A Time-Resolved Cryo-EM Study of Saccharomyces cerevisiae 80S Ribosome Protein Composition in Response to a Change in Carbon Source.

The role of the ribosome in the regulation of gene expression has come into increased focus. It is proposed that ribosomes are catalytic engines capable of changing their protein composition in response to environmental stimuli. Time-resolved cryo-electron microscopy (cryo-EM) techniques are employed to identify quantitative changes in the protein composition and structure of the Saccharomyces cerevisiae 80S ribosomes after shifting the carbon source from glucose to glycerol. Using cryo-EM combined with the computational classification approach, it is found that a fraction of the yeast cells' 80S ribosomes lack ribosomal proteins at the entrance and exit sites for tRNAs, including uL16(RPL10), eS1(RPS1), uS11(RPS14A/B), and eS26(RPS26A/B). This fraction increased after a change from glucose to glycerol medium. The quantitative structural analysis supports the hypothesis that ribosomes are dynamic complexes that alter their composition in response to changes in growth or environmental conditions.

A cost-sensitive online learning method for peptide identification.

BACKGROUND: Post-database search is a key procedure in peptide identification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with a distribution of unbalanced PSMs. A more efficient learning strategy is required for improving the accuracy of peptide identification on challenging datasets. While complex learning models have larger power of classification, they may cause overfitting problems and introduce computational complexity on large-scale datasets. Kernel methods map data from the sample space to high dimensional spaces where data relationships can be simplified for modeling. RESULTS: In order to tackle the computational challenge of using the kernel-based learning model for practical peptide identification problems, we present an online learning algorithm, OLCS-Ranker, which iteratively feeds only one training sample into the learning model at each round, and, as a result, the memory requirement for computation is significantly reduced. Meanwhile, we propose a cost-sensitive learning model for OLCS-Ranker by using a larger loss of decoy PSMs than that of target PSMs in the loss function. CONCLUSIONS: The new model can reduce its false discovery rate on datasets with a distribution of unbalanced PSMs. Experimental studies show that OLCS-Ranker outperforms other methods in terms of accuracy and stability, especially on datasets with a distribution of unbalanced PSMs. Furthermore, OLCS-Ranker is 15-85 times faster than CRanker.

Targeted Identification of Protein Interactions in Eukaryotic mRNA Translation.

To identify protein-protein interactions and phosphorylated amino acid sites in eukaryotic mRNA translation, replicate TAP-MudPIT and control experiments are performed targeting Saccharomyces cerevisiae genes previously implicated in eukaryotic mRNA translation by their genetic and/or functional roles in translation initiation, elongation, termination, or interactions with ribosomal complexes. Replicate tandem affinity purifications of each targeted yeast TAP-tagged mRNA translation protein coupled with multidimensional liquid chromatography and tandem mass spectrometry analysis are used to identify and quantify copurifying proteins. To improve sensitivity and minimize spurious, nonspecific interactions, a novel cross-validation approach is employed to identify the most statistically significant protein-protein interactions. Using experimental and computational strategies discussed herein, the previously described protein composition of the canonical eukaryotic mRNA translation initiation, elongation, and termination complexes is calculated. In addition, statistically significant unpublished protein interactions and phosphorylation sites for S. cerevisiae's mRNA translation proteins and complexes are identified.

AS03-Adjuvanted H5N1 Avian Influenza Vaccine Modulates Early Innate Immune Signatures in Human Peripheral Blood Mononuclear Cells.

BACKGROUND: Adjuvant System 03 (AS03) markedly enhances responses to influenza A/H5N1 vaccines, but the mechanisms of this enhancement are incompletely understood. METHODS: Using ribonucleic acid sequencing on peripheral blood mononuclear cells (PBMCs) from AS03-adjuvanted and unadjuvanted inactivated H5N1 vaccine recipients, we identified differentially expressed genes, enriched pathways, and genes that correlated with serologic responses. We compared bulk PBMC findings with our previously published assessments of flow-sorted immune cell types. RESULTS: AS03-adjuvanted vaccine induced the strongest differential signals on day 1 postvaccination, activating multiple innate immune pathways including interferon and JAK-STAT signaling, Fcγ receptor (FcγR)-mediated phagocytosis, and antigen processing and presentation. Changes in signal transduction and immunoglobulin genes predicted peak hemagglutinin inhibition (HAI) titers. Compared with individual immune cell types, activated PBMC genes and pathways were most similar to innate immune cells. However, several pathways were unique to PBMCs, and several pathways identified in individual cell types were absent in PBMCs. CONCLUSIONS: Transcriptomic analysis of PBMCs after AS03-adjuvanted H5N1 vaccination revealed early activation of innate immune signaling, including a 5- to 8-fold upregulation of FcγR1A/1B/1C genes. Several early gene responses were correlated with HAI titer, indicating links with the adaptive immune response. Although PBMCs and cell-specific results shared key innate immune signals, unique signals were identified by both approaches.

