Medicine and Health Sciences Commons^™

Dna Methylation-Based Epigenetic Biomarkers In Cell-Type Deconvolution And Tumor Tissue Of Origin Identification, Ze Zhang

Dartmouth College Ph.D Dissertations

DNA methylation is an epigenetic modification that regulates gene expression and is essential to establishing and preserving cellular identity. Genome-wide DNA methylation arrays provide a standardized and cost-effective approach to measuring DNA methylation. When combined with a cell-type reference library, DNA methylation measures allow the assessment of underlying cell-type proportions in heterogeneous mixtures. This approach, known as DNA methylation deconvolution or methylation cytometry, offers a standardized and cost-effective method for evaluating cell-type proportions. While this approach has succeeded in discerning cell types in various human tissues like blood, brain, tumors, skin, breast, and buccal swabs, the existing methods have major …

Genome-Scale Methylation Analysis In Blood And Tumor Identifies Immune Profile, Age Acceleration, And Dna Methylation Alterations Associated With Bladder Cancer Outcomes, Ji-Qing Chen

Dartmouth College Ph.D Dissertations

Bladder cancer patients receive frequent screening due to the high tumor recurrence rate (more than 60%). Nowadays, the conventional monitoring method relies on cystoscopy which is highly invasive and increases patient morbidity and burden to the health care system with frequent follow-up. As a result, it is urgent to explore novel markers related to the outcomes of bladder cancer. Immune profiles have been associated with cancer outcomes and may have the potential to be biomarkers for outcomes management. However, little work has been conducted to investigate the associations of immune cell profiles with bladder cancer outcomes. Here, I utilized the …

The E2f4 Prognostic Signature Predicts Pathological Response To Neoadjuvant Chemotherapy In Breast Cancer Patients, Kenneth M. K. Mark, Frederick S. Varn, Matthew H. Ung, Feng Qian, Chao Cheng

Dartmouth Scholarship

Neoadjuvant chemotherapy is a key component of breast cancer treatment regimens and pathologic complete response to this therapy varies among patients. This is presumably due to differences in the molecular mechanisms that underlie each tumor’s disease pathology. Developing genomic clinical assays that accurately categorize responders from non-responders can provide patients with the most effective therapy for their individual disease. We applied our previously developed E2F4 genomic signature to predict neoadjuvant chemotherapy response in breast cancer. E2F4 individual regulatory activity scores were calculated for 1129 patient samples across 5 independent breast cancer neoadjuvant chemotherapy datasets. Accuracy of the E2F4 signature in …

Genetic Variants Of Ptpn2 Are Associated With Lung Cancer Risk: A Re-Analysis Of Eight Gwass In The Tricl-Ilcco Consortium, Yun Feng, Yanru Wang, Hongliang Liu, Zhensheng Liu, Coleman Mills, Younghun Han, Rayjean J. Hung, Yonathan Brhane, John Mcclaughlin, Paul Brennan

Dartmouth Scholarship

The T-cell protein tyrosine phosphatase (TCPTP) pathway consists of signaling events mediated by TCPTP. Mutations and genetic variants of some genes in the TCPTP pathway are associated with lung cancer risk and survival. In the present study, we first investigated associations of 5,162 single nucleotide polymorphisms (SNPs) in 43 genes of this TCPTP pathway with lung cancer risk by using summary data of six published genome-wide association studies (GWAS) of 12,160 cases and 16,838 controls. We identified 11 independent SNPs in eight genes after correction for multiple comparisons by a false discovery rate < 0.20. Then, we performed in silico functional analyses for these 11 SNPs by eQTL analysis, two of which, PTPN2 SNPs rs2847297 and rs2847282, were chosen as tagSNPs. We further included two additional GWAS datasets of Harvard University (984 cases and 970 controls) and deCODE (1,319 cases and 26,380 controls), and the overall effects of these two SNPs among all eight GWAS studies remained significant (OR = 0.95, 95% CI = 0.92–0.98, and P = 0.004 for rs2847297; OR = 0.95, 95% CI = 0.92–0.99, and P = 0.009 for rs2847282). In conclusion, the PTPN2 rs2847297 and rs2847282 may be potential susceptible loci for lung cancer risk.

