Xinnan Niu

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 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.

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.

Peptide identification based on fuzzy classification and clustering

BackgroundThe 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.ResultsA 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.ConclusionsOur 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.

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

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.

An adaptive classification model for peptide identification

BackgroundPeptide sequence assignment is the central task in protein identification with MS/MS-based strategies. Although a number of post-database search algorithms for filtering target peptide spectrum matches (PSMs) have been developed, the discrepancy among the output PSMs is usually significant, remaining a few disputable PSMs. Current studies show that a number of target PSMs which are close to decoy PSMs can hardly be separated from those decoys by only using the discrimination function.ResultsIn this paper, we assign each target PSM a weight showing its possibility of being correct. We employ a SVM-based learning model to search the optimal weight for each target PSM and develop a new score system, CRanker, to rank all target PSMs. Due to the large PSM datasets generated in routine database searches, we use the Cholesky factorization technique for storing a kernel matrix to reduce the memory requirement.ConclusionsCompared with PeptideProphet and Percolator, CRanker has identified more PSMs under similar false discover rates over different datasets. CRanker has shown consistent performance on different test sets, validated the reasonability the proposed model.

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.

A weighted classification model for peptide identification

Although a number of sequence database search tools and post-database search algorithms for filtering target PSMs have been developed, the discrepancy among the output PSMs is usually significant, remaining a few disputable PSMs. We employ a SVM-based learning model to search the optimal weight for each target PSM and develop a new score system, C-Ranker, to rank all target PSMs. Compared with PeptideProphet and Percolator, CRanker has identified more PSMs under similar false discover rates over different datasets.

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.