A fast SEQUEST cross correlation algorithm

A deeper look into Comet--implementation and features

The Comet database search software was initially released as an open source project in late 2012. Prior to that, Comet existed as the University of Washington's academic version of the SEQUEST database search tool. Despite its availability and widespread use over the years, some details about its implementation have not been previously disseminated or are not well understood. We address a few of these details in depth and highlight new features available in the latest release. Comet is freely available for download at http://comet-ms.sourceforge.net or it can be accessed as a component of a number of larger software projects into which it has been incorporated. Graphical Abstract ᅟ.

Environmental Interactions and Epistasis Are Revealed in the Proteomic Responses to Complex Stimuli

Ultimately, the genotype of a cell and its interaction with the environment determine the cell’s biochemical state. While the cell’s response to a single stimulus has been studied extensively, a conceptual framework to model the effect of multiple environmental stimuli applied concurrently is not as well developed. In this study, we developed the concepts of environmental interactions and epistasis to explain the responses of the S. cerevisiae proteome to simultaneous environmental stimuli. We hypothesize that, as an abstraction, environmental stimuli can be treated as analogous to genetic elements. This would allow modeling of the effects of multiple stimuli using the concepts and tools developed for studying gene interactions. Mirroring gene interactions, our results show that environmental interactions play a critical role in determining the state of the proteome. We show that individual and complex environmental stimuli behave similarly to genetic elements in regulating the cellular responses to stimuli, including the phenomena of dominance and suppression. Interestingly, we observed that the effect of a stimulus on a protein is dominant over other stimuli if the response to the stimulus involves the protein. Using publicly available transcriptomic data, we find that environmental interactions and epistasis regulate transcriptomic responses as well.

Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data

In mass spectrometry-based bottom-up proteomics, data-independent acquisition is an emerging technique because of its comprehensive and unbiased sampling of precursor ions. However, current data-independent acquisition methods use wide precursor isolation windows, resulting in cofragmentation and complex mixture spectra. Thus, conventional database searching tools that identify peptides by interpreting individual tandem MS spectra are inherently limited in analyzing data-independent acquisition data. Here we discuss an alternative approach, peptide-centric analysis, which tests directly for the presence and absence of query peptides. We discuss how peptide-centric analysis resolves some limitations of traditional spectrum-centric analysis, and we outline the unique characteristics of peptide-centric analysis in general.

ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity

ProLuCID, a new algorithm for peptide identification using tandem mass spectrometry and protein sequence databases has been developed. This algorithm uses a three tier scoring scheme. First, a binomial probability is used as a preliminary scoring scheme to select candidate peptides. The binomial probability scores generated by ProLuCID minimize molecular weight bias and are independent of database size. A modified cross-correlation score is calculated for each candidate peptide identified by the binomial probability. This cross-correlation scoring function models the isotopic distributions of fragment ions of candidate peptides which ultimately results in higher sensitivity and specificity than that obtained with the SEQUEST XCorr. Finally, ProLuCID uses the distribution of XCorr values for all of the selected candidate peptides to compute a Z score for the peptide hit with the highest XCorr. The ProLuCID Z score combines the discriminative power of XCorr and DeltaCN, the standard parameters for assessing the quality of the peptide identification using SEQUEST, and displays significant improvement in specificity over ProLuCID XCorr alone. ProLuCID is also able to take advantage of high resolution MS/MS spectra leading to further improvements in specificity when compared to low resolution tandem MS data. A comparison of filtered data searched with SEQUEST and ProLuCID using the same false discovery rate as estimated by a target-decoy database strategy, shows that ProLuCID was able to identify as many as 25% more proteins than SEQUEST. ProLuCID is implemented in Java and can be easily installed on a single computer or a computer cluster. This article is part of a Special Issue entitled: Computational Proteomics.

Improving protein identification from tandem mass spectrometry data by one-step methods and integrating data from other platforms

MOTIVATION

Many approaches have been proposed for the protein identification problem based on tandem mass spectrometry (MS/MS) data. In these experiments, proteins are digested into peptides and the resulting peptide mixture is subjected to mass spectrometry. Some interesting putative peptide features (peaks) are selected from the mass spectra. Following that, the precursor ions undergo fragmentation and are analyzed by MS/MS. The process of identification of peptides from the mass spectra and the constituent proteins in the sample is called protein identification from MS/MS data. There are many two-step protein identification procedures, reviewed in the literature, which first attempt to identify the peptides in a separate process and then use these results to infer the proteins. However, in recent years, there have been attempts to provide a one-step solution to protein identification, which simultaneously identifies the proteins and the peptides in the sample.

RESULTS

In this review, we briefly introduce the most popular two-step protein identification procedure, PeptideProphet coupled with ProteinProphet. Following that, we describe the difficulties with two-step procedures and review some recently introduced one-step protein/peptide identification procedures that do not suffer from these issues. The focus of this review is on one-step procedures that are based on statistical likelihood-based models, but some discussion of other one-step procedures is also included. We report comparative performances of one-step and two-step methods, which support the overall superiorities of one-step procedures. We also cover some recent efforts to improve protein identification by incorporating other molecular data along with MS/MS data.

Kojak: efficient analysis of chemically cross-linked protein complexes

Protein chemical cross-linking and mass spectrometry enable the analysis of protein-protein interactions and protein topologies; however, complicated cross-linked peptide spectra require specialized algorithms to identify interacting sites. The Kojak cross-linking software application is a new, efficient approach to identify cross-linked peptides, enabling large-scale analysis of protein-protein interactions by chemical cross-linking techniques. The algorithm integrates spectral processing and scoring schemes adopted from traditional database search algorithms and can identify cross-linked peptides using many different chemical cross-linkers with or without heavy isotope labels. Kojak was used to analyze both novel and existing data sets and was compared to existing cross-linking algorithms. The algorithm provided increased cross-link identifications over existing algorithms and, equally importantly, the results in a fraction of computational time. The Kojak algorithm is open-source, cross-platform, and freely available. This software provides both existing and new cross-linking researchers alike an effective way to derive additional cross-link identifications from new or existing data sets. For new users, it provides a simple analytical resource resulting in more cross-link identifications than other methods.