A Novel Multi-Network Approach Reveals Tissue-Specific Cellular Modulators Of Fibrosis In Systemic Sclerosis, Jaclyn N. Taroni, Casey S. Greene, Viktor Martyanov, Tammara A. Wood

Dartmouth Scholarship

Systemic sclerosis (SSc) is a multi-organ autoimmune disease characterized by skin fibrosis. Internal organ involvement is heterogeneous. It is unknown whether disease mechanisms are common across all involved affected tissues or if each manifestation has a distinct underlying pathology.We used consensus clustering to compare gene expression profiles of biopsies from four SSc-affected tissues (skin, lung, esophagus, and peripheral blood) from patients with SSc, and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension, and derived a consensus disease-associate signature across all tissues. We used this signature to query tissue-specific functional genomic networks. We performed novel network analyses to contrast …

Familial Lung Cancer: A Brief History From The Earliest Work To The Most Recent Studies, Anthony Musolf, Claire Simpson, Mariza De Andrade, Diptasri Mandal, Colette Gaba, Ping Yang, Yafang Li

Dartmouth Scholarship

Lung cancer is the deadliest cancer in the United States, killing roughly one of four cancer patients in 2016. While it is well-established that lung cancer is caused primarily by environmental effects (particularly tobacco smoking), there is evidence for genetic susceptibility. Lung cancer has been shown to aggregate in families, and segregation analyses have hypothesized a major susceptibility locus for the disease. Genetic association studies have provided strong evidence for common risk variants of small-to-moderate effect. Rare and highly penetrant alleles have been identified by linkage studies, including on 6q23–25. Though not common, some germline mutations have also been identified …

Application Of Rnai-Induced Gene Expression Profiles For Prognostic Prediction In Breast Cancer, Yue Wang, Kenneth . M. K. Mark, Matthew H. Ung, Arminja Kettenbach, Todd Miller, Wei Xu, Wenqing Cheng Cheng, Tian Xia, Chao Cheng

Dartmouth Scholarship

Homologous recombination (HR) is the primary pathway for repairing double-strand DNA breaks implicating in the development of cancer. RNAi-based knockdowns of BRCA1 and RAD51 in this pathway have been performed to investigate the resulting transcriptomic profiles. Here we propose a computational framework to utilize these profiles to calculate a score, named RNA-Interference derived Proliferation Score (RIPS), which reflects cell proliferation ability in individual breast tumors. RIPS is predictive of breast cancer classes, prognosis, genome instability, and neoadjuvant chemosensitivity. This framework directly translates the readout of knockdown experiments into potential clinical applications and generates a robust biomarker in breast cancer.

Detecting Gene-Gene Interactions Using A Permutation-Based Random Forest Method, Jing Li, James D. Malley, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore

Dartmouth Scholarship

Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions.

Cross-Platform Normalization Of Microarray And Rna-Seq Data For Machine Learning Applications, Jeffrey A. Thompson, Jie Tan, Casey S. Greene

Dartmouth Scholarship

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log …

Leveraging Global Gene Expression Patterns To Predict Expression Of Unmeasured Genes, James Rudd, René A. Zelaya, Eugene Demidenko, Ellen L. Goode, Casey S. Greene S. Greene, Jennifer A. Doherty

Dartmouth Scholarship

BackgroundLarge collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

Genome-Wide Meta-Analysis In Alopecia Areata Resolves Hla Associations And Reveals Two New Susceptibility Loci, Regina C. Betz, Lynn Petukhova, Stephan Ripke, Hailiang Huang, Androniki Menelaou, Silke Redeler, Tim Becker, Stefanie Heilmann, Tarek Yamany, Madeleine Duvic, Maria Hordinsky, David Norris, Vera H. Price, Julian Mackay-Wiggan, Annemieke De Jong, Gina M. Destefano, Susanne Moebus, Markus Böhm, Ulrike Blume-Peytavi, Hans Wolff, Gerhard Lutz, Roland Kruse, Li Bian, Christopher I. Amos

Dartmouth Scholarship

Alopecia areata (AA) is a prevalent autoimmune disease with ten known susceptibility loci. Here we perform the first meta-analysis in AA by combining data from two genome-wide association studies (GWAS), and replication with supplemented ImmunoChip data for a total of 3,253 cases and 7,543 controls. The strongest region of association is the MHC, where we fine-map 4 independent effects, all implicating HLA-DR as a key etiologic driver. Outside the MHC, we identify two novel loci that exceed statistical significance, containing ACOXL/BCL2L11(BIM) (2q13); GARP (LRRC32) (11q13.5), as well as a third nominally significant region SH2B3(LNK)/ ATXN2 (12q24.12). Candidate susceptibility gene expression …

Integrative Analysis Of Survival-Associated Gene Sets In Breast Cancer, Frederick S. Varn, Matthew H. Ung, Shao Ke Lou, Chao Cheng

Dartmouth Scholarship

Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient’s cancer. Identifying robust gene sets that are consistently predictive of a patient’s clinical outcome has become one of the main challenges in the field. We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that …

Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore

Dartmouth Scholarship

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …

Systems Level Analysis Of Systemic Sclerosis Shows A Network Of Immune And Profibrotic Pathways Connected With Genetic Polymorphisms, J. Matthew Mahoney, Jaclyn Taroni, Viktor Martyanov, Tammara A. A. Wood, Casey S. Greene, Patricia A. Pioli, Monique E. Hinchcliff, Michael L. Whitfield

Dartmouth Scholarship

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes …

Dprp: A Database Of Phenotype-Specific Regulatory Programs Derived From Transcription Factor Binding Data, David T. W. Tzeng, Yu-Ting Tseng, Matthew Ung, I-En Liao, Chun-Chi Liu, Chao Cheng

Dartmouth Scholarship

Gene expression profiling has been extensively used in the past decades, resulting in an enormous amount of expression data available in public databases. These data sets are informative in elucidating transcriptional regulation of genes underlying various biological and clinical conditions. However, it is usually difficult to identify transcription factors (TFs) responsible for gene expression changes directly from their own expression, as TF activity is often regulated at the posttranscriptional level. In recent years, technical advances have made it possible to systematically determine the target genes of TFs by ChIP-seq experiments. To identify the regulatory programs underlying gene expression profiles, we …

Myeloid Derived Hypoxia Inducible Factor 1-Alpha Is Required For Protection Against Pulmonary Aspergillus Fumigatus Infection, Kelly M. Shepardson, Anupam Jhingran, Alayna Caffrey, Joshua J. Obar, Benjamin T. Suratt, Brent L. Berwin, Tobias M. Hohl, Robert A. Cramer

Dartmouth Scholarship

Hypoxia inducible factor 1α (HIF1α) is the mammalian transcriptional factor that controls metabolism, survival, and innate immunity in response to inflammation and low oxygen. Previous work established that generation of hypoxic microenvironments occurs within the lung during infection with the human fungal pathogen Aspergillus fumigatus. Here we demonstrate that A. fumigatus stabilizes HIF1α protein early after pulmonary challenge that is inhibited by treatment of mice with the steroid triamcinolone. Utilizing myeloid deficient HIF1α mice, we observed that HIF1α is required for survival and fungal clearance early following pulmonary challenge with A. fumigatus. Unlike previously reported research with bacterial …

The Genetic Interacting Landscape Of 63 Candidate Genes In Major Depressive Disorder: An Explorative Study, Magnus Lekman, Ola Hössjer, Peter Andrews, Henrik Källberg, Daniel Uvehag, Dennis Charney, Husseini Manji, John A. Rush, Frances J. Mcmahon, Jason H Moore, Ingrid Kockum

Dartmouth Scholarship

Background:

Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear.

This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and …

Genome-Wide Dna Methylation Profiles In Progression To In Situ And Invasive Carcinoma Of The Breast With Impact On Gene Transcription And Prognosis, Thomas Fleischer, Arnoldo Frigessi, Kevin C. Johnson, Hege Edvardsen, Nizar Touleimat, Jovana Klajic, Margit Lh Riis, Vilde D. Haakensen, Fredrik Wärnberg, Bjørn Naume, Åslaug Helland, Anne-Lise Børresen-Dale, Jörg Tost, Brock C. Christensen, Vessela N. Kristensen

Dartmouth Scholarship

Background: Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development. Results: We generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals …

Structural Features Of The Pseudomonas Fluorescens Biofilm Adhesin Lapa Required For Lapg-Dependent Cleavage, Biofilm Formation, And Cell Surface Localization, Chelsea D. Boyd, T. Jarrod Smith, Sofiane El-Kirat-Chatel, Peter D. Newell, Yves F. Dufrêne, George A. O'Toole

Dartmouth Scholarship

The localization of the LapA protein to the cell surface is a key step required by Pseudomonas fluorescens Pf0-1 to irreversibly attach to a surface and form a biofilm. LapA is a member of a diverse family of predicted bacterial adhesins, and although lacking a high degree of sequence similarity, family members do share common predicted domains. Here, using mutational analysis, we determine the significance of each domain feature of LapA in relation to its export and localization to the cell surface and function in biofilm formation. Our previous work showed that the N terminus of LapA is required for …

Methylation Of Leukocyte Dna And Ovarian Cancer: Relationships With Disease Status And Outcome, Brooke L. Fridley, Sebastian M. Armasu, Mine S. Cicek, Melissa C. Larson, Chen Wang, Stacey J. Winham, Kimberly R. Kalli, Devin C. Koestler

Dartmouth Scholarship

Genome-wide interrