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For participants receiving experimental gene therapy or gene editing clinical trials, the FDA requires contemporaneous controls to compare clinical outcomes. However, developing a contemporaneous cohort of rare diseases is costly and requires multiple person-hours. In a single referral center for sickle cell disease, we tested the hypothesis that we could create an automated contemporaneous cohort of children and adults with sickle cell anemia (SCA) to predict mortality. Data were obtained between 1/1/2004 and 4/30/2021. We identified 419 individuals with SCA with consistent medical care (i.e., followed continuously for >0.5 years with no visit gaps ≥3.0 years). The median age was 10.2 years (IQR 1.0 - 24.0 years), with a median follow-up of 7.4 years (IQR 3.6-13.5 years) and 47 deaths. A total of 98% (274 of 277) of the children remained alive at 18 years of age, and 34.3% (94 of 274) of those children were followed into adulthood. For adults, the median age of survival was 49.3 years of age. Treatment groups were mutually exclusive and in a hierarchical order: hematopoietic stem cell transplant (n=22)>regular blood transfusion for at least two years (n=56)>hydroxyurea for at least one year (n=243)>no disease-modifying therapy (n=98). Compared to those receiving no disease-modifying treatment, those treated with hydroxyurea therapy had significantly lower hazard of mortality (hazard ratio=0.38, p=0.016), but no statistical difference for those receiving regular blood transfusions compared to no disease-modifying therapy (hazard ratio=0.71, p=0.440). An automated contemporaneous SCA cohort can be generated to estimate mortality in children and adults with SCA.

OBJECTIVE: This study measures effect of CYP2C19 genotype on ischemic stroke risk during clopidogrel therapy for asymptomatic, extracranial carotid stenosis patients. METHODS: Using deidentified electronic health records, patients were selected for retrospective cohort using administrative code for carotid stenosis, availability of CYP2C19 genotype result, clopidogrel exposure, and established patient care. Patients with intracranial atherosclerosis, aneurysm, arteriovenous malformation, prior ischemic stroke, or observation time

Background/Objectives Melanocortin-4 receptor (MC4R) plays an essential role in food intake and energy homeostasis. More than 170 MC4R variants have been described over the past two decades, with conflicting reports regarding the prevalence and phenotypic effects of these variants in diverse cohorts. To determine the frequency of MC4R variants in large cohort of different ancestries, we evaluated the MC4R coding region for 20,537 eMERGE participants with sequencing data plus additional 77,454 independent individuals with genome-wide genotyping data at this locus. Subjects/Methods The sequencing data were obtained from the eMERGE phase III study, in which multisample variant call format calls have been generated, curated, and annotated. In addition to penetrance estimation using body mass index (BMI) as a binary outcome, GWAS and PheWAS were performed using median BMI in linear regression analyses. All results were adjusted for principal components, age, sex, and sites of genotyping. Results Targeted sequencing data of MC4R revealed 125 coding variants in 1839 eMERGE participants including 30 unreported coding variants that were predicted to be functionally damaging. Highly penetrant unreported variants included (L325I, E308K, D298N, S270F, F261L, T248A, D111V, and Y80F) in which seven participants had obesity class III defined as BMI ≥ 40 kg/m2. In GWAS analysis, in addition to known risk haplotype upstream of MC4R (best variant rs6567160 (P = 5.36 × 10−25, Beta = 0.37), a novel rare haplotype was detected which was protective against obesity and encompassed the V103I variant with known gain-of-function properties (P = 6.23 × 10−08, Beta = −0.62). PheWAS analyses extended this protective effect of V103I to type 2 diabetes, diabetic nephropathy, and chronic renal failure independent of BMI. Conclusions MC4R screening in a large eMERGE cohort confirmed many previous findings, extend the MC4R pleotropic effects, and discovered additional MC4R rare alleles that probably contribute to obesity.