A cell-based systems biology assessment of human blood to monitor immune responses after influenza vaccination

Systems biology is an approach to comprehensively study complex interactions within a biological system. Most published systems vaccinology studies have utilized whole blood or peripheral blood mononuclear cells (PBMC) to monitor the immune response after vaccination. Because human blood is comprised of multiple hematopoietic cell types, the potential for masking responses of under-represented cell populations is increased when analyzing whole blood or PBMC. To investigate the contribution of individual cell types to the immune response after vaccination, we established a rapid and efficient method to purify human T and B cells, natural killer (NK) cells, myeloid dendritic cells (mDC), monocytes, and neutrophils from fresh venous blood. Purified cells were fractionated and processed in a single day. RNA-Seq and quantitative shotgun proteomics were performed to determine expression profiles for each cell type prior to and after inactivated seasonal influenza vaccination. Our results show that transcriptomic and proteomic profiles generated from purified immune cells differ significantly from PBMC. Differential expression analysis for each immune cell type also shows unique transcriptomic and proteomic expression profiles as well as changing biological networks at early time points after vaccination. This cell type-specific information provides a more comprehensive approach to monitor vaccine responses.

3DSpectra: A 3-dimensional quantification algorithm for LC-MS labeled profile data

Mass spectrometry-based proteomics can generate highly informative datasets, as profile three-dimensional (3D) LC-MS data: LC-MS separates peptides in two dimensions (time, m/z) minimizing their overlap, and profile acquisition enhances quantification. To exploit both data features, we developed 3DSpectra, a 3D approach embedding a statistical method for peptide border recognition. 3DSpectra efficiently accesses profile data by means of mzRTree, and makes use of a priori metadata, provided by search engines, to quantify the identified peptides. An isotopic distribution model, shaped by a bivariate Gaussian Mixture Model (GMM), which includes a noise component, is fitted to the peptide peaks using the expectation-maximization (EM) approach. The EM starting parameters, i.e., the centers and shapes of the Gaussians, are retrieved from the metadata. The borders of the peaks are delimited by the GMM iso-density curves, and noisy or outlying data are discarded from subsequent analysis. The 3DSpectra program was compared to ASAPRatio for a controlled mixture of Isotope-Coded Protein Labels (ICPL) labeled proteins, which were mixed at predefined ratios and acquired in enhanced profile mode, in triplicate. The 3DSpectra software showed significantly higher linearity, quantification accuracy, and precision than did ASAPRatio in this real use case simulation where the true ratios are known, and it also achieved wider peptide coverage and dynamic range.

BIOLOGICAL SIGNIFICANCE

Quantitative proteomics is pivotal for many systems biology related fields, such as biomarker discovery. The quantification quality provided by the adopted software is crucial for the success of protein differential expression studies. To determine the reliability of a quantitative computational method, we suggest evaluating performance parameters like accuracy and precision of the quantifications, robustness to outliers and proteome coverage. A quantitative comparison of these parameters is highly desirable since it enables to benchmark software performance. We applied this strategy to 3DSpectra, a 3-dimensional approach to spectra analysis for MS1 peptide quantification. It distinguishes peptide peaks from spurious peaks interfering in the survey scan. 3DSpectra was compared to ASAPRatio in terms of quantification quality performance parameters and showed an overall improvement.

On the importance of well-calibrated scores for identifying shotgun proteomics spectra

Identifying the peptide responsible for generating an observed fragmentation spectrum requires scoring a collection of candidate peptides and then identifying the peptide that achieves the highest score. However, analysis of a large collection of such spectra requires that the score assigned to one spectrum be well-calibrated with respect to the scores assigned to other spectra. In this work, we define the notion of calibration in the context of shotgun proteomics spectrum identification, and we introduce a simple, albeit computationally intensive, technique to calibrate an arbitrary score function. We demonstrate that this calibration procedure yields an increased number of identified spectra at a fixed false discovery rate (FDR) threshold. We also show that proper calibration of scores has a surprising effect on a previously described FDR estimation procedure, making the procedure less conservative. Finally, we provide empirical results suggesting that even partial calibration, which is much less computationally demanding, can yield significant increases in spectrum identification. Overall, we argue that accurate shotgun proteomics analysis requires careful attention to score calibration.

WITHDRAWN: Recent advances in chemometric methods for plant metabolomics: A review

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.

Shotgun proteomics reveals physiological response to ocean acidification in Crassostrea gigas

BACKGROUND

Ocean acidification as a result of increased anthropogenic CO2 emissions is occurring in marine and estuarine environments worldwide. The coastal ocean experiences additional daily and seasonal fluctuations in pH that can be lower than projected end-of-century open ocean pH reductions. In order to assess the impact of ocean acidification on marine invertebrates, Pacific oysters (Crassostrea gigas) were exposed to one of four different p CO2 levels for four weeks: 400 μatm (pH 8.0), 800 μatm (pH 7.7), 1000 μatm (pH 7.6), or 2800 μatm (pH 7.3).

RESULTS

At the end of the four week exposure period, oysters in all four p CO2 environments deposited new shell, but growth rate was not different among the treatments. However, micromechanical properties of the new shell were compromised by elevated p CO2. Elevated p CO2 affected neither whole body fatty acid composition, nor glycogen content, nor mortality rate associated with acute heat shock. Shotgun proteomics revealed that several physiological pathways were significantly affected by ocean acidification, including antioxidant response, carbohydrate metabolism, and transcription and translation. Additionally, the proteomic response to a second stress differed with p CO2, with numerous processes significantly affected by mechanical stimulation at high versus low p CO2 (all proteomics data are available in the ProteomeXchange under the identifier PXD000835).

CONCLUSIONS

Oyster physiology is significantly altered by exposure to elevated p CO2, indicating changes in energy resource use. This is especially apparent in the assessment of the effects of p CO2 on the proteomic response to a second stress. The altered stress response illustrates that ocean acidification may impact how oysters respond to other changes in their environment. These data contribute to an integrative view of the effects of ocean acidification on oysters as well as physiological trade-offs during environmental stress.

