altered, although T3 levels in tibialis anterior (TA) muscle were elevated ~190-fold and serum T4 was decreased 1.2-fold. Serum T3 and thyroid stimulating hormone (TSH) levels were unaffected. Crym transgenic mice studied in metabolic chambers showed a significant decrease in the respiratory exchange ratio (RER) corresponding to a 13.7% increase in fat utilization as an energy source compared to controls. Female but not male Crym tg mice gained weight more rapidly than controls when fed high fat or high simple carbohydrate diets. Although labeling for myosin heavy chains showed no fiber type differences in TA or soleus muscles, application of machine learning algorithms revealed small but significant morphological differences between Crym tg and control soleus fibers. RNA-seq and gene ontology enrichment analysis showed a significant shift towards genes associated with slower muscle function and its metabolic correlate, β-oxidation. Protein expression showed a similar shift, though with little overlap. Our study shows that μ-crystallin plays an important role in determining substrate utilization in mammalian muscle and that high levels of μ-crystallin are associated with a shift toward greater fat metabolism.

Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.

Static Growth Promotes PrrF and 2-Alkyl-4(1H)-Quinolone Regulation of Type VI Secretion Protein Expression in Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen that is frequently associated with both acute and chronic infections. P. aeruginosa possesses a complex regulatory network that modulates nutrient acquisition and virulence, but our knowledge of these networks is largely based on studies with shaking cultures, which are not likely representative of conditions during infection. Here, we provide proteomic, metabolic, and genetic evidence that regulation by iron, a critical metallonutrient, is altered in static P. aeruginosa cultures. Specifically, we observed a loss of iron-induced expression of proteins for oxidative phosphorylation, tricarboxylic acid (TCA) cycle metabolism under static conditions. Moreover, we identified type VI secretion as a target of iron regulation in P. aeruginosa cells under static but not shaking conditions, and we present evidence that this regulation occurs via PrrF small regulatory RNA (sRNA)-dependent production of 2-alkyl-4(1H)-quinolone metabolites. These results yield new iron regulation paradigms in an important opportunistic pathogen and highlight the need to redefine iron homeostasis in static microbial communities.IMPORTANCE Host-mediated iron starvation is a broadly conserved signal for microbial pathogens to upregulate expression of virulence traits required for successful infection. Historically, global iron regulatory studies in microorganisms have been conducted in shaking cultures to ensure culture homogeneity, yet these conditions are likely not reflective of growth during infection. Pseudomonas aeruginosa is a well-studied opportunistic pathogen and model organism for iron regulatory studies. Iron homeostasis is maintained through the Fur protein and PrrF small regulatory sRNAs, the functions of which are highly conserved in many other bacterial species. In the current study, we examined how static growth affects the known iron and PrrF regulons of P. aeruginosa, leading to the discovery of novel PrrF-regulated virulence processes. This study demonstrates how the utilization of distinct growth models can enhance our understanding of basic physiological processes that may also affect pathogenesis.

Proteomics of Non-human Primate Plasma after Partial-body Radiation with Minimal Bone Marrow Sparing

High-dose radiation exposure results in organ-specific sequelae that occurs in a time- and dose-dependent manner. The partial body irradiation with minimal bone marrow sparing model was developed to mimic intentional or accidental radiation exposures in humans where bone marrow sparing is likely and permits the concurrent analysis of coincident short- and long-term damage to organ systems. To help inform on the natural history of the radiation-induced injury of the partial body irradiation model, we quantitatively profiled the plasma proteome of non-human primates following 12 Gy partial body irradiation with 2.5% bone marrow sparing with 6 MV LINAC-derived photons at 0.80 Gy min over a time period of 3 wk. The plasma proteome was analyzed by liquid chromatography-tandem mass spectrometry. A number of trends were identified in the proteomic data including pronounced protein changes as well as protein changes that were consistently upregulated or downregulated at all time points and dose levels interrogated. Pathway and gene ontology analysis were performed; bioinformatic analysis revealed significant pathway and biological process perturbations post high-dose irradiation and shed light on underlying mechanisms of radiation damage. Additionally, proteins were identified that had the greatest potential to serve as biomarkers for radiation exposure.

Proteomic Evaluation of the Natural History of the Acute Radiation Syndrome of the Gastrointestinal Tract in a Non-human Primate Model of Partial-body Irradiation with Minimal Bone Marrow Sparing Includes Dysregulation of the Retinoid Pathway

Exposure to ionizing radiation results in injuries of the hematopoietic, gastrointestinal, and respiratory systems, which are the leading causes responsible for morbidity and mortality. Gastrointestinal injury occurs as an acute radiation syndrome. To help inform on the natural history of the radiation-induced injury of the partial body irradiation model, we quantitatively profiled the proteome of jejunum from non-human primates following 12 Gy partial body irradiation with 2.5% bone marrow sparing over a time period of 3 wk. Jejunum was analyzed by liquid chromatography-tandem mass spectrometry, and pathway and gene ontology analysis were performed. A total of 3,245 unique proteins were quantified out of more than 3,700 proteins identified in this study. Also a total of 289 proteins of the quantified proteins showed significant and consistent responses across at least three time points post-irradiation, of which 263 proteins showed strong upregulations while 26 proteins showed downregulations. Bioinformatic analysis suggests significant pathway and upstream regulator perturbations post-high dose irradiation and shed light on underlying mechanisms of radiation damage. Canonical pathways altered by radiation included GP6 signaling pathway, acute phase response signaling, LXR/RXR activation, and intrinsic prothrombin activation pathway. Additionally, we observed dysregulation of proteins of the retinoid pathway and retinoic acid, an active metabolite of vitamin A, as quantified by liquid chromatography-tandem mass spectrometry. Correlation of changes in protein abundance with a well-characterized histological endpoint, corrected crypt number, was used to evaluate biomarker potential. These data further define the natural history of the gastrointestinal acute radiation syndrome in a non-human primate model of partial body irradiation with minimal bone marrow sparing.