The electronic health record (EHR) provides valuable data for understanding physical and mental health conditions in autism. We developed an approach to identify charts of autistic young adults, retrieved from our institution's de-identified EHR database. Clinical notes within two cohorts were identified. Cohort 1 charts had at least one International Classification of Diseases (ICD-CM) autism code. Cohort 2 charts had only autism key terms without ICD-CM codes, and at least four notes per chart. A natural language processing tool parsed medical charts to identify key terms associated with autism diagnoses and mapped them to Unified Medical Language System Concept Unique Identifiers (CUIs). Average scores were calculated for each set of charts based on captured CUIs. Chart review determined whether patients met criteria for autism using a classification rubric. In Cohort 1, of 418 patients, 361 were confirmed to have autism by chart review. Sensitivity was 0.99 and specificity was 0.68 with positive predictive value (PPV) of 0.97. Specificity improved to 0.81 (sensitivity was 0.95; PPV was 0.98) when the number of notes was limited to four or more per chart. In Cohort 2, 48 of 136 patients were confirmed to have autism by chart review. Sensitivity was 0.95, specificity was 0.73, and PPV was 0.70. Our approach, which included using key terms, identified autism charts with high sensitivity, even in the absence of ICD-CM codes. Relying on ICD-CM codes alone may result in inclusion of false positive cases and exclusion of true cases with autism.

From the perspective of most regulatory agencies, it is usually unethical to perform interventional clinical trials on pregnant people. While this policy recognizes the vulnerability of an expectant mother and unborn child, it has created a public health emergency for millions of pregnant patients through a dearth of robust safety data for many common drugs. To address this problem, we harnessed an enterprise collection of 2.8M electronic health records (EHRs) originally collected from routine primary care, leveraging the data linkage between mothers and their babies to create a surrogate for randomized, controlled drug trials in this population. To demonstrate the feasibility of our clinical trial emulation platform to stimulate new hypotheses for post-market drug surveillance, we identified 1,054 drugs historically prescribed to pregnant patients and developed a medication history-wide association study and follow-up evidence synthesis platform—leveraging expert clinician review and real-world data analysis—to test the effects of maternal exposure to these drugs on the incidence of neurodevelopmental defects in their children. Our results replicate known teratogenic risks and existing knowledge on drug structure-related teratogenic risks. Herein, we highlight 5 common drug classes that we believe warrant further assessment of their safety in pregnancy. We also discuss our efforts to develop a discovery-to-regulatory framework that could allow for pragmatic translation of our results to enhanced regulatory policy. Collectively, our work presents a simple approach to evaluating the utility of EHRs in guiding new regulatory review programs focused on improving the delicate equipoise of accuracy and ethics inherent to assessing drug safety in an extremely vulnerable patient population.

Post-marketing population pharmacokinetic (PK) and pharmacodynamic (PD) studies can be useful to capture patient characteristics affecting PK or PD in real-world settings. These studies require longitudinally measured dose, outcomes, and covariates in large numbers of patients; however, prospective data collection is cost-prohibitive. Electronic health records (EHRs) can be an excellent source for such data, but there are challenges, including accurate ascertainment of drug dose. We developed a standardized system to prepare datasets from EHRs for population PK/PD studies. Our system handles a variety of tasks involving data extraction from clinical text using a natural language processing algorithm, data processing, and data building. Applying this system, we performed a fentanyl population PK analysis, resulting in comparable parameter estimates to a prior study. This new system makes the EHR data extraction and preparation process more efficient and accurate, and provides a powerful tool to facilitate post-marketing population PK/PD studies using information available in EHRs.

The potential impact of using human genetic data linked to longitudinal electronic medical records on drug development is extraordinary; however, the practical application of these data necessitates some organizational innovations. Vanderbilt has created resources such as an easily queried database of >2.6 million de-identified electronic health records linked to BioVU, which is a DNA biobank with more than 230,000 unique samples. To ensure these data are used to maximally benefit and accelerate both de novo drug discovery and drug repurposing efforts, we created the Accelerating Drug Development and Repurposing Incubator, a multidisciplinary think tank of experts in various therapeutic areas within both basic and clinical science as well as experts in legal, business, and other operational domains. The Incubator supports a diverse pipeline of drug indication finding projects, leveraging the natural experiment of human genetics.

Additional file 1 Additional results. The derivation of iteration formulae of OLCS-Ranker and some additional results.

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 ${\ell}_2$ 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.