Disruption of murine Tcte3-3 induces tissue specific apoptosis via co-expression of Anxa5 and Pebp1

Programmed cell death or apoptosis plays a vital physiological role in the development and homeostasis. Any discrepancy in apoptosis may trigger testicular and neurodegenerative diseases, ischemic damage, autoimmune disorders and many types of cancer. Tcte3 (T-complex testis expressed 3) is an accessory component of axonemal and cytoplasmic dynein which expresses predominantly in meiotic and postmeiotic germ cells. It plays an essential role during spermatogenesis; however, to explore its diverse and complex functioning in male germ cell apoptosis, requires further prosecution. Here, 2D-gel electrophoresis, mass spectrometry and qRT-PCR analyses were performed to elucidate the differential expression of genes, in both wild-type and homozygous Tcte3-3 mice. We observed an increased expression of Tcte3 in homozygotes as compared to wild-type testes. Perpetually, an increased expression of Anxa5 and Pebp1, while a lower expression of Rsph1 was detected in Tcte3-3^-/- mice. We propose that over-expression of Pebp1 and Anxa5 in Tcte3-3^-/- testes might be due to increased apoptosis. To evaluate this possibility, testes specific microarray data set extracted from NCBI gene ontology omnibus (GEO) was used to cluster the possible co-expression partners of Tcte3. Further functional coherence of compiled candidate genes was monitored computationally by studying the common TFBS overlapped at the regulatory regions. Differential expression of Tcte3-3 and its involvement in apoptosis may provide a basis for the investigation of transcriptional specificities of other Tcte3 paralogs (Tcte3-1 and Tcte3-2). A complete understanding of controlling factors which have implications in regulating tissue-specific Tcte3 expression would provide additional insights into the gene control events. The collective knowledge may prove useful for the development of novel therapeutic regimen and would open new avenues in defining selective roles of Tcte3 in germ cell development.

Transcription factor networks in Drosophila melanogaster

Specific cellular fates and functions depend on differential gene expression, which occurs primarily at the transcriptional level and is controlled by complex regulatory networks of transcription factors (TFs). TFs act through combinatorial interactions with other TFs, cofactors, and chromatin-remodeling proteins. Here, we define protein-protein interactions using a coaffinity purification/mass spectrometry method and study 459 Drosophila melanogaster transcription-related factors, representing approximately half of the established catalog of TFs. We probe this network in vivo, demonstrating functional interactions for many interacting proteins, and test the predictive value of our data set. Building on these analyses, we combine regulatory network inference models with physical interactions to define an integrated network that connects combinatorial TF protein interactions to the transcriptional regulatory network of the cell. We use this integrated network as a tool to connect the functional network of genetic modifiers related to mastermind, a transcriptional cofactor of the Notch pathway.

Experimental and bioinformatic characterization of a recombinant polygalacturonase-inhibitor protein from pearl millet and its interaction with fungal polygalacturonases

Polygalacturonases (PGs) are hydrolytic enzymes employed by several phytopathogens to weaken the plant cell wall by degrading homopolygalacturonan, a major constituent of pectin. Plants fight back by employing polygalacturonase-inhibitor proteins (PGIPs). The present study compared the inhibition potential of pearl millet PGIP (Pennisetum glaucum; PglPGIP1) with the known inhibition of Phaseolus vulgaris PGIP (PvPGIP2) against two PGs, the PG-II isoform from Aspergillus niger (AnPGII) and the PG-III isoform from Fusarium moniliforme (FmPGIII). The key rationale was to elucidate the relationship between the extent of sequence similarity of the PGIPs and the corresponding PG inhibition potential. First, a pearl millet pgip gene (Pglpgip1) was isolated and phylogenetically placed among monocot PGIPs alongside foxtail millet (Setaria italica). Upstream sequence analysis of Pglpgip1 identified important cis-elements responsive to light, plant stress hormones, and anoxic stress. PglPGIP1, heterologously produced in Escherichia coli, partially inhibited AnPGII non-competitively with a pH optimum between 4.0 and 4.5, and showed no inhibition against FmPGIII. Docking analysis showed that the concave surface of PglPGIP1 interacted strongly with the N-terminal region of AnPGII away from the active site, whereas it weakly interacted with the C-terminus of FmPGIII. Interestingly, PglPGIP1 and PvPGIP2 employed similar motif regions with few identical amino acids for interaction with AnPGII at non-substrate-binding sites; however, they engaged different regions of AnPGII. Computational mutagenesis predicted D126 (PglPGIP1)-K39 (AnPGII) to be the most significant binding contact in the PglPGIP1-AnPGII complex. Such protein-protein interaction studies are crucial in the future generation of designer host proteins for improved resistance against ever-evolving pathogen virulence factors.

Architecture of the Saccharomyces cerevisiae RNA polymerase I Core Factor complex

Core Factor (CF) is a conserved RNA polymerase (Pol) I general transcription factor and is comprised of Rrn6, Rrn11, and the TFIIB-related subunit Rrn7. CF binds TBP, Pol I, and the regulatory factors Rrn3 and UAF. We used chemical crosslinking-mass spectrometry (CXMS) to determine the molecular architecture of CF and its interactions with TBP. The CF subunits assemble through an interconnected network of interactions between five structural domains that are conserved in orthologous subunits of the human Pol I factor SL1. The crosslinking-derived model was validated through a series of genetic and biochemical assays. Our combined results show the architecture of CF and the functions of the CF subunits in assembly of the complex. We extend these findings to model how CF assembles into the Pol I preinitiation complex, providing new insight into the roles of CF, TBP and Rrn3.

The calcium-dependent protein kinase 3 of toxoplasma influences basal calcium levels and functions beyond egress as revealed by quantitative phosphoproteome analysis

Calcium-dependent protein kinases (CDPKs) are conserved in plants and apicomplexan parasites. In Toxoplasma gondii, TgCDPK3 regulates parasite egress from the host cell in the presence of a calcium-ionophore. The targets and the pathways that the kinase controls, however, are not known. To identify pathways regulated by TgCDPK3, we measured relative phosphorylation site usage in wild type and TgCDPK3 mutant and knock-out parasites by quantitative mass-spectrometry using stable isotope-labeling with amino acids in cell culture (SILAC). This revealed known and novel phosphorylation events on proteins predicted to play a role in host-cell egress, but also a novel function of TgCDPK3 as an upstream regulator of other calcium-dependent signaling pathways, as we also identified proteins that are differentially phosphorylated prior to egress, including proteins important for ion-homeostasis and metabolism. This observation is supported by the observation that basal calcium levels are increased in parasites where TgCDPK3 has been inactivated. Most of the differential phosphorylation observed in CDPK3 mutants is rescued by complementation of the mutants with a wild type copy of TgCDPK3. Lastly, the TgCDPK3 mutants showed hyperphosphorylation of two targets of a related calcium-dependent kinase (TgCDPK1), as well as TgCDPK1 itself, indicating that this latter kinase appears to play a role downstream of TgCDPK3 function. Overexpression of TgCDPK1 partially rescues the egress phenotype of the TgCDPK3 mutants, reinforcing this conclusion. These results show that TgCDPK3 plays a pivotal role in regulating tachyzoite functions including, but not limited to, egress.

Computing exact p-values for a cross-correlation shotgun proteomics score function

The core of every protein mass spectrometry analysis pipeline is a function that assesses the quality of a match between an observed spectrum and a candidate peptide. We describe a procedure for computing exact p-values for the oldest and still widely used score function, SEQUEST XCorr. The procedure uses dynamic programming to enumerate efficiently the full distribution of scores for all possible peptides whose masses are close to that of the spectrum precursor mass. Ranking identified spectra by p-value rather than XCorr significantly reduces variance because of spectrum-specific effects on the score. In combination with the Percolator postprocessor, the XCorr p-value yields more spectrum and peptide identifications at a fixed false discovery rate than Mascot, X!Tandem, Comet, and MS-GF+ across a variety of data sets.

CAMS-RS: Clustering Algorithm for Large-Scale Mass Spectrometry Data Using Restricted Search Space and Intelligent Random Sampling

High-throughput mass spectrometers can produce massive amounts of redundant data at an astonishing rate with many of them having poor signal-to-noise (S/N) ratio. These low S/N ratio spectra may not get interpreted using conventional spectra-to-database matching techniques. In this paper, we present an efficient algorithm, CAMS-RS (Clustering Algorithm for Mass Spectra using Restricted Space and Sampling) for clustering of raw mass spectrometry data. CAMS-RS utilizes a novel metric (called F-set) that exploits the temporal and spatial patterns to accurately assess similarity between two given spectra. The F-set similarity metric is independent of the retention time and allows clustering of mass spectrometry data from independent LC-MS/MS runs. A novel restricted search space strategy is devised to limit the comparisons of the number of spectra. An intelligent sampling method is executed on individual bins that allow merging of the results to make the final clusters. Our experiments, using experimentally generated data sets, show that the proposed algorithm is able to cluster spectra with high accuracy and is helpful in interpreting low S/N ratio spectra. The CAMS-RS algorithm is highly scalable with increasing number of spectra and our implementation allows clustering of up to a million spectra within minutes.

Proteomic analysis of the Notch interactome

Recent large-scale studies have provided a global description of the interactome-the whole network of protein interactions in a cell or an organism-for several model organisms. Defining protein interactions on a proteome-wide scale has led to a better understanding of the cellular functions of many proteins, especially those that have not been studied by classical molecular genetic approaches. Here we describe the resources, methods, and techniques necessary for generation of such a proteome-scale interactome in a high throughput manner. These procedures will also be applicable to low or medium throughput focused studies aimed at understanding interactions between members of specific pathways such as Notch signaling.

PhosSA: Fast and accurate phosphorylation site assignment algorithm for mass spectrometry data

Phosphorylation site assignment of high throughput tandem mass spectrometry (LC-MS/MS) data is one of the most common and critical aspects of phosphoproteomics. Correctly assigning phosphorylated residues helps us understand their biological significance. The design of common search algorithms (such as Sequest, Mascot etc.) do not incorporate site assignment; therefore additional algorithms are essential to assign phosphorylation sites for mass spectrometry data. The main contribution of this study is the design and implementation of a linear time and space dynamic programming strategy for phosphorylation site assignment referred to as PhosSA. The proposed algorithm uses summation of peak intensities associated with theoretical spectra as an objective function. Quality control of the assigned sites is achieved using a post-processing redundancy criteria that indicates the signal-to-noise ratio properties of the fragmented spectra. The quality assessment of the algorithm was determined using experimentally generated data sets using synthetic peptides for which phosphorylation sites were known. We report that PhosSA was able to achieve a high degree of accuracy and sensitivity with all the experimentally generated mass spectrometry data sets. The implemented algorithm is shown to be extremely fast and scalable with increasing number of spectra (we report up to 0.5 million spectra/hour on a moderate workstation). The algorithm is designed to accept results from both Sequest and Mascot search engines. An executable is freely available at http://helixweb.nih.gov/ESBL/PhosSA/ for academic research purposes.

Molecular characterization of tick salivary gland glutaminyl cyclase

Glutaminyl cyclase (QC) catalyzes the cyclization of N-terminal glutamine residues into pyroglutamate. This post-translational modification extends the half-life of peptides and, in some cases, is essential in binding to their cognate receptor. Due to its potential role in the post-translational modification of tick neuropeptides, we report the molecular, biochemical and physiological characterization of salivary gland QC during the prolonged blood feeding of the black-legged tick (Ixodes scapularis) and the gulf-coast tick (Amblyomma maculatum). QC sequences from I. scapularis and A. maculatum showed a high degree of amino acid identity to each other and other arthropods and residues critical for zinc binding/catalysis (D159, E202, and H330) or intermediate stabilization (E201, W207, D248, D305, F325, and W329) are conserved. Analysis of QC transcriptional gene expression kinetics depicts an upregulation during the bloodmeal of adult female ticks prior to fast-feeding phases in both I. scapularis and A. maculatum suggesting a functional link with bloodmeal uptake. QC enzymatic activity was detected in saliva and extracts of tick salivary glands and midguts. Recombinant QC was shown to be catalytically active. Furthermore, knockdown of QC transcript by RNA interference resulted in lower enzymatic activity, and small, unviable egg masses in both studied tick species as well as lower engorged tick weights for I. scapularis. These results suggest that the post-translational modification of neurotransmitters and other bioactive peptides by QC is critical to oviposition and potentially other physiological processes. Moreover, these data suggest that tick-specific QC-modified neurotransmitters/hormones or other relevant parts of this system could potentially be used as novel physiological targets for tick control.