Mechanistic Analysis of an Extracellular Signal-Regulated Kinase 2-Interacting Compound that Inhibits Mutant BRAF-Expressing Melanoma Cells by Inducing Oxidative Stress

Constitutively active extracellular signal–regulated kinase (ERK) 1/2 signaling promotes cancer cell proliferation and survival. We previously described a class of compounds containing a 1,1-dioxido-2,5-dihydrothiophen-3-yl 4-benzenesulfonate scaffold that targeted ERK2 substrate docking sites and selectively inhibited ERK1/2-dependent functions, including activator protein-1–mediated transcription and growth of cancer cells containing active ERK1/2 due to mutations in Ras G-proteins or BRAF, Proto-oncogene B-RAF (Rapidly Acclerated Fibrosarcoma) kinase. The current study identified chemical features required for biologic activity and global effects on gene and protein levels in A375 melanoma cells containing mutant BRAF (V600E). Saturation transfer difference-NMR and mass spectrometry analyses revealed interactions between a lead compound (SF-3-030) and ERK2, including the formation of a covalent adduct on cysteine 252 that is located near the docking site for ERK/FXF (DEF) motif for substrate recruitment. Cells treated with SF-3-030 showed rapid changes in immediate early gene levels, including DEF motif–containing ERK1/2 substrates in the Fos family. Analysis of transcriptome and proteome changes showed that the SF-3-030 effects overlapped with ATP-competitive or catalytic site inhibitors of MAPK/ERK Kinase 1/2 (MEK1/2) or ERK1/2. Like other ERK1/2 pathway inhibitors, SF-3-030 induced reactive oxygen species (ROS) and genes associated with oxidative stress, including nuclear factor erythroid 2–related factor 2 (NRF2). Whereas the addition of the ROS inhibitor N-acetyl cysteine reversed SF-3-030–induced ROS and inhibition of A375 cell proliferation, the addition of NRF2 inhibitors has little effect on cell proliferation. These studies provide mechanistic information on a novel chemical scaffold that selectively regulates ERK1/2-targeted transcription factors and inhibits the proliferation of A375 melanoma cells through a ROS-dependent mechanism.

Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era

Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated.

Diabetes Impairs Cellular Cholesterol Efflux From ABCA1 to Small HDL Particles

RATIONALE

HDL (high-density lipoprotein) may be cardioprotective because it accepts cholesterol from macrophages via the cholesterol transport proteins ABCA1 (ATP-binding cassette transporter A1) and ABCG1 (ATP-binding cassette transporter G1). The ABCA1-specific cellular cholesterol efflux capacity (ABCA1 CEC) of HDL strongly and negatively associates with cardiovascular disease risk, but how diabetes mellitus impacts that step is unclear.

OBJECTIVE

To test the hypothesis that HDL's cholesterol efflux capacity is impaired in subjects with type 2 diabetes mellitus.

METHODS AND RESULTS

We performed a case-control study with 19 subjects with type 2 diabetes mellitus and 20 control subjects. Three sizes of HDL particles, small HDL, medium HDL, and large HDL, were isolated by high-resolution size exclusion chromatography from study subjects. Then we assessed the ABCA1 CEC of equimolar concentrations of particles. Small HDL accounted for almost all of ABCA1 CEC activity of HDL. ABCA1 CEC-but not ABCG1 CEC-of small HDL was lower in the subjects with type 2 diabetes mellitus than the control subjects. Isotope dilution tandem mass spectrometry demonstrated that the concentration of SERPINA1 (serpin family A member 1) in small HDL was also lower in subjects with diabetes mellitus. Enriching small HDL with SERPINA1 enhanced ABCA1 CEC. Structural analysis of SERPINA1 identified 3 amphipathic α-helices clustered in the N-terminal domain of the protein; biochemical analyses demonstrated that SERPINA1 binds phospholipid vesicles.

CONCLUSIONS

The ABCA1 CEC of small HDL is selectively impaired in type 2 diabetes mellitus, likely because of lower levels of SERPINA1. SERPINA1 contains a cluster of amphipathic α-helices that enable apolipoproteins to bind phospholipid and promote ABCA1 activity. Thus, impaired ABCA1 activity of small HDL particles deficient in SERPINA1 could increase cardiovascular disease risk in subjects with diabetes mellitus.

VCP Machinery Mediates Autophagic Degradation of Empty Argonaute

The Argonaute subfamily of proteins (AGO) loads microRNAs (miRNAs) to form the effector complex that mediates target gene silencing. Empty AGO, but not miRNA-loaded AGO, is selectively degraded across species. We have reported that the degradation of empty AGO is part of a quality control pathway that eliminates dysfunctional AGO. However, how empty AGO is degraded remains unclear. Here we show that the empty state of Drosophila Ago1 is degraded by autophagy. Comprehensive LC-MS/MS analyses, together with manipulation of the Ago1 ubiquitination level, revealed that VCP, which mediates selective autophagy, recognizes empty Ago1 via the Ufd1-Npl4 heterodimer. Depletion of VCP-Ufd1-Npl4 machinery impairs degradation of empty Ago1 and miRNA-mediated target gene silencing. Our findings reveal a direct link between empty AGO degradation and selective autophagy that ensures efficient miRNA function.

QCMS2 as a new method for providing insight into peptide fragmentation: The influence of the side-chain and inter-side-chain interactions

The identification of peptides and proteins from tandem mass spectra is a difficult task and multiple tools have been developed to aid this identification. We present a new method called quantum chemical mass spectrometry for materials science (QCMS² ), which is based on quantum chemical calculations of bond orders, reaction, and transition-state energies at the DFT/B3LYP/6-311+G* level of theory. The method was used to describe the fragmentation pathways of five X-His-Ser tripeptides with X = Asn, Asp, Glu, Ser, and Trp, thereby focusing on the influence of the side chain and inter-side-chain interactions on the fragmentation. The main features in the mass spectra of the five tripeptides were correctly reproduced, and a number of fragments were assigned to fragmentations involving the side chain and the influence of inter-side-chain interactions. Product ion spectra were recorded to evaluate the capabilities and limitations of QCMS² and a number of conventional tools.

Annotation of tandem mass spectrometry data using stochastic neural networks in shotgun proteomics

MOTIVATION

The discrimination ability of score functions to separate correct from incorrect peptide-spectrum-matches in database-searching-based spectrum identification is hindered by many superfluous peaks belonging to unexpected fragmentation ions or by the lacking peaks of anticipated fragmentation ions.

RESULTS

Here, we present a new method, called BoltzMatch, to learn score functions using a particular stochastic neural networks, called restricted Boltzmann machines, in order to enhance their discrimination ability. BoltzMatch learns chemically explainable patterns among peak pairs in the spectrum data, and it can augment peaks depending on their semantic context or even reconstruct lacking peaks of expected ions during its internal scoring mechanism. As a result, BoltzMatch achieved 50% and 33% more annotations on high- and low-resolution MS2 data than XCorr at a 0.1% false discovery rate in our benchmark; conversely, XCorr yielded the same number of spectrum annotations as BoltzMatch, albeit with 4-6 times more errors. In addition, BoltzMatch alone does yield 14% more annotations than Prosit (which runs with Percolator), and BoltzMatch with Percolator yields 32% more annotations than Prosit at 0.1% FDR level in our benchmark.

AVAILABILITY AND IMPLEMENTATION

BoltzMatch is freely available at: https://github.com/kfattila/BoltzMatch.

CONTACT

akerteszfarkas@hse.ru.

SUPPORTING INFORMATION

Supplementary data are available at Bioinformatics online.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.

Reproductive tract extracellular vesicles are sufficient to transmit intergenerational stress and program neurodevelopment

Extracellular vesicles (EVs) are a unique mode of intercellular communication capable of incredible specificity in transmitting signals involved in cellular function, including germ cell maturation. Spermatogenesis occurs in the testes, behind a protective barrier to ensure safeguarding of germline DNA from environmental insults. Following DNA compaction, further sperm maturation occurs in the epididymis. Here, we report reproductive tract EVs transmit information regarding stress in the paternal environment to sperm, potentially altering fetal development. Using intracytoplasmic sperm injection, we found that sperm incubated with EVs collected from stress-treated epididymal epithelial cells produced offspring with altered neurodevelopment and adult stress reactivity. Proteomic and transcriptomic assessment of these EVs showed dramatic changes in protein and miRNA content long after stress treatment had ended, supporting a lasting programmatic change in response to chronic stress. Thus, EVs as a normal process in sperm maturation, can also perform roles in intergenerational transmission of paternal environmental experience.

Effects of ATP-competitive and function-selective ERK inhibitors on airway smooth muscle cell proliferation

Increased airway smooth muscle (ASM) cell mass and secretory functions are characteristics of airway inflammatory diseases, such as asthma. To date, there are no effective therapies to combat ASM cell proliferation, which contributes to bronchoconstriction and airway obstruction. Growth factors such as platelet-derived growth factor (PDGF) and the activation of the ERK1/2 are major regulators of ASM cell proliferation and airway remodeling in asthma. However, given the ubiquitous expression and multiple functions of ERK1/2, complete inhibition of ERK1/2 using ATP-competitive inhibitors may lead to unwanted off-target effects. Alternatively, we have identified compounds that are designed to target substrate docking sites and act as function-selective inhibitors of ERK1/2 signaling. Here, we show that both function-selective and ATP-competitive ERK1/2 inhibitors are effective at inhibiting PDGF-mediated proliferation, collagen production, and IL-6 secretion in ASM cells. Proteomic analysis revealed that both types of inhibitors had similar effects on reducing proteins related to TGF-β and IL-6 signaling that are relevant to airway remodeling. However, function-selective ERK1/2 inhibitors caused fewer changes in protein expression compared with ATP-competitive inhibitors. These studies provide a molecular basis for the development of function-selective ERK1/2 inhibitors to mitigate airway remodeling in asthma with defined regulation of ERK1/2 signaling.-Defnet, A. E., Huang, W., Polischak, S., Yadav, S. K., Kane, M. A., Shapiro, P., Deshpande, D. A. Effects of ATP-competitive and function-selective ERK inhibitors on airway smooth muscle cell proliferation.

Automatic Analyte-Ion Recognition and Background Removal for Ambient Mass-Spectrometric Data Based on Cross-Correlation

Ambient mass spectrometry is a powerful approach for rapid, high-throughput, and direct sample analysis. Due to the open-air desorption and ionization processes, random fluctuations of ambient conditions can lead to large variances in mass-spectral signals over time. The mass-spectral data also can be further complicated due to multiple analytes present in the sample, background-ion signals stemming from the desorption/ionization source itself, and other laboratory-specific conditions (e.g., ambient laboratory air, nearby hardware). Thus, background removal and analyte-ion recognition can be quite difficult, particularly in non-targeted analyses. Here, we demonstrate the use of a cross-correlation-based approach to exploit chemical information encoded in the time domain to group/categorize mass-spectral peaks from a single analysis dataset. Ions that originate from ambient (or other) background species were readily flagged and removed from spectra; the result was a decrease in mass-spectral complexity by over 70% due to the removal of these background ions. Meanwhile, analyte ions were differentiated and categorized based on their time-domain profiles. With sufficient mass resolving-power and mass-spectral acquisition rate, isolated mass spectra containing ions from the same species in a sample could be extracted, leading to a reduction in mass-spectral complexity by more than 98% in some cases. The cross-correlation approach was tested with different ionization sources as well as reproducible and irreproducible sample introduction. Software built in-house enabled fully automated data processing, which can be performed within a few seconds. Ultimately, this approach provides an additional dimension of analyte separation in ambient mass-spectrometric analyses with information that is already recorded throughout the analysis.

Hydrogen sulfide perturbs mitochondrial bioenergetics and triggers metabolic reprogramming in colon cells

Unlike most other tissues, the colon epithelium is exposed to high levels of H2S derived from gut microbial metabolism. H2S is a signaling molecule that modulates various physiological effects. It is also a respiratory toxin that inhibits complex IV in the electron transfer chain (ETC). Colon epithelial cells are adapted to high environmental H2S exposure as they harbor an efficient mitochondrial H2S oxidation pathway, which is dedicated to its disposal. Herein, we report that the sulfide oxidation pathway enzymes are apically localized in human colonic crypts at the host-microbiome interface, but that the normal apical-to-crypt gradient is lost in colorectal cancer epithelium. We found that sulfide quinone oxidoreductase (SQR), which catalyzes the committing step in the mitochondrial sulfide oxidation pathway and couples to complex III, is a critical respiratory shield against H2S poisoning. H2S at concentrations ≤20 μm stimulated the oxygen consumption rate in colon epithelial cells, but, when SQR expression was ablated, H2S concentrations as low as 5 μm poisoned cells. Mitochondrial H2S oxidation altered cellular bioenergetics, inducing a reductive shift in the NAD+/NADH redox couple. The consequent electron acceptor insufficiency caused uridine and aspartate deficiency and enhanced glutamine-dependent reductive carboxylation. The metabolomic signature of this H2S-induced stress response mapped, in part, to redox-sensitive nodes in central carbon metabolism. Colorectal cancer tissues and cell lines appeared to counter the growth-restricting effects of H2S by overexpressing sulfide oxidation pathway enzymes. Our findings reveal an alternative mechanism for H2S signaling, arising from alterations in mitochondrial bioenergetics that drive metabolic reprogramming.

Heme uptake and utilization by hypervirulent Acinetobacter baumannii LAC-4 is dependent on a canonical heme oxygenase (abHemO)

Acinetobacter baumannii is an opportunistic pathogen that causes serious infections in critically ill and immune compromised patients. The ability to acquire iron from the hosts iron and heme containing proteins is critical to their survival and virulence. The majority of A. baumannii hypervirulent strains encode a heme uptake system that includes a putative heme oxygenase (hemO). Despite reports indicating A. baumannii can grow on heme direct evidence of extracellular heme uptake and metabolism has not been shown. Through isotopic labeling (¹³C-heme) we show the hypervirulent A. baumannii LAC-4 metabolizes heme to biliverdin IXα (BVIXα), whereas ATC 17978 that lacks the hemO gene cluster cannot efficiently utilize heme. Expression and purification of the protein encoded by the A. baumannii LAC-4 hemO gene confirmed catalytic conversion of heme to BVIX. We further show inhibition of abHemO with previously characterized P. aeruginosa HemO inhibitors in a fluorescence based assay that couples HemO catalytic activity to the BVIXα binding phytochrome IFP1.4. Furthermore, the hemO gene cluster encodes genes with homology to heme-dependent extra cytoplasmic function (ECF) σ factor systems. The hemophore-dependent ECF system in Pseudomonas aeruginosa has been shown to play a critical role in heme sensing and virulence within the host. The prevalence of a hemO gene cluster in A. baumannii LAC4 and other hypervirulent strains suggests it is required within the host to adapt and utilize heme and is a major contributor to virulence.

Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate

Systematic assessment of tyrosine kinase-substrate relationships is fundamental to a better understanding of cellular signaling and its profound alterations in human diseases such as cancer. In human cells, such assessments are confounded by complex signaling networks, feedback loops, conditional activity, and intra-kinase redundancy. Here we address this challenge by exploiting the yeast proteome as an in vivo model substrate. We individually expressed 16 human non-receptor tyrosine kinases (NRTKs) in Saccharomyces cerevisiae and identified 3,279 kinase-substrate relationships involving 1,351 yeast phosphotyrosine (pY) sites. Based on the yeast data without prior information, we generated a set of linear kinase motifs and assigned ∼1,300 known human pY sites to specific NRTKs. Furthermore, experimentally defined pY sites for each individual kinase were shown to cluster within the yeast interactome network irrespective of linear motif information. We therefore applied a network inference approach to predict kinase-substrate relationships for more than 3,500 human proteins, providing a resource to advance our understanding of kinase biology.

Proteomic Characterization of Immunoglobulin Content in Dermal Interstitial Fluid

Microneedles have been demonstrated to be a minimally invasive technique for sampling dermal interstitial fluid (ISF). Shotgun quantitative proteomics has already identified hundreds of proteins in ISF and quantitatively compared the proteome to matching serum and plasma. Interstitial fluid was determined to be a viable minimally invasive alternative to blood-derived fluids. In this communication, we re-examined the proteomic data from previous work to determine the diversity of immunoglobulins present compared with serum and plasma. Similar to our previous findings regarding the proteomic content across fluid types, ISF had a similar composition of IgG, IgA, IgD, and IgE antibodies as plasma or serum and lower quantities of IgM, which reflects the relative concentrations of dermal tissue T-cell and B-cell populations, indicating that the Ig's were likely locally derived. This work has significant implications for the utility of measuring Ig's in ISF for the clinical diagnosis of immunological diseases and skin infections. Data are available via ProteomeXchange with identifier PXD012658.

Proteomic Evaluation of the Acute Radiation Syndrome of the Gastrointestinal Tract in a Murine Total-body Irradiation Model

Radiation exposure to the gastrointestinal system contributes to the acute radiation syndrome in a dose- and time-dependent manner. Molecular mechanisms that lead to the gastrointestinal acute radiation syndrome remain incompletely understood. Using a murine model of total-body irradiation, C57BL/6J male mice were irradiated at 8, 10, 12, and 14 Gy and assayed at day 1, 3, and 6 after exposure and compared to nonirradiated (sham) controls. Tryptic digests of gastrointestinal tissues (upper ileum) were analyzed by liquid chromatography-tandem mass spectrometry on a Waters nanoLC coupled to a Thermo Scientific Q Exactive hybrid quadrupole-orbitrap mass spectrometer. Pathway and gene ontology analysis were performed with Qiagen Ingenuity, Panther GO, and DAVID databases. A number of trends were identified in our proteomic data including pronounced protein changes as well as protein changes that were consistently up regulated or down regulated at all time points and dose levels interrogated. Time- and dose-dependent protein changes, canonical pathways affected by irradiation, and changes in proteins that serve as upstream regulators were also identified. Additionally, proteins involved in key processes including inflammation, radiation, and retinoic acid signaling were identified. The proteomic profiling conducted here represents an untargeted systems biology approach to identify acute molecular events that will be useful for a greater understanding of animal models and may be potentially useful toward the development of medical countermeasures and/or biomarkers.

Acute Proteomic Changes in the Lung After WTLI in a Mouse Model: Identification of Potential Initiating Events for Delayed Effects of Acute Radiation Exposure

Radiation-induced lung injury is a delayed effect of acute radiation exposure resulting in pulmonary pneumonitis and fibrosis. Molecular mechanisms that lead to radiation-induced lung injury remain incompletely understood. Using a murine model of whole-thorax lung irradiation, C57BL/6J mice were irradiated at 8, 10, 12, and 14 Gy and assayed at day 1, 3, and 6 postexposure and compared to nonirradiated (sham) controls. Tryptic digests of lung tissues were analyzed by liquid chromatography-tandem mass spectrometry on a Waters nanoLC instrument coupled to a Thermo Scientific Q Exactive hybrid quadrupole-orbitrap mass spectrometer. Pathway and gene ontology analysis were performed with Qiagen Ingenuity, Panther GO, and DAVID databases. A number of trends were identified in the proteomic data, including protein changes greater than 10 fold, protein changes that were consistently up regulated or down regulated at all time points and dose levels interrogated, time and dose dependency of protein changes, canonical pathways affected by irradiation, changes in proteins that serve as upstream regulators, and proteins involved in key processes including inflammation, radiation, and retinoic acid signaling. The proteomic profiling conducted here represents an untargeted systems biology approach to identify acute molecular events that could potentially be initiating events for radiation-induced lung injury.

Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate

Decoy database search with target-decoy competition (TDC) provides an intuitive, easy-to-implement method for estimating the false discovery rate (FDR) associated with spectrum identifications from shotgun proteomics data. However, the procedure can yield different results for a fixed data set analyzed with different decoy databases, and this decoy-induced variability is particularly problematic for smaller FDR thresholds, data sets, or databases. The average TDC (aTDC) protocol combats this problem by exploiting multiple independently shuffled decoy databases to provide an FDR estimate with reduced variability. We provide a tutorial introduction to aTDC, describe an improved variant of the protocol that offers increased statistical power, and discuss how to deploy aTDC in practice using the Crux software toolkit.

Iruka Eliminates Dysfunctional Argonaute by Selective Ubiquitination of Its Empty State

MicroRNAs (miRNAs) are loaded into the Argonaute subfamily of proteins (AGO) to form an effector complex that silences target genes. Empty but not miRNA-loaded AGO is selectively degraded across species. However, the mechanism and biological significance of selective AGO degradation remain unclear. We discovered a RING-type E3 ubiquitin ligase we named Iruka (Iru), which selectively ubiquitinates the empty form of Drosophila Ago1 to trigger its degradation. Iru preferentially binds empty Ago1 and ubiquitinates Lys514 in the L2 linker, which is predicted to be inaccessible in the miRNA-loaded state. Depletion of Iru results in global impairment of miRNA-mediated silencing of target genes and in the accumulation of aberrant Ago1 that is dysfunctional for canonical protein-protein interactions and miRNA loading. Our findings reveal a sophisticated mechanism for the selective degradation of empty AGO that underlies a quality control process to ensure AGO function.

Alterations in retinoic acid signaling affect the development of the mouse coronary vasculature

BACKGROUND

During the final stages of heart development the myocardium grows and becomes vascularized by means of paracrine factors and cell progenitors derived from the epicardium. There is evidence to suggest that retinoic acid (RA), a metabolite of vitamin A, plays an important role in epicardial-based developmental programming. However, the consequences of altered RA-signaling in coronary development have not been systematically investigated.

RESULTS

We explored the developmental consequences of altered RA-signaling in late cardiogenic events that involve the epicardium. For this, we used a model of embryonic RA excess based on mouse embryos deficient in the retinaldehyde reductase DHRS3, and a complementary model of embryonic RA deficiency based on pharmacological inhibition of RA synthesis. We found that alterations in embryonic RA signaling led to a thin myocardium and aberrant coronary vessel formation and remodeling. Both excess, and deficient RA-signaling are associated with reductions in ventricular coverage and density of coronary vessels, altered vessel morphology, and impaired recruitment of epicardial-derived mural cells. Using a combined transcriptome and proteome profiling approach, we found that RA treatment of epicardial cells influenced key signaling pathways relevant for cardiac development.

CONCLUSIONS

Epicardial RA-signaling plays critical roles in the development of the coronary vasculature needed to support myocardial growth. Developmental Dynamics 247:976-991, 2018. © 2018 Wiley Periodicals, Inc.

Membrane proteomics of cervical cancer cell lines reveal insights on the process of cervical carcinogenesis

The available therapeutic approaches for cervical cancer can seriously affect the fertility potential of patient; thus, there is a pressing requirement for less toxic and targeted therapies. The membrane proteome is a potential source of therapeutic targets; however, despite the significance of membrane proteins in cancer, proteomic analysis has been a challenging task due to their unique biochemical properties. The aim of the present study was to develop an efficient membrane protein enrichment protocol, and to the best of our knowledge, to compare for the first time the expression pattern of membrane proteins of one normal cell line, HCK1T, and three cervical cancer cell lines, C33A, a human papilloma virus (HPV)-negative cell line, and two HPV-positive cell lines, SiHa (HPV16+) and HeLa (HPV18+). The study aimed to identify the proteins that are involved in cervical carcinogenesis and may constitute novel drug targets. Membrane protein isolation, liquid chromatography coupled with tandem mass spectrometry proteomics and bioinformatics analysis were performed in the membrane fraction of the informative cervical cell lines following a novel enrichment protocol. The percentages of membrane and transmembrane proteins in the enrichment protocol were higher compared with those of the corresponding data derived from total cell extract analysis. Differentially expressed proteins were detected by the comparison of the cervical cancer cell lines with the normal cell line. These proteins constitute molecular features of cancer pathology and participate in biological pathways relevant to malignancy, including 'HIPPO signaling', 'PI3K/Akt signaling', 'cell cycle: G2/M DNA damage checkpoint regulation' and 'EIF2 signaling'. These unique membrane protein identifications offer insights on a previously inaccessible region of the cervical cancer proteome, and may represent putative diagnostic and prognostic markers, and eventually therapeutic targets.

Computational Convolution of SELDI Data for the Diagnosis of Alzheimer's Disease

Alzheimer’s disease is rapidly becoming an endemic for people over the age of 65. A vital path towards reversing this ominous trend is the building of reliable diagnostic devices for definite and early diagnoses in lieu of the longitudinal, usually inconclusive and non-generalize-able methods currently in use. In this article, we present a survey of methods for mining pools of mass spectrometer saliva data in relation to diagnosing Alzheimer’s disease. The computational methods provides new approaches for appropriately gleaning latent information from mass spectra data. They improve traditional machine learning algorithms and are most fit for handling matrix data points including solving problems beyond protein identifications and biomarker discovery.

Chemical Crosslinking Mass Spectrometry Analysis of Protein Conformations and Supercomplexes in Heart Tissue

While modern structural biology technologies have greatly expanded the size and type of protein complexes that can now be studied, the ability to derive large-scale structural information on proteins and complexes as they exist within tissues is practically nonexistent. Here, we demonstrate the application of crosslinking mass spectrometry to identify protein structural features and interactions in tissue samples, providing systems structural biology insight into protein complexes as they exist in the mouse heart. This includes insights into multiple conformational states of sarcomere proteins, as well as interactions among OXPHOS complexes indicative of supercomplex assembly. The extension of crosslinking mass spectrometry analysis into the realm of tissues opens the door to increasing our understanding of protein structures and interactions within the context of the greater biological system.

Proteomics show antigen presentation processes in human immune cells after AS03-H5N1 vaccination

Adjuvants enhance immunity elicited by vaccines through mechanisms that are poorly understood. Using a systems biology approach, we investigated temporal protein expression changes in five primary human immune cell populations: neutrophils, monocytes, natural killer cells, T cells, and B cells after administration of either an Adjuvant System 03 adjuvanted or unadjuvanted split-virus H5N1 influenza vaccine. Monocytes demonstrated the strongest differential signal between vaccine groups. On day 3 post-vaccination, several antigen presentation-related pathways, including MHC class I-mediated antigen processing and presentation, were enriched in monocytes and neutrophils and expression of HLA class I proteins was increased in the Adjuvant System 03 group. We identified several protein families whose proteomic responses predicted seroprotective antibody responses (>1:40 hemagglutination inhibition titer), including inflammation and oxidative stress proteins at day 1 as well as immunoproteasome subunit (PSME1 and PSME2) and HLA class I proteins at day 3 in monocytes. While comparison between temporal proteomic and transcriptomic results showed little overlap overall, enrichment of the MHC class I antigen processing and presentation pathway in monocytes and neutrophils was confirmed by both approaches.