INTRODUCTION: Intracranial atherosclerotic disease (ICAD) is responsible for 8–10% of acute ischemic strokes, and resistance to antiplatelet therapy is prevalent. CYP2C19 gene loss-of-function (up to 45% of patients) causes clopidogrel resistance. For patients with asymptomatic ICAD and ICAD characterized by transient ischemic attack (TIA), this study measures the effect of CYP2C19 loss-of-function on ischemic stroke risk during clopidogrel therapy. METHODS: From a deidentified database of medical records, patients were selected with ICD-9/10 code for ICAD, availability of CYP2C19 genotype, clopidogrel exposure, and established patient care. Dual-antiplatelet therapy patients were included. Patients with prior ischemic stroke, other neurovascular condition, intracranial angioplasty/stenting, or observation time

Additional methodology. (DOC 53 kb)

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.

Phenome-wide association is a novel reverse genetic strategy to analyze genome-to-phenome relations in human clinical cohorts. Here we test this approach using a large murine population segregating for ∼5 million sequence variants, and we compare our results to those extracted from a matched analysis of gene variants in a large human cohort. For the mouse cohort, we amassed a deep and broad open-access phenome consisting of ∼4,500 metabolic, physiological, pharmacological and behavioural traits, and more than 90 independent expression quantitative trait locus (QTL), transcriptome, proteome, metagenome and metabolome data sets—by far the largest coherent phenome for any experimental cohort (www.genenetwork.org). We tested downstream effects of subsets of variants and discovered several novel associations, including a missense mutation in fumarate hydratase that controls variation in the mitochondrial unfolded protein response in both mouse and Caenorhabditis elegans, and missense mutations in Col6a5 that underlies variation in bone mineral density in both mouse and human., Phenome-wide association is a novel method that links sequence variants to a spectrum of phenotypes and diseases. Here the authors generate detailed mouse genetic and phenome data which links their phenome-wide association study (PheWAS) of mouse to corresponding PheWAS in human.

Post-database searching is a key procedure for peptide spectrum matches (PSMs) in protein identification with mass spectrometry-based strategies. Although many machine learning-based approaches have been developed to improve the accuracy of peptide identification, the challenge remains for improvement due to the poor quality of data samples. CRanker has shown its effectiveness and efficiency in terms of the number of identified PSMs compared with benchmark algorithms. However, it has two weaknesses: overfitting and instability on small-sized datasets. In this paper, we incorporate two new strategies into CRanker to tackle its weaknesses. First of all, we modify the CRanker model by using different weight parameters for the learning losses of decoy and target PSMs. Moreover, we employ self-paced learning in training process to help the classifier getting avoid of those incorrect PSMs. Experimental studies show the modified CRanker with new strategies is more stable than the original one and outperforms benchmark methods in terms of the number of identified PSMs at the same false discovery rates (FDRs).

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.

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.

Background Peptide 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. Results In 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. Conclusions Compared 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.

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.

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.

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.

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.

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.

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.

Peptide identification is a critical step to understand the proteome in cells and tissue. Typically, high-throughput peptide spectra generated in the MS/MS procedure are searched against real protein sequences by peptide matching. Although a number of automated algorithms have been developed to help identifying those high quality of peptide spectrum matches (PSMs), lack of trustworthy target PSMs remains an open problem. In this paper, we design the FC-Ranker algorithm to calculate the score of each target PSM. A nonnegative weight is assigned to each target PSM to indicate its likelihood of being correct. Particularly, we proposed a fuzzy SVM classification model and a fuzzy silhouette index for iteratively updating the scores of target PSMs. Furthermore, FC-Ranker provides a framework for tackling the problem of uncertainty of target PSMs, and it can be easily adjusted to adapt new datasets.

Systems biology represents a novel approach to comprehensively study the human immune response to vaccines at the global transcriptional and proteomic level. However, most systems vaccinology approaches utilize total PBMCs in their analyses. In this context, responses of underrepresented immune cell types in the blood are potentially obscured by the predominant cells in the PBMC fraction, and the contribution of PMNs is completely ignored. To investigate the contribution of individual cell types in the immune response following vaccination, we developed a rapid and efficient method for purifying large numbers of T cells, B cells, monocytes, NK cells, myeloid DCs and neutrophils from fresh venous human blood for systems vaccinology studies. This optimized protocol was applied to adult volunteers vaccinated with 2011-12 seasonal TIV. 100mL blood was obtained prior to and on days 1, 3, and 7 post-vaccination. Whole blood, PBMC and PMN fractions were subjected to phenotypic analysis by flow cytometry. Immune cells were fractionated and processed for RNA and protein extraction in a single day. RNA-Seq and quantitative proteomics were performed on purified cells in order to determine individual expression profiles. Our results show significant variation in the phenotypes and expression profiles of immune cells at each time point. This innovative systems approach is currently being utilized to evaluate vaccine safety and efficacy in an adjuvanted influenza clinical trial.