Sulfur oxidizers dominate carbon fixation at a biogeochemical hot spot in the dark ocean

Bacteria and archaea in the dark ocean (>200 m) comprise 0.3-1.3 billion tons of actively cycled marine carbon. Many of these microorganisms have the genetic potential to fix inorganic carbon (autotrophs) or assimilate single-carbon compounds (methylotrophs). We identified the functions of autotrophic and methylotrophic microorganisms in a vent plume at Axial Seamount, where hydrothermal activity provides a biogeochemical hot spot for carbon fixation in the dark ocean. Free-living members of the SUP05/Arctic96BD-19 clade of marine gamma-proteobacterial sulfur oxidizers (GSOs) are distributed throughout the northeastern Pacific Ocean and dominated hydrothermal plume waters at Axial Seamount. Marine GSOs expressed proteins for sulfur oxidation (adenosine phosphosulfate reductase, sox (sulfur oxidizing system), dissimilatory sulfite reductase and ATP sulfurylase), carbon fixation (ribulose-1,5-bisphosphate carboxylase oxygenase (RuBisCO)), aerobic respiration (cytochrome c oxidase) and nitrogen regulation (PII). Methylotrophs and iron oxidizers were also active in plume waters and expressed key proteins for methane oxidation and inorganic carbon fixation (particulate methane monooxygenase/methanol dehydrogenase and RuBisCO, respectively). Proteomic data suggest that free-living sulfur oxidizers and methylotrophs are among the dominant primary producers in vent plume waters in the northeastern Pacific Ocean.

Investigation of scrambled ions in tandem mass spectra, part 2. On the influence of the ions on peptide identification

A comprehensive investigation was performed to understand the influence of sequence scrambling in peptide ions on peptide identification results. To achieve this, four tandem mass spectrometry datasets with scrambled ions included and with them excluded were analyzed by Crux, X!Tandem, SpectraST, Lutefisk, and PepNovo. While the different algorithms differed in their performance, an increase in the number of correctly identified peptides was generally observed when removing scrambled ions, with the exception of the SpectraST algorithm. However, the variation of the match scores upon removal was unpredictable. Following these investigations, an interpretation was given on how the scrambled ions affect peptide identification. Lastly, a simulated theoretical mass spectral library derived from the NIST peptide Libraries was constructed and searched by SpectraST to study whether scrambled ions in predicted mass spectra could affect peptide identification. Consistent with the peptide library search results, no significant variations for dot product scores as well as peptide identification results were observed when these ions were included in the theoretical MS/MS spectra. From the five adopted algorithms, the SpectraST and Crux provided the most robust results, whereas X!Tandem, PepNovo, and Lutefisk were sensitive to the existence of the scrambled ions, especially the latter two de novo sequencing algorithms.

Performance evaluation of a dual linear ion trap-Fourier transform ion cyclotron resonance mass spectrometer for proteomics research

A novel dual cell linear ion trap Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) and its performance characteristics are reported. A linear ion trap-Fourier transform ion cyclotron resonance mass spectrometer has been modified to incorporate a LTQ-Velos mass spectrometer. This modified instrument features efficient ion accumulation and fast MS/MS acquisition capabilities of dual cell linear RF ion trap instruments coupled to the high mass accuracy, resolution, and dynamic range of a FT-ICR for improved proteomic coverage. The ion accumulation efficiency is demonstrated to be an order of magnitude greater than that observed with LTQ-FT Ultra instrumentation. The proteome coverage with yeast was shown to increase over the previous instrument generation by 50% (100% increase on the peptide level). In addition, many lower abundance level yeast proteins were only detected with this modified instrument. This novel configuration also enables beam type CID fragmentation using a dual cell RF ion trap mass spectrometer. This technique involves accelerating ions between traps while applying an elevated DC offset to one of the traps to accelerate ions and induce fragmentation. This instrument design may serve as a useful option for labs currently considering purchasing new instrumentation or upgrading existing instruments.

BIOLOGICAL SIGNIFICANCE

A novel hybrid mass spectrometer that allows increased MS/MS acquisition rates with high mass measurement accuracy and new ion fragmentation methods greatly improves the number of proteins, posttranslational modifications and protein-protein interactions that can be identified from cells.

Introduction to glycosylation and mass spectrometry

Glycosylation is increasingly recognized as a common and biologically significant post-translational modification of proteins. Modern mass spectrometry methods offer the best ways to characterize the glycosylation state of proteins. Both glycobiology and mass spectrometry rely on specialized nomenclature, techniques, and knowledge, which pose a barrier to entry by the nonspecialist. This introductory chapter provides an overview of the fundamentals of glycobiology, mass spectrometry methods, and the intersection of the two fields. Foundational material included in this chapter includes a description of the biological process of glycosylation, an overview of typical glycoproteomics workflows, a description of mass spectrometry ionization methods and instrumentation, and an introduction to bioinformatics resources. In addition to providing an orientation to the contents of the other chapters of this volume, this chapter cites other important works of potential interest to the practitioner. This overview, combined with the state-of-the-art protocols contained within this volume, provides a foundation for both glycobiologists and mass spectrometrists seeking to bridge the two fields.