Multi-omics Comparative Analysis Reveals Multiple Layers of Host Signaling Pathway Regulation by the Gut Microbiota

Multiple host pathways were affected by its adaptation to the microbiota. We have found significant transcriptome-proteome discordance caused by the microbiota. This discovery leads to the definite conclusion that transcript-level analysis is not sufficient to predict protein levels and their influence on the function of many specific cellular pathways, so only analysis of combinations of the quantitative data determined at different levels will lead to a complete understanding of the complex relationships between the host and the microbiota. Therefore, our results demonstrate the importance of using an integrative approach to study host-microbiota interaction at the molecular level.

The bodies of mammals are hosts to vast microbial communities composed of trillions of bacteria from thousands of species, whose effects on health and development have begun to be appreciated only recently. In this investigation, an integrated analysis combining proteomics and transcriptomics was used to quantitatively compare the terminal ilia from conventional and germfree mice. Female and male mice responded similarly to the microbiota, but C57BL/10A mice responded more strongly than BALB/c mice at both the transcriptome and proteome levels. The microbiota primarily caused upregulation of immunological pathways and downregulation of metabolic pathways in the conventional mice. Many of the affected pathways were altered only at either the transcriptome or proteome level. Of the pathways that were affected at both levels, most were affected concordantly. The discordant pathways were not principally involved in the immune system but instead were related to metabolism, oxidative phosphorylation, protein translation, transport, and turnover. To broaden the discovery of affected host pathways, a meta-analysis was performed using intestinal transcriptomics data from previously published studies of germfree versus conventional mice with diverse microbiota populations. Similar transcript-level responses to the microbiota were found, and many additional affected host pathways were discovered.

IMPORTANCE Multiple host pathways were affected by its adaptation to the microbiota. We have found significant transcriptome-proteome discordance caused by the microbiota. This discovery leads to the definite conclusion that transcript-level analysis is not sufficient to predict protein levels and their influence on the function of many specific cellular pathways, so only analysis of combinations of the quantitative data determined at different levels will lead to a complete understanding of the complex relationships between the host and the microbiota. Therefore, our results demonstrate the importance of using an integrative approach to study host-microbiota interaction at the molecular level.

Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach

The Human Proteome Project (HPP) aims deciphering the complete map of the human proteome. In the past few years, significant efforts of the HPP teams have been dedicated to the experimental detection of the missing proteins, which lack reliable mass spectrometry evidence of their existence. In this endeavor, an in depth analysis of shotgun experiments might represent a valuable resource to select a biological matrix in design validation experiments. In this work, we used all the proteomic experiments from the NCI60 cell lines and applied an integrative approach based on the results obtained from Comet, Mascot, OMSSA, and X!Tandem. This workflow benefits from the complementarity of these search engines to increase the proteome coverage. Five missing proteins C-HPP guidelines compliant were identified, although further validation is needed. Moreover, 165 missing proteins were detected with only one unique peptide, and their functional analysis supported their participation in cellular pathways as was also proposed in other studies. Finally, we performed a combined analysis of the gene expression levels and the proteomic identifications from the common cell lines between the NCI60 and the CCLE project to suggest alternatives for further validation of missing protein observations.

Neutrophil microparticle production and inflammasome activation by hyperglycemia due to cytoskeletal instability

Microparticles are lipid bilayer-enclosed vesicles produced by cells under oxidative stress. MP production is elevated in patients with diabetes, but the underlying cellular mechanisms are poorly understood. We hypothesized that raising glucose above the physiological level of 5.5 mm would stimulate leukocytes to produce MPs and activate the nucleotide-binding domain, leucine-rich repeat pyrin domain-containing 3 (NLRP3) inflammasome. We found that when incubated in buffer with up to 20 mm glucose, human and murine neutrophils, but not monocytes, generate progressively more MPs with high interleukin (IL)-1β content. Enhanced MP production required generation of reactive chemical species by mitochondria, NADPH oxidase, and type 2 nitric-oxide synthase (NOS-2) and resulted in S-nitrosylation of actin. Depleting cells of capon (C-terminal PDZ ligand of neuronal nitric-oxide synthase protein), apoptosis-associated speck-like protein containing C-terminal caspase recruitment domain (ASC), or pro-IL-1β prevented the hyperglycemia-induced enhancement of reactive species production, MP generation, and IL-1β synthesis. Additional components required for these responses included inositol 1,3,5-triphosphate receptors, PKC, and enhancement of filamentous-actin turnover. Numerous proteins become localized to short filamentous actin in response to S-nitrosylation, including vasodilator-stimulated phosphoprotein, focal adhesion kinase, the membrane phospholipid translocation enzymes flippase and floppase, capon, NLRP3, and ASC. We conclude that an interdependent oxidative stress response to hyperglycemia perturbs neutrophil cytoskeletal stability leading to MP production and IL-1β synthesis.

Exhaustively Identifying Cross-Linked Peptides with a Linear Computational Complexity

Chemical cross-linking coupled to mass spectrometry is a powerful tool to study protein-protein interactions and protein conformations. Two linked peptides are ionized and fragmented to produce a tandem mass spectrum. In such an experiment, a tandem mass spectrum contains ions from two peptides. The peptide identification problem becomes a peptide-peptide pair identification problem. Currently, most tools do not search all possible pairs due to the quadratic time complexity. Consequently, missed findings are unavoidable. In our previous work, we developed a tool named ECL to search all pairs of peptides exhaustively. Unfortunately, it is very slow due to the quadratic computational complexity, especially when the database is large. Furthermore, ECL uses a score function without statistical calibration, while researchers1-3 have proposed that it is inappropriate to directly compare uncalibrated scores because different spectra have different random score distributions. Here we propose an advanced version of ECL, named ECL2. It achieves a linear time and space complexity by taking advantage of the additive property of a score function. It can search a data set containing tens of thousands of spectra against a database containing thousands of proteins in a few hours. Comparison with other five state-of-the-art tools shows that ECL2 is much faster than pLink, StavroX, ProteinProspector, and ECL. Kojak is the only one that is faster than ECL2, but Kojak does not exhaustively search all possible peptide pairs. The comparison shows that ECL2 has the highest sensitivity among the state-of-the-art tools. The experiment using a large-scale in vivo cross-linking data set demonstrates that ECL2 is the only tool that can find the peptide-spectrum matches (PSMs) passing the false discovery rate/q-value threshold. The result illustrates that the exhaustive search and a well-calibrated score function are useful to find PSMs from a huge search space.