Rational design of CD8+ T cell (TCD8)-based vaccines requires knowledge of the immunogenic and protective epitopes presented during infection, information which is currently lacking. Using the clinically successful smallpox vaccine as a model, ~200 novel naturally processed vaccinia viral peptides presented by HLA-A*0201 and -B*0702 molecules were identified and characterized. Humans showed a variegated response to these determinants, reminiscent of the hierarchic response seen in HLA class I transgenic mice. Importantly, multiple TCD8 epitopes were commonly recognized by humans and mice and identified potential targets for protective TCD8-mediated immunity. After acute infection, both dominant and subdominant TCD8 specificities exhibited all of the immunologic features necessary for protection. However, early and efficient presentation of immune determinants during infection ensured protective responses, regardless of dominance, such that subunit vaccination targeting subdominant or recessive TCD8 specificities conferred protection against lethal poxvirus challenge. Hence, an in-depth knowledge of naturally processed T cell epitopes coupled with the identification of TCD8-based targets that protect from lethal poxviral infection are essential for rational vaccine design.

Humans deficient in TAP or ERAAP function present with recurrent infections despite elevated CD4+ T cell numbers. As CD4+ T cells regualte multiple immune response, their actions are critical to anti-microbial responses. Recently, studies have identified a role for TAP and ERAAP in generating influenza, Listeria and minor histocompatibility antigen peptides recognized by CD4+ T cells. Though the MHC class I-restricted self peptidome is known to be regulated by TAP and ERAAP, the affect on MHC class II-restricted self peptidome is unknown. Here we show that the MHC class II-restricted self peptidome is dramatically altered by the activities of TAP and ERAAP. The CD4+ T cell repertoire correspondingly had profound alterations in the diversity of the TCR CDR3 region that defines antigen specificity. This altered CD4+ T cell repertoire led to altered class II-restricted peptide recognition pattern in TAP-deficient mice. Though some peptides were recognized at an enhanced magnitude, the recognition of others was either lost or newly acquired. Taken together these data suggest that TAP and ERAAP sculpt class II-restricted peptidomes and thereby affect the CD4+ T cell repertoire. The recognition of a new, yet overlapping, immunome by TAP deficient CD4+ T cells demands further investigation to understand the extent of interaction between the MHC class I and class II antigen processing pathways and their effects on anti-microbial responses particularly in TAP deficient individuals.

Background Analysis Tool for Proteomics Research (ATP) is our next generation approach for processing, storing and analyzing mass spectrometry-based proteomics data base on our previously developed bioinformatics graphical comparative analysis tools (BIGCAT) software [1]. This software is being developed for the efficient management, visualization, identification, characterization and comparison of peptides and proteins from tandem mass spectrometry experiments. The basic framework of the software suite consists of a back-end relational database to store primary LC-MS/MS data and the peptide/protein identification data from various database search algorithms. The interactive front-end software applications are interfaced to the relational database for managing, visual interpreting, and analyzing the LC-MS/MS results (Figure 1). As an open source and multi-functional analysis software suite, it enables users to 1) customize source code scheme to facilitate specialized experiment aims, 2) manage and load LC-MS/ MS data and protein identification, 3) graphically display and evaluate protein identifications using various views and threshold modifications, 4) compare, merge, and run cluster results from multiple LC-MS/MS experiments, 5) search for neutral losses that are indications of modified peptides, and 6) search immunological peptide motifs. The overall goal is to increase the efficiency and simplicity of managing proteomics data while providing means of publishing and analyzing data on the web in a biologically intuitive and robust way. Acknowledgements We thank Vanderbilt University's Advanced Computing Center for Research and Education (ACCRE), which provides technique support for parallel computing service. Also, thank to Jennifer Jennings for providing from UT-ORNL-KBRIN Bioinformatics Summit 2008 Cadiz, KY, USA. 28–30 March 2008

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.