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

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

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

Antigen processing and presentation responses for Neutrophils and NK-cells showed increased MHC-II sub-pathway activity that differed between vaccine groups.

<p><b>(A)</b> Neutrophil responses. <b>(B)</b> NK-cell responses. To the right: Heatmaps of subject-specific baseline log₂ fold changes for KEGG Antigen processing and presentation genes across post-vaccination days by vaccine group. Colored in red: up regulated from baseline; colored in green: down regulated from baseline. To the left: KEGG pathway maps for post-vaccination days at which this pathway was significantly perturbed (day 1 for neutrophils, day 28 for NK-cells). Pathway node color gradient encodes log₂ fold change difference (LFCD) between vaccine groups (for multi-gene nodes the median LFCD was used). In red: increased log₂ fold change response for the SV-AS03 group relative to the SV-PBS group, in green: decreased log₂ fold change response for the SV-AS03 group relative to the SV-AS03 group, and vice versa, in black: fold change close to 1, in dark grey: genes filtered out due to low overall expression, light grey: gene missing database mapping, white: non-human gene. DE genes are highlighted using red and green label colors.</p

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

<div><p>Background</p><p>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.</p><p>Objective and Methods</p><p>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.</p><p>Results</p><p>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.</p><p>Conclusions</p><p>Using this cell-based systems approach, novel mechanisms of action for AS03-adjuvanted pandemic influenza vaccination were observed.</p><p>Trial Registration</p><p>ClinicalTrials.gov <a href="https://clinicaltrials.gov/ct2/show/NCT01573312" target="_blank">NCT01573312</a></p></div

Proteomic analysis of purified immune cells after TIV vaccination.

<p><b>(a)</b> Pair-wise comparison of day 0 protein profiles (3,852 proteins, filtered to remove zero values and contaminating keratins) from a vaccinated subject shows that proteomes of sorted cells correlate poorly with PBMC. <b>(b)</b> PCA of protein profiles from a TIV-vaccinated subject at four time points shows that purified immune cell types cluster into distinct groups. <b>(c)</b> Semi-supervised hierarchical clustering analysis of relative protein expression from a vaccinated individual reveals that purified immune cells have distinct proteomic expression profiles compared to PBMC. Data was centered across protein and cell type; red = up, black = no change, green = down.</p

Unique modules of proteins are differentially expressed in each immune cell type after TIV vaccination.

<p>Differentially expressed proteins (≥1.25-fold change) that were shared between both subjects after vaccination with TIV were subjected to semi-supervised hierarchical clustering analysis. Log2 fold change values of shared DE proteins in each cell type from both subjects were clustered at <b>(a)</b> day 1 (196 proteins), <b>(b)</b> day 3 (263 proteins), and <b>(c)</b> day 7 (199 proteins) post-vaccination. Very little overlap of differentially expressed proteins is observed between cell types; red = up; yellow = no change; blue = down. B cell data was derived from only one subject due to insufficient recovery of B cells from the second subject.</p

Unique modules of RNA transcripts are differentially expressed in each immune cell type after TIV vaccination.

<p>Differentially expressed RNA transcripts (≥1.5-fold change, p < 0.05) that were shared between both subjects after TIV-vaccination were subjected to semi-supervised hierarchical clustering analysis. Log2 fold-change values of shared DE transcripts in all cell types from both subjects were clustered at <b>(a)</b> day 1 (463 transcripts), <b>(b)</b> day 3 (653 transcripts), and <b>(c)</b> day 7 (428 transcripts) post-vaccination. Very little overlap of shared differentially expressed RNA transcripts is observed between cell types; red = up; yellow = no change; blue = down.</p