Methods to Calculate Spectrum Similarity

Scoring functions that assess spectrum similarity play a crucial role in many computational mass spectrometry algorithms. These functions are used to compare an experimentally acquired fragmentation (MS/MS) spectrum against two different types of target MS/MS spectra: either against a theoretical MS/MS spectrum derived from a peptide from a sequence database, or against another, previously acquired MS/MS spectrum. The former is typically encountered in database searching, while the latter is used in spectrum clustering and spectral library searching. The comparison between acquired versus theoretical MS/MS spectra is most commonly performed using cross-correlations or probability derived scoring functions, while the comparison of two acquired MS/MS spectra typically makes use of a normalized dot product, especially in spectrum library search algorithms. In addition to these scoring functions, Pearson's or Spearman's correlation coefficients, mean squared error, or median absolute deviation scores can also be used for the same purpose. Here, we describe and evaluate these scoring functions with regards to their ability to assess spectrum similarity for theoretical versus acquired, and acquired versus acquired spectra.

Unassigned MS/MS Spectra: Who Am I?

Recent advances in high resolution tandem mass spectrometry (MS) has resulted in the accumulation of high quality data. Paralleled with these advances in instrumentation, bioinformatics software have been developed to analyze such quality datasets. In spite of these advances, data analysis in mass spectrometry still remains critical for protein identification. In addition, the complexity of the generated MS/MS spectra, unpredictable nature of peptide fragmentation, sequence annotation errors, and posttranslational modifications has impeded the protein identification process. In a typical MS data analysis, about 60 % of the MS/MS spectra remains unassigned. While some of these could attribute to the low quality of the MS/MS spectra, a proportion can be classified as high quality. Further analysis may reveal how much of the unassigned MS spectra attribute to search space, sequence annotation errors, mutations, and/or posttranslational modifications. In this chapter, the tools used to identify proteins and ways to assign unassigned tandem MS spectra are discussed.

Rapid lipid a structure determination via surface acoustic wave nebulization and hierarchical tandem mass spectrometry algorithm

RATIONALE

Surface acoustic wave nebulization (SAWN) is an easy to use sample transfer method for rapid mass spectrometric analysis. A new standing wave (SW) SAWN chip, with higher ionization efficiency than our previously reported design, is used for rapid analysis of lipids.

METHODS

The crude, yet fast, Caroff protocol was used for lipid A extraction from Francisella novicida. SW-SAWN with a Waters Synapt G2S quadrupole time-of-flight (QTOF) mass spectrometer was used to generate lipid A ions. Quadrupole collision-induced dissociation (Q-CID) of lipid A at varying CID energies was used to approximate the ion trap MSⁿ data required for our hierarchical tandem mass spectrometry (HiTMS) algorithm. Structural hypotheses can be obtained directly from the HiTMS algorithm to identify species-specific lipid A molecules.

RESULTS

SW-SAWN successfully generated ions from lipid A extracted from Francisella novicida using the faster Caroff method. In addition, varying collision energies were used to generate tandem mass spectra similar to MS³ and MS⁴ spectra from an ion trap. The Q-CID spectra are compatible with our HiTMS algorithm and offer an improvement over lipid A tandem mass spectra acquired in an ion trap.

CONCLUSIONS

Combining SW-SAWN and Q-CID enabled more structural assignments than previously reported in half the time. The ease of generating spectra by SAWN tandem MS in combination with HiTMS interpretation offers high-throughput lipid A structural analysis and thereby rapid detection of pathogens based on lipid fingerprinting. Copyright © 2016 John Wiley & Sons, Ltd.

Tempest: Accelerated MS/MS Database Search Software for Heterogeneous Computing Platforms

MS/MS database search algorithms derive a set of candidate peptide sequences from in silico digest of a protein sequence database, and compute theoretical fragmentation patterns to match these candidates against observed MS/MS spectra. The original Tempest publication described these operations mapped to a CPU-GPU model, in which the CPU (central processing unit) generates peptide candidates that are asynchronously sent to a discrete GPU (graphics processing unit) to be scored against experimental spectra in parallel. The current version of Tempest expands this model, incorporating OpenCL to offer seamless parallelization across multicore CPUs, GPUs, integrated graphics chips, and general-purpose coprocessors. Three protocols describe how to configure and run a Tempest search, including discussion of how to leverage Tempest's unique feature set to produce optimal results. © 2016 by John Wiley & Sons, Inc.

Integrated Proteomic Pipeline Using Multiple Search Engines for a Proteogenomic Study with a Controlled Protein False Discovery Rate

In the Chromosome-Centric Human Proteome Project (C-HPP), false-positive identification by peptide spectrum matches (PSMs) after database searches is a major issue for proteogenomic studies using liquid-chromatography and mass-spectrometry-based large proteomic profiling. Here we developed a simple strategy for protein identification, with a controlled false discovery rate (FDR) at the protein level, using an integrated proteomic pipeline (IPP) that consists of four engrailed steps as follows. First, using three different search engines, SEQUEST, MASCOT, and MS-GF+, individual proteomic searches were performed against the neXtProt database. Second, the search results from the PSMs were combined using statistical evaluation tools including DTASelect and Percolator. Third, the peptide search scores were converted into E-scores normalized using an in-house program. Last, ProteinInferencer was used to filter the proteins containing two or more peptides with a controlled FDR of 1.0% at the protein level. Finally, we compared the performance of the IPP to a conventional proteomic pipeline (CPP) for protein identification using a controlled FDR of <1% at the protein level. Using the IPP, a total of 5756 proteins (vs 4453 using the CPP) including 477 alternative splicing variants (vs 182 using the CPP) were identified from human hippocampal tissue. In addition, a total of 10 missing proteins (vs 7 using the CPP) were identified with two or more unique peptides, and their tryptic peptides were validated using MS/MS spectral pattern from a repository database or their corresponding synthetic peptides. This study shows that the IPP effectively improved the identification of proteins, including alternative splicing variants and missing proteins, in human hippocampal tissues for the C-HPP. All RAW files used in this study were deposited in ProteomeXchange (PXD000395).