Critical Role for Saccharomyces cerevisiae Asc1p in Translational Initiation at Elevated Temperatures.

The eukaryotic ribosomal protein RACK1/Asc1p is localized to the mRNA exit channel of the 40S subunit but lacks a defined role in mRNA translation. Saccharomyces cerevisiae deficient in ASC1 exhibit temperature-sensitive growth. Using this null mutant, potential roles for Asc1p in translation and ribosome biogenesis are evaluated. At the restrictive temperature the asc1Δ null mutant has reduced polyribosomes. To test the role of Asc1p in ribosome stability, cryo-EM is used to examine the structure of 80S ribosomes in an asc1Δ yeast deletion mutant at both the permissive and nonpermissive temperatures. CryoEM indicates that loss of Asc1p does not severely disrupt formation of this complex structure. No defect is found in rRNA processing in the asc1Δ null mutant. A proteomic approach is applied to survey the effect of Asc1p loss on the global translation of yeast proteins. At the nonpermissive temperature, the asc1Δ mutant has reduced levels of ribosomal proteins and other factors critical for translation. Collectively, these results are consistent with recent observations suggesting that Asc1p is important for ribosome occupancy of short mRNAs. The results show the Asc1 ribosomal protein is critical in translation during heat stress.

Identification of Changing Ribosome Protein Compositions using Mass Spectrometry.

The regulatory role of the ribosome in gene expression has come into sharper focus. It has been proposed that ribosomes are dynamic complexes capable of changing their protein composition in response to environmental stimuli. MS is applied to identify quantitative changes in the protein composition of S. cerevisiae 80S ribosomes in response to different environmental stimuli. Using quantitative MS, it is found that the paralog yeast ribosomal proteins RPL8A (eL8A) and RPL8B (eL8B) change their relative proportions in the 80S ribosome when yeast is switched from growth in glucose to glycerol. By using yeast genetics and polysome profiling, it is shown that yeast ribosomes containing either RPL8A or RPL8B are not functionally interchangeable. The quantitative proteomic data support the hypothesis that ribosomes are dynamic complexes that alter their composition and functional activity in response to changes in growth or environmental conditions.

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.

Cell-Based Systems Biology Analysis of Human AS03-Adjuvanted H5N1 Avian Influenza Vaccine Responses: A Phase I Randomized Controlled Trial.

BACKGROUND: Vaccine development for influenza A/H5N1 is an important public health priority, but H5N1 vaccines are less immunogenic than seasonal influenza vaccines. Adjuvant System 03 (AS03) markedly enhances immune responses to H5N1 vaccine antigens, but the underlying molecular mechanisms are incompletely understood. OBJECTIVE AND METHODS: We compared the safety (primary endpoint), immunogenicity (secondary), gene expression (tertiary) and cytokine responses (exploratory) between AS03-adjuvanted and unadjuvanted inactivated split-virus H5N1 influenza vaccines. In a double-blinded clinical trial, we randomized twenty adults aged 18-49 to receive two doses of either AS03-adjuvanted (n = 10) or unadjuvanted (n = 10) H5N1 vaccine 28 days apart. We used a systems biology approach to characterize and correlate changes in serum cytokines, antibody titers, and gene expression levels in six immune cell types at 1, 3, 7, and 28 days after the first vaccination. RESULTS: Both vaccines were well-tolerated. Nine of 10 subjects in the adjuvanted group and 0/10 in the unadjuvanted group exhibited seroprotection (hemagglutination inhibition antibody titer > 1:40) at day 56. Within 24 hours of AS03-adjuvanted vaccination, increased serum levels of IL-6 and IP-10 were noted. Interferon signaling and antigen processing and presentation-related gene responses were induced in dendritic cells, monocytes, and neutrophils. Upregulation of MHC class II antigen presentation-related genes was seen in neutrophils. Three days after AS03-adjuvanted vaccine, upregulation of genes involved in cell cycle and division was detected in NK cells and correlated with serum levels of IP-10. Early upregulation of interferon signaling-related genes was also found to predict seroprotection 56 days after first vaccination. CONCLUSIONS: Using this cell-based systems approach, novel mechanisms of action for AS03-adjuvanted pandemic influenza vaccination were observed. TRIAL REGISTRATION: ClinicalTrials.gov NCT01573312.

l2 Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification.

SEQUEST is a database-searching engine, which calculates the correlation score between observed spectrum and theoretical spectrum deduced from protein sequences stored in a flat text file, even though it is not a relational and object-oriental repository. Nevertheless, the SEQUEST score functions fail to discriminate between true and false PSMs accurately. Some approaches, such as PeptideProphet and Percolator, have been proposed to address the task of distinguishing true and false PSMs. However, most of these methods employ time-consuming learning algorithms to validate peptide assignments [1] . In this paper, we propose a fast algorithm for validating peptide identification by incorporating heterogeneous information from SEQUEST scores and peptide digested knowledge. To automate the peptide identification process and incorporate additional information, we employ l2 multiple kernel learning (MKL) to implement the current peptide identification task. Results on experimental datasets indicate that compared with state-of-the-art methods, i.e., PeptideProphet and Percolator, our data fusing strategy has comparable performance but reduces the running time significantly.

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.

Proteomic contributions to our understanding of vaccine and immune responses.

Vaccines are one of the greatest public health successes; yet, due to the empirical nature of vaccine design, we have an incomplete understanding of how the genes and proteins induced by vaccines contribute to the development of both protective innate and adaptive immune responses. While the advent of genomics has enabled new vaccine development and facilitated understanding of the immune response, proteomics identifies potentially new vaccine antigens with increasing speed and sensitivity. In addition, as proteomics is complementary to transcriptomic approaches, a combination of both approaches provides a more comprehensive view of the immune response after vaccination via systems vaccinology. This review details the advances that proteomic strategies have made in vaccine development and reviews how proteomics contributes to the development of a more complete understanding of human vaccines and immune responses.

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.

Viral infection causes a shift in the self peptide repertoire presented by human MHC class I molecules.

PURPOSE: MHC class I presentation of peptides allows T cells to survey the cytoplasmic protein milieu of host cells. During infection, presentation of self peptides is, in part, replaced by presentation of microbial peptides. However, little is known about the self peptides presented during infection, despite the fact that microbial infections alter host cell gene expression patterns and protein metabolism. EXPERIMENTAL DESIGN: The self peptide repertoire presented by HLA-A*01;01, HLA-A*02;01, HLA-B*07;02, HLA-B*35;01, and HLA-B*45;01 (where HLA is human leukocyte antigen) was determined by tandem MS before and after vaccinia virus infection. RESULTS: We observed a profound alteration in the self peptide repertoire with hundreds of self peptides uniquely presented after infection for which we have coined the term "self peptidome shift." The fraction of novel self peptides presented following infection varied for different HLA class I molecules. A large part (approximately 40%) of the self peptidome shift arose from peptides derived from type I interferon-inducible genes, consistent with cellular responses to viral infection. Interestingly, approximately 12% of self peptides presented after infection showed allelic variation when searched against approximately 300 human genomes. CONCLUSION AND CLINICAL RELEVANCE: Self peptidome shift in a clinical transplant setting could result in alloreactivity by presenting new self peptides in the context of infection-induced inflammation.

Analysis of protein composition using multidimensional chromatography and mass spectrometry.

Multidimensional liquid chromatography of peptides produced by protease digestion of complex protein mixtures followed by tandem mass spectrometry can be coupled with automated database searching to identify large numbers of proteins in complex samples. These methods avoid the limitations of gel electrophoresis and in-gel digestions by directly identifying protein mixtures in solution. One method used extensively is named Multidimensional Protein Identification Technology (MudPIT), where reversed-phase chromatography and strong cation-exchange chromatography are coupled directly in a microcapillary column. This column is then placed in line between an HPLC and a mass spectrometer for complex mixture analysis. MudPIT remains a powerful approach for analyzing complex mixtures like whole proteomes and protein complexes. MudPIT is used for quantitative proteomic analysis of complex mixtures to generate novel biological insights.

Discovering naturally processed antigenic determinants that confer protective T cell immunity.

CD8+ T cells (TCD8) confer protective immunity against many infectious diseases, suggesting that microbial TCD8 determinants are promising vaccine targets. Nevertheless, current T cell antigen identification approaches do not discern which epitopes drive protective immunity during active infection - information that is critical for the rational design of TCD8-targeted vaccines. We employed a proteomics-based approach for large-scale discovery of naturally processed determinants derived from a complex pathogen, vaccinia virus (VACV), that are presented by the most frequent representatives of four major HLA class I supertypes. Immunologic characterization revealed that many previously unidentified VACV determinants were recognized by smallpox-vaccinated human peripheral blood cells in a variegated manner. Many such determinants were recognized by HLA class I-transgenic mouse immune TCD8 too and elicited protective TCD8 immunity against lethal intranasal VACV infection. Notably, efficient processing and stable presentation of immune determinants as well as the availability of naive TCD8 precursors were sufficient to drive a multifunctional, protective TCD8 response. Our approach uses fundamental insights into T cell epitope processing and presentation to define targets of protective TCD8 immunity within human pathogens that have complex proteomes, suggesting that this approach has general applicability in vaccine sciences.

