Parimal Samir

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.

Analyzing the Cryptome: Uncovering Secret Sequences

The mammalian cryptome consists of bioactive peptides generated by the proteolysis of precursor proteins. It is speculated that the cryptide repertoire increases the complexity of the proteome by an order of magnitude. Cryptides have been found to function in a wide range of processes including neuronal signaling, antigen presentation, and the inflammatory response. Due to their potential as therapeutic agents, there has been an increasing interest in studying cryptides. In this review, we discuss different approaches for discovering these hidden peptides and how proteomic tools can be utilized to aid in their identification and characterization.

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

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.

The Yeast Eukaryotic Translation Initiation Factor 2B Translation Initiation Complex Interacts with the Fatty Acid Synthesis Enzyme YBR159W and Endoplasmic Reticulum Membranes

ABSTRACT 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.

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.

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.

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.

𝓁 2 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

Identification of Changing Ribosome Protein Compositions using Mass Spectrometry

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