Extracellular histones identified in crocodile blood inhibit in-vitro HIV-1 infection

OBJECTIVE

It has been reported that crocodile blood contains potent antibacterial and antiviral properties. However, its effects on HIV-1 infection remain unknown.

DESIGN

We obtained blood from saltwater crocodiles to examine whether serum or plasma could inhibit HIV-1 infection. We purified plasma fractions then used liquid chromatography-mass spectrometry to identify the inhibitory protein factor(s). We then analyzed the ability of recombinant proteins to recapitulate HIV-1 inhibition and determine their mechanism of action.

METHODS

Crocodylus porosus plasma was tested for inhibition of Jurkat T-cell HIV-1 infection. Inhibitor(s) were purified by reverse-phase chromatography then identified by protein liquid chromatography-mass spectrometry. Anti-HIV-1 activity of purified plasma or recombinant proteins were measured by p24 enzyme-linked immunosorbent assay and luciferase readouts, and mechanism of action was determined by measuring HIV-1 RNA, cDNA and transcription (using 1G5 cells).

RESULTS

Crocodile plasma contains potent inhibitors of HIV-1IIIB infection, which were identified as histones. Recombinant human histones H1 and H2A significantly reduced HIV-1JR-FL infection (IC50 of 0.79 and 0.45 μmol/l, respectively), whereas H4 enhanced JR-FL luciferase activity. The inhibitory effects of crocodile plasma, recombinant H1 or recombinant H2A on HIV-1 infection were during or post-viral transcription.

CONCLUSION

Circulating histones in crocodile blood, possibly released by neutrophil extracellular traps, are significant inhibitors of HIV-1 infection in-vitro. Extracellular recombinant histones have different effects on HIV-1 transcription and protein expression and are downregulated in HIV-1 patients. Circulating histones may be a novel resistance factor during HIV-1 infection, and peptide versions should be explored as future HIV-1 therapeutics that modulate viral transcription.

Confidence assignment for mass spectrometry based peptide identifications via the extreme value distribution

MOTIVATION

There is a growing trend for biomedical researchers to extract evidence and draw conclusions from mass spectrometry based proteomics experiments, the cornerstone of which is peptide identification. Inaccurate assignments of peptide identification confidence thus may have far-reaching and adverse consequences. Although some peptide identification methods report accurate statistics, they have been limited to certain types of scoring function. The extreme value statistics based method, while more general in the scoring functions it allows, demands accurate parameter estimates and requires, at least in its original design, excessive computational resources. Improving the parameter estimate accuracy and reducing the computational cost for this method has two advantages: it provides another feasible route to accurate significance assessment, and it could provide reliable statistics for scoring functions yet to be developed.

RESULTS

We have formulated and implemented an efficient algorithm for calculating the extreme value statistics for peptide identification applicable to various scoring functions, bypassing the need for searching large random databases.

AVAILABILITY AND IMPLEMENTATION

The source code, implemented in C ++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit

CONTACT

yyu@ncbi.nlm.nih.gov

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Data Independent Acquisition analysis in ProHits 4.0

Affinity purification coupled with mass spectrometry (AP-MS) is a powerful technique for the identification and quantification of physical interactions. AP-MS requires careful experimental design, appropriate control selection and quantitative workflows to successfully identify bona fide interactors amongst a large background of contaminants. We previously introduced ProHits, a Laboratory Information Management System for interaction proteomics, which tracks all samples in a mass spectrometry facility, initiates database searches and provides visualization tools for spectral counting-based AP-MS approaches. More recently, we implemented Significance Analysis of INTeractome (SAINT) within ProHits to provide scoring of interactions based on spectral counts. Here, we provide an update to ProHits to support Data Independent Acquisition (DIA) with identification software (DIA-Umpire and MSPLIT-DIA), quantification tools (through DIA-Umpire, or externally via targeted extraction), and assessment of quantitative enrichment (through mapDIA) and scoring of interactions (through SAINT-intensity). With additional improvements, notably support of the iProphet pipeline, facilitated deposition into ProteomeXchange repositories and enhanced export and viewing functions, ProHits 4.0 offers a comprehensive suite of tools to facilitate affinity proteomics studies.

SIGNIFICANCE

It remains challenging to score, annotate and analyze proteomics data in a transparent manner. ProHits was previously introduced as a LIMS to enable storing, tracking and analysis of standard AP-MS data. In this revised version, we expand ProHits to include integration with a number of identification and quantification tools based on Data-Independent Acquisition (DIA). ProHits 4.0 also facilitates data deposition into public repositories, and the transfer of data to new visualization tools.

Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology

Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection-hallmarks of host-pathogen interactions-were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies.

IMPORTANCE

The exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy.

MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics

Shotgun proteomics experiments generate large amounts of fragment spectra as primary data, normally with high redundancy between and within experiments. Here, we have devised a clustering technique to identify fragment spectra stemming from the same species of peptide. This is a powerful alternative method to traditional search engines for analyzing spectra, specifically useful for larger scale mass spectrometry studies. As an aid in this process, we propose a distance calculation relying on the rarity of experimental fragment peaks, following the intuition that peaks shared by only a few spectra offer more evidence than peaks shared by a large number of spectra. We used this distance calculation and a complete-linkage scheme to cluster data from a recent large-scale mass spectrometry-based study. The clusterings produced by our method have up to 40% more identified peptides for their consensus spectra compared to those produced by the previous state-of-the-art method. We see that our method would advance the construction of spectral libraries as well as serve as a tool for mining large sets of fragment spectra. The source code and Ubuntu binary packages are available at https://github.com/statisticalbiotechnology/maracluster (under an Apache 2.0 license).

HiXCorr: a portable high-speed XCorr engine for high-resolution tandem mass spectrometry

Peptide identification is an important problem in proteomics. One of the most popular scoring schemes for peptide identification is XCorr (cross-correlation). Since calculating XCorr is computationally intensive, a lot of efforts have been made to develop fast XCorr engines. However, the existing XCorr engines are not suitable for high-resolution MS/MS spectrometry because they are either slow or require a specific type of CPU. We present a portable high-speed XCorr engine for high-resolution tandem mass spectrometry by developing a novel algorithm for calculating XCorr. The algorithm enables XCorr calculation 1.25-49 times faster than previous algorithms for 0.01 Da fragment tolerance. Furthermore, our engine is easily portable to any machine with different types of CPU because it is developed in C language. Hence, our XCorr engine will expedite peptide identification by high-resolution tandem mass spectrometry.

AVAILABILITY AND IMPLEMENTATION

Available at http://isa.hanyang.ac.kr/HiXCorr/HiXCorr.html.