A novel algorithm for validating peptide identification from a shotgun proteomics search engine.

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has revolutionized the proteomics analysis of complexes, cells, and tissues. In a typical proteomic analysis, the tandem mass spectra from a LC-MS/MS experiment are assigned to a peptide by a search engine that compares the experimental MS/MS peptide data to theoretical peptide sequences in a protein database. The peptide spectra matches are then used to infer a list of identified proteins in the original sample. However, the search engines often fail to distinguish between correct and incorrect peptides assignments. In this study, we designed and implemented a novel algorithm called De-Noise to reduce the number of incorrect peptide matches and maximize the number of correct peptides at a fixed false discovery rate using a minimal number of scoring outputs from the SEQUEST search engine. The novel algorithm uses a three-step process: data cleaning, data refining through a SVM-based decision function, and a final data refining step based on proteolytic peptide patterns. Using proteomics data generated on different types of mass spectrometers, we optimized the De-Noise algorithm on the basis of the resolution and mass accuracy of the mass spectrometer employed in the LC-MS/MS experiment. Our results demonstrate De-Noise improves peptide identification compared to other methods used to process the peptide sequence matches assigned by SEQUEST. Because De-Noise uses a limited number of scoring attributes, it can be easily implemented with other search engines.

Sculpting MHC class II-restricted self and non-self peptidome by the class I Ag-processing machinery and its impact on Th-cell responses.

It is generally assumed that the MHC class I antigen (Ag)-processing (CAP) machinery - which supplies peptides for presentation by class I molecules - plays no role in class II-restricted presentation of cytoplasmic Ags. In striking contrast to this assumption, we previously reported that proteasome inhibition, TAP deficiency or ERAAP deficiency led to dramatically altered T helper (Th)-cell responses to allograft (HY) and microbial (Listeria monocytogenes) Ags. Herein, we tested whether altered Ag processing and presentation, altered CD4(+) T-cell repertoire, or both underlay the above finding. We found that TAP deficiency and ERAAP deficiency dramatically altered the quality of class II-associated self peptides suggesting that the CAP machinery impacts class II-restricted Ag processing and presentation. Consistent with altered self peptidomes, the CD4(+) T-cell receptor repertoire of mice deficient in the CAP machinery substantially differed from that of WT animals resulting in altered CD4(+) T-cell Ag recognition patterns. These data suggest that TAP and ERAAP sculpt the class II-restricted peptidome, impacting the CD4(+) T-cell repertoire, and ultimately altering Th-cell responses. Together with our previous findings, these data suggest multiple CAP machinery components sequester or degrade MHC class II-restricted epitopes that would otherwise be capable of eliciting functional Th-cell responses.

The yeast eukaryotic translation initiation factor 2B translation initiation complex interacts with the fatty acid synthesis enzyme YBR159W and endoplasmic reticulum membranes.

Using affinity purifications coupled with mass spectrometry and yeast two-hybrid assays, we show the Saccharomyces cerevisiae translation initiation factor complex eukaryotic translation initiation factor 2B (eIF2B) and the very-long-chain fatty acid (VLCFA) synthesis keto-reductase enzyme YBR159W physically interact. The data show that the interaction is specifically between YBR159W and eIF2B and not between other members of the translation initiation or VLCFA pathways. A ybr159wΔ null strain has a slow-growth phenotype and a reduced translation rate but a normal GCN4 response to amino acid starvation. Although YBR159W localizes to the endoplasmic reticulum membrane, subcellular fractionation experiments show that a fraction of eIF2B cofractionates with lipid membranes in a YBR159W-independent manner. We show that a ybr159wΔ yeast strain and other strains with null mutations in the VLCFA pathway cause eIF2B to appear as numerous foci throughout the cytoplasm.

Peptide identification based on fuzzy classification and clustering.

BACKGROUND: The sequence database searching has been the dominant method for peptide identification, in which a large number of peptide spectra generated from LC/MS/MS experiments are searched using a search engine against theoretical fragmentation spectra derived from a protein sequences database or a spectral library. Selecting trustworthy peptide spectrum matches (PSMs) remains a challenge. RESULTS: A novel scoring method named FC-Ranker is developed to assign a nonnegative weight to each target PSM based on the possibility of its being correct. Particularly, the scores of PSMs are updated by using a fuzzy SVM classification model and a fuzzy silhouette index iteratively. Trustworthy PSMs will be assigned high scores when the algorithm stops. CONCLUSIONS: Our experimental studies show that FC-Ranker outperforms other post-database search algorithms over a variety of datasets, and it can be extended to solve a general classification problem with uncertain labels.