A census of human soluble protein complexes

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

An Efficient Dynamic Programming Algorithm for Phosphorylation Site Assignment of Large-Scale Mass Spectrometry Data

Phosphorylation site assignment of large-scale data from high throughput tandem mass spectrometry (LC-MS/MS) data is an important aspect of phosphoproteomics. Correct assignment of phosphorylated residue(s) is important for functional interpretation of the data within a biological context. Common search algorithms (Sequest etc.) for mass spectrometry data are not designed for accurate site assignment; thus, additional algorithms are needed. In this paper, we propose a linear-time and linear-space dynamic programming strategy for phosphorylation site assignment. The algorithm, referred to as PhosSA, optimizes the objective function defined as the summation of peak intensities that are associated with theoretical phosphopeptide fragmentation ions. Quality control is achieved through the use of a post-processing criteria whose value is indicative of the signal-to-noise (S/N) properties and redundancy of the fragmentation spectra. The algorithm is tested using experimentally generated data sets of peptides with known phosphorylation sites while varying the fragmentation strategy (CID or HCD) and molar amounts of the peptides. The algorithm is also compatible with various peptide labeling strategies including SILAC and iTRAQ. PhosSA is shown to achieve > 99% accuracy with a high degree of sensitivity. The algorithm is extremely fast and scalable (able to process up to 0.5 million peptides in an hour). The implemented algorithm is freely available at http://helixweb.nih.gov/ESBL/PhosSA/ for academic purposes.

Electrophilic adduction of ubiquitin activating enzyme E1 by N,N-diethyldithiocarbamate inhibits ubiquitin activation and is accompanied by striatal injury in the rat

Previous studies have shown ubiquitin activating enzyme E1 to be sensitive to adduction through both Michael addition and SN(2) chemistry in vitro. E1 presents a biologically important putative protein target for adduction due to its role in initiating ubiquitin based protein processing and the involvement of impaired ubiquitin protein processing in two types of familial Parkinson's disease. We tested whether E1 is susceptible to xenobiotic-mediated electrophilic adduction in vivo and explored the potential contribution of E1 adduction to neurodegenerative events in an animal model. N,N-Diethyldithiocarbamate (DEDC) was administered to rats using a protocol that produces covalent cysteine modifications in vivo, and brain E1 protein adducts were characterized and mapped using shotgun LC-MS/MS. E1 activity, global and specific protein expression, and protein carbonyls were used to characterize cellular responses and injury in whole brain and dorsal striatal samples. The data demonstrate that DEDC treatment produced S-(ethylaminocarbonyl) adducts on Cys234 and Cys179 residues of E1 and decreased the levels of activated E1 and total ubiquitinated proteins. Proteomic analysis of whole brain samples identified expression changes for proteins involved in myelin structure, antioxidant response, and catechol metabolism, systems often disrupted in neurodegenerative disease. Our studies also delineated localized injury within the striatum as indicated by decreased levels of tyrosine hydroxylase, elevated protein carbonyl content, increased antioxidant enzyme and α-synuclein expression, and enhanced phosphorylation of tau and tyrosine hydroxylase. These data are consistent with E1 having similar susceptibility to adduction in vivo as previously reported in vitro and support further investigation into environmental agent adduction of E1 as a potential contributing factor to neurodegenerative disease. Additionally, this study supports the predictive value of in vitro screens for identifying sensitive protein targets that can be used to guide subsequent in vivo experiments.

Learning score function parameters for improved spectrum identification in tandem mass spectrometry experiments

The identification of proteins from spectra derived from a tandem mass spectrometry experiment involves several challenges: matching each observed spectrum to a peptide sequence, ranking the resulting collection of peptide-spectrum matches, assigning statistical confidence estimates to the matches, and identifying the proteins. The present work addresses algorithms to rank peptide-spectrum matches. Many of these algorithms, such as PeptideProphet, IDPicker, or Q-ranker, follow a similar methodology that includes representing peptide-spectrum matches as feature vectors and using optimization techniques to rank them. We propose a richer and more flexible feature set representation that is based on the parametrization of the SEQUEST XCorr score and that can be used by all of these algorithms. This extended feature set allows a more effective ranking of the peptide-spectrum matches based on the target-decoy strategy, in comparison to a baseline feature set devoid of these XCorr-based features. Ranking using the extended feature set gives 10-40% improvement in the number of distinct peptide identifications relative to a range of q-value thresholds. While this work is inspired by the model of the theoretical spectrum and the similarity measure between spectra used specifically by SEQUEST, the method itself can be applied to the output of any database search. Further, our approach can be trivially extended beyond XCorr to any linear operator that can serve as similarity score between experimental spectra and peptide sequences.

Surface acoustic wave nebulization facilitating lipid mass spectrometric analysis

Surface acoustic wave nebulization (SAWN) is a novel method to transfer nonvolatile analytes directly from the aqueous phase to the gas phase for mass spectrometric analysis. The lower ion energetics of SAWN and its planar nature make it appealing for analytically challenging lipid samples. This challenge is a result of their amphipathic nature, labile nature, and tendency to form aggregates, which readily precipitate clogging capillaries used for electrospray ionization (ESI). Here, we report the use of SAWN to characterize the complex glycolipid, lipid A, which serves as the membrane anchor component of lipopolysaccharide (LPS) and has a pronounced tendency to clog nano-ESI capillaries. We also show that unlike ESI SAWN is capable of ionizing labile phospholipids without fragmentation. Lastly, we compare the ease of use of SAWN to the more conventional infusion-based ESI methods and demonstrate the ability to generate higher order tandem mass spectral data of lipid A for automated structure assignment using our previously reported hierarchical tandem mass spectrometry (HiTMS) algorithm. The ease of generating SAWN-MS(n) data combined with HiTMS interpretation offers the potential for high throughput lipid A structure analysis.

Tempest: GPU-CPU computing for high-throughput database spectral matching

Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new "Accelerated Score" for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to that of the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer.

Cross-linking measurements of the Potato leafroll virus reveal protein interaction topologies required for virion stability, aphid transmission, and virus-plant interactions

Protein interactions are critical determinants of insect transmission for viruses in the family Luteoviridae. Two luteovirid structural proteins, the capsid protein (CP) and the readthrough protein (RTP), contain multiple functional domains that regulate virus transmission. There is no structural information available for these economically important viruses. We used Protein Interaction Reporter (PIR) technology, a strategy that uses chemical cross-linking and high resolution mass spectrometry, to discover topological features of the Potato leafroll virus (PLRV) CP and RTP that are required for the diverse biological functions of PLRV virions. Four cross-linked sites were repeatedly detected, one linking CP monomers, two within the RTP, and one linking the RTP and CP. Virus mutants with triple amino acid deletions immediately adjacent to or encompassing the cross-linked sites were defective in virion stability, RTP incorporation into the capsid, and aphid transmission. Plants infected with a new, infectious PLRV mutant lacking 26 amino acids encompassing a cross-linked site in the RTP exhibited a delay in the appearance of systemic infection symptoms. PIR technology provided the first structural insights into luteoviruses which are crucially lacking and are involved in vector-virus and plant-virus interactions. These are the first cross-linking measurements on any infectious, insect-transmitted virus.

Identifying and tracking proteins through the marine water column: insights into the inputs and preservation mechanisms of protein in sediments

Proteins generated during primary production represent an important fraction of marine organic nitrogen and carbon, and have the potential to provide organism-specific information in the environment. The Bering Sea is a highly productive system dominated by seasonal blooms and was used as a model system for algal proteins to be tracked through the water column and incorporated into detrital sedimentary material. Samples of suspended and sinking particles were collected at multiple depths along with surface sediments on the continental shelf and deeper basin of the Bering Sea. Modified standard proteomic preparations were used in conjunction with high pressure liquid chromatography-tandem mass spectrometry to identify the suite of proteins present and monitor changes in their distribution. In surface waters 207 proteins were identified, decreasing through the water column to 52 proteins identified in post-bloom shelf surface sediments and 24 proteins in deeper (3490 m) basin sediments. The vast majority of identified proteins in all samples were diatom in origin, reflecting their dominant contribution of biomass during the spring bloom. Identified proteins were predominantly from metabolic, binding/structural, and transport-related protein groups. Significant linear correlations were observed between the number of proteins identified and the concentration of total hydrolysable amino acids normalized to carbon and nitrogen. Organelle-bound, transmembrane, photosynthetic, and other proteins involved in light harvesting were preferentially retained during recycling. These findings suggest that organelle and membrane protection represent important mechanisms that enhance the preservation of protein during transport and incorporation into sediments.

Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification

Fourier transform-all reaction monitoring (FT-ARM) is a novel approach for the identification and quantification of peptides that relies upon the selectivity of high mass accuracy data and the specificity of peptide fragmentation patterns. An FT-ARM experiment involves continuous, data-independent, high mass accuracy MS/MS acquisition spanning a defined m/z range. Custom software was developed to search peptides against the multiplexed fragmentation spectra by comparing theoretical or empirical fragment ions against every fragmentation spectrum across the entire acquisition. A dot product score is calculated against each spectrum to generate a score chromatogram used for both identification and quantification. Chromatographic elution profile characteristics are not used to cluster precursor peptide signals to their respective fragment ions. FT-ARM identifications are demonstrated to be complementary to conventional data-dependent shotgun analysis, especially in cases where the data-dependent method fails because of fragmenting multiple overlapping precursors. The sensitivity, robustness, and specificity of FT-ARM quantification are shown to be analogous to selected reaction monitoring-based peptide quantification with the added benefit of minimal assay development. Thus, FT-ARM is demonstrated to be a novel and complementary data acquisition, identification, and quantification method for the large scale analysis of peptides.

Search and decoy: the automatic identification of mass spectra

In recent years, the generation and interpretation of MS/MS spectra for the identification of peptides and proteins has matured to a frequently used automatic workflow in Proteomics. Several software solutions for the automated analysis of MS/MS spectra allow for high-throughput/high-performance analyses of complex samples. Related to MS/MS searches, target-decoy approaches have gained more and more popularity: in a "decoy" part of the search database nonexistent sequences mimic real sequences (the "target" sequences). With their help, the number of falsely identified peptides/proteins can be estimated after a search and the resulting protein list can be cut at a specified false discovery rate (FDR). This is an essential prerequisite for all quantitative approaches, as they rely on correct identifications. Especially the label-free approach "spectral counting"-gaining more and more popularity due to low costs and simplicity-depends directly on the correctness of peptide-spectrum matches (PSMs). This work's aim is to describe five popular search engines-especially their general properties regarding protein identification, but also their quantification abilities, if those go beyond spectral counting. By doing so, Proteomics researchers are enabled to compare their features and to choose an appropriate solution for their specific question. Furthermore, the search engines are applied to a spectrum data set generated from a complex sample with a Thermo LTQ Velos OrbiTrap (Thermo Fisher Scientific, Waltham, MA, USA). The results of the search engines are compared, e.g., regarding time requirements, peptides and proteins found, and the search engines' behavior using the decoy approach.

A protein complex network of Drosophila melanogaster

Determining the composition of protein complexes is an essential step toward understanding the cell as an integrated system. Using coaffinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly 5,000 individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a statistical framework designed to define individual protein-protein interactions, led to the generation of a Drosophila protein interaction map (DPiM) encompassing 556 protein complexes. The high quality of the DPiM and its usefulness as a paradigm for metazoan proteomes are apparent from the recovery of many known complexes, significant enrichment for shared functional attributes, and validation in human cells. The DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. The DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution.

Absolute quantification of the glycolytic pathway in yeast: deployment of a complete QconCAT approach

The availability of label-free data derived from yeast cells (based on the summed intensity of the three strongest, isoform-specific peptides) permitted a preliminary assessment of protein abundances for glycolytic proteins. Following this analysis, we demonstrate successful application of the QconCAT technology, which uses recombinant DNA techniques to generate artificial concatamers of large numbers of internal standard peptides, to the quantification of enzymes of the glycolysis pathway in the yeast Saccharomyces cerevisiae. A QconCAT of 88 kDa (59 tryptic peptides) corresponding to 27 isoenzymes was designed and built to encode two or three analyte peptides per protein, and after stable isotope labeling of the standard in vivo, protein levels were determined by LC-MS, using ultra high performance liquid chromatography-coupled mass spectrometry. We were able to determine absolute protein concentrations between 14,000 and 10 million molecules/cell. Issues such as efficiency of extraction and completeness of proteolysis are addressed, as well as generic factors such as optimal quantotypic peptide selection and expression. In addition, the same proteins were quantified by intensity-based label-free analysis, and both sets of data were compared with other quantification methods.

Faster SEQUEST searching for peptide identification from tandem mass spectra

Computational analysis of mass spectra remains the bottleneck in many proteomics experiments. SEQUEST was one of the earliest software packages to identify peptides from mass spectra by searching a database of known peptides. Though still popular, SEQUEST performs slowly. Crux and TurboSEQUEST have successfully sped up SEQUEST by adding a precomputed index to the search, but the demand for ever-faster peptide identification software continues to grow. Tide, introduced here, is a software program that implements the SEQUEST algorithm for peptide identification and that achieves a dramatic speedup over Crux and SEQUEST. The optimization strategies detailed here employ a combination of algorithmic and software engineering techniques to achieve speeds up to 170 times faster than a recent version of SEQUEST that uses indexing. For example, on a single Xeon CPU, Tide searches 10,000 spectra against a tryptic database of 27,499 Caenorhabditis elegans proteins at a rate of 1550 spectra per second, which compares favorably with a rate of 8.8 spectra per second for a recent version of SEQUEST with index running on the same hardware.

A bayesian mixture model for comparative spectral count data in shotgun proteomics

Recent developments in mass-spectrometry-based shotgun proteomics, especially methods using spectral counting, have enabled large-scale identification and differential profiling of complex proteomes. Most such proteomic studies are interested in identifying proteins, the abundance of which is different under various conditions. Several quantitative methods have recently been proposed and implemented for this purpose. Building on some techniques that are now widely accepted in the microarray literature, we developed and implemented a new method using a Bayesian model to calculate posterior probabilities of differential abundance for thousands of proteins in a given experiment simultaneously. Our Bayesian model is shown to deliver uniformly superior performance when compared with several existing methods.

Protein identification using MS/MS data

The subject of this tutorial is protein identification and characterisation by database searching of MS/MS Data. Peptide Mass Fingerprinting is excluded because it is covered in a separate tutorial. Practical aspects of database searching are emphasised, such as choice of sequence database, effect of mass tolerance, and how to identify post-translational modifications. The relationship between sensitivity and specificity is discussed, as is the challenge of using peptide match information to infer which proteins were present in the sample. Since these tutorials are introductory in nature, most references are to reviews, rather than primary research papers. Some familiarity with mass spectrometry and protein chemistry is assumed. There is an accompanying slide presentation, including speaker notes, and a collection of web-based, practical exercises, designed to reinforce key points. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 6).

Automated lipid A structure assignment from hierarchical tandem mass spectrometry data

Infusion-based electrospray ionization (ESI) coupled to multiple-stage tandem mass spectrometry (MS(n)) is a standard methodology for investigating lipid A structural diversity (Shaffer et al. J. Am. Soc. Mass. Spectrom. 18(6), 1080-1092, 2007). Annotation of these MS(n) spectra, however, has remained a manual, expert-driven process. In order to keep up with the data acquisition rates of modern instruments, we devised a computational method to annotate lipid A MS(n) spectra rapidly and automatically, which we refer to as hierarchical tandem mass spectrometry (HiTMS) algorithm. As a first-pass tool, HiTMS aids expert interpretation of lipid A MS(n ) data by providing the analyst with a set of candidate structures that may then be confirmed or rejected. HiTMS deciphers the signature ions (e.g., A-, Y-, and Z-type ions) and neutral losses of MS(n) spectra using a species-specific library based on general prior structural knowledge of the given lipid A species under investigation. Candidates are selected by calculating the correlation between theoretical and acquired MS(n) spectra. At a false discovery rate of less than 0.01, HiTMS correctly assigned 85% of the structures in a library of 133 manually annotated Francisella tularensis subspecies novicida lipid A structures. Additionally, HiTMS correctly assigned 85% of the structures in a smaller library of lipid A species from Yersinia pestis demonstrating that it may be used across species.

Seasonal liver protein differences in a hibernator revealed by quantitative proteomics using whole animal isotopic labeling

Hibernation is an energy-saving strategy used by diverse species of mammals to survive winter. It is characterized by cycles between multi-day periods of torpor with low body temperature (T(b)), and short periods of rapid, spontaneous rewarming. The ability to retain cellular integrity and function throughout torpor and rewarming is a key attribute of hibernation. Livers from winter hibernators are resistant to cellular damage induced by cold storage followed by warm reperfusion. Identifying proteins that differ between the summer-sensitive and winter-protected phenotypic states is one useful approach that may elucidate the molecular mechanisms that underlie this protection. Here we employ a novel quantitative proteomics screening strategy whereby a newly-weaned 13-lined ground squirrel was metabolically labeled by ingesting heavy-isotope substituted ((15)N) Spirulina. The liver protein extract from this animal provided a common reference for quantitative evaluation of protein differences by its addition to extracts from pooled samples of summer active (SA) or winter entrance (Ent) phase hibernating ground squirrels. We identified 61 significantly different proteins between the two groups and compared them to proteins identified previously in the same samples using 2D gels. Of the 20 proteins common to the two datasets, the direction and magnitude of their differences were perfectly concordant for 18, providing confidence that both sets of altered proteins reflect bona fide differences between the two physiological states. Furthermore, the 41 novel proteins recovered in this study included many new enzymes in pathways identified previously: specifically, additional enzymes belonging to the urea cycle, amino acid and carbohydrate degradation, and lipid biosynthetic pathways were decreased, whereas enzymes involved in ketone body synthesis, fatty acid utilization, protein synthesis and gluconeogenesis were increased in the samples from entrance hibernators compared to summer active animals, providing additional specific evidence for the importance of these pathways in the hibernating phenotype.