Driving Precision of Pediatric VTE Risk-stratification through Genetics.

This study addresses rising incidence of pediatric venous thromboembolism by validating a VTE phenotype and developing a polygenic risk score (PRS) using UK Biobank data. Our findings demonstrate predictive value of the PRS, enhancing VTE risk assessment in clinical settings. Future steps involve integrating the PRS into risk stratification models.

A practical approach to identifying autistic adults within the electronic health record.

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.

Creating an automated contemporaneous cohort in sickle cell anemia to predict survival after disease-modifying therapy.

The Food and Drug Administration requires contemporaneous controls to compare clinical outcomes for participants receiving experimental gene therapy or gene editing clinical trials. However, developing a contemporaneous cohort of rare diseases 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 January 2004 and 30 April 2021. We identified 419 individuals with SCA with consistent medical care defined as followed continuously for >0.5 years with no visit gaps >3.0 years. The median age was 10.2 years (IQR, 1-24 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. Treatment groups were mutually exclusive and in a hierarchical order: hematopoietic stem cell transplant (n = 22)>regular blood transfusion for at least 2 years (n = 56)>hydroxyurea for at least 1 year (n = 243)>no disease-modifying therapy (n = 98). Compared to those receiving no disease-modifying treatment, those treated with hydroxyurea therapy had a 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.

Medication history-wide association studies for pharmacovigilance of pregnant patients.

Background: Systematic exclusion of pregnant people from interventional clinical trials has created a public health emergency for millions of patients through a dearth of robust safety data for common drugs. Methods: We harnessed an enterprise collection of 2.8 M electronic health records (EHRs) from routine care, leveraging data linkages between mothers and their babies to detect drug safety signals in this population at full scale. Our mixed-methods signal detection approach stimulates new hypotheses for post-marketing surveillance agnostically of both drugs and diseases-by identifying 1,054 drugs historically prescribed to pregnant patients; developing a quantitative, medication history-wide association study; and integrating a qualitative evidence synthesis platform using expert clinician review for integration of biomedical specificity-to test the effects of maternal exposure to diverse drugs on the incidence of neurodevelopmental defects in their children. Results: We replicated known teratogenic risks and existing knowledge on drug structure-related teratogenicity; we also highlight 5 common drug classes for which we believe this work warrants updated assessment of their safety. Conclusion: Here, we present roots of an agile framework to guide enhanced medication regulations, as well as the ontological and analytical limitations that currently restrict the integration of real-world data into drug safety management during pregnancy. This research is not a replacement for inclusion of pregnant people in prospective clinical studies, but it presents a tractable team science approach to evaluating the utility of EHRs for new regulatory review programs-towards improving the delicate equipoise of accuracy and ethics in assessing drug safety in pregnancy.

CYP2C19 Loss-of-Function Associated with First-Time Ischemic Stroke in Non-surgical Asymptomatic Carotid Artery Stenosis During Clopidogrel Therapy.

This study measures effect of CYP2C19 genotype on ischemic stroke risk during clopidogrel therapy for asymptomatic, extracranial carotid stenosis patients. 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 <1 month were excluded. Dual antiplatelet therapy patients were included. Patients with carotid endarterectomy or stenting were analyzed in a separate subgroup. Time-to-event analysis using Cox regression was conducted to model ischemic stroke events based on CYP2C19 loss-of-function allele and adjusted with the most predictive covariates from univariate analysis. Covariates included age, gender, race, length of aspirin, length of concurrent antiplatelet/anticoagulant treatment, diabetes, coagulopathy, hypertension, heart disease, atrial fibrillation, and lipid disorder. A total of 1110 patients met selection criteria for medical therapy cohort (median age 68 [interquartile range (IQR) 60-75] years, 64.9% male, 91.9% Caucasian). Median study period was 2.8 [0.8-5.3] years. A total of 47 patients (4.2%) had an ischemic stroke event during study period. CYP2C19 loss-of-function allele was strongly associated with ischemic stroke events (one allele: HR 2.3, 95% CI 1.1-4.7, p=0.020; two alleles: HR 10.2, 95% CI 2.8-36.8, p<0.001) after adjustment. For asymptomatic carotid stenosis patients receiving clopidogrel to prevent ischemic stroke, CYP2C19 loss-of-function allele is associated with 2- to 10-fold increased risk of ischemic stroke. CYP2C19 genotype may be considered when selecting antiplatelet therapy for stroke prophylaxis in non-procedural, asymptomatic carotid stenosis.

Evaluation of the MC4R gene across eMERGE network identifies many unreported obesity-associated variants.

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.

CYP2C19 Loss-of-Function is Associated with Increased Risk of Ischemic Stroke after Transient Ischemic Attack in Intracranial Atherosclerotic Disease.

OBJECTIVES: 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. MATERIALS AND 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 <1 month were excluded. Time-to-event analysis using Cox regression was conducted to model first-time ischemic stroke events based on CYP2C19 loss-of-function allele and adjusted for age, gender, race, length of aspirin, length of concurrent antiplatelet/anticoagulant treatment, diabetes, coagulopathy, hypertension, heart disease, atrial fibrillation, and lipid disorder. Subset analyses were performed for asymptomatic and post-TIA subtypes of ICAD. RESULTS: A total of 337 patients were included (median age 68, 58% male, 88% Caucasian, 26% CYP2C19 loss-of-function). A total of 161 (47.8%) patients had TIA at time of ICAD diagnosis, while 176 (52.2%) were asymptomatic. First-time ischemic stroke was observed among 20 (12.4%) post-TIA ICAD patients and 17 (9.7%) asymptomatic ICAD patients. Median observation time was 2.82 [IQR 1.13-5.17] years. CYP2C19 loss-of-function allele was associated with ischemic stroke event (HR 2.2, 95% CI 1.1-4.3, p=0.020) after adjustment. Post-TIA ICAD patients had a higher risk of ischemic stroke from CYP2C19 loss-of-function (HR 3.4, 95% CI 1.4-8.2, p=0.006). CONCLUSIONS: CYP2C19 loss-of-function was associated with 3-fold increased risk of first-time ischemic stroke for ICAD patients treated with clopidogrel after TIA. This effect was not observed for asymptomatic ICAD. CYP2C19-guided antiplatelet selection may improve stroke prevention in ICAD after TIA.

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.

Development of a System for Postmarketing Population Pharmacokinetic and Pharmacodynamic Studies Using Real-World Data From Electronic Health Records.

Postmarketing 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 postmarketing population PK/PD studies using information available in EHRs.

GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across eMERGE Network.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition. METHODS: First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI). RESULTS: Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p = 1.70 × 10- 20). This effect was consistent in both pediatric (p = 9.92 × 10- 6) and adult (p = 9.73 × 10- 15) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) (p = 3.94 × 10- 8, beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout (p = 1.09 × 10- 4). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p = 3.80 × 10- 8), and another near ZFP90-CDH1 for fibrosis (rs698718, p = 2.74 × 10- 11). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses. CONCLUSIONS: In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings.

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.

Accelerating Precision Drug Development and Drug Repurposing by Leveraging Human Genetics.

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.

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.

Joint mouse-human phenome-wide association to test gene function and disease risk.

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.

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.

An adaptive classification model for peptide identification.

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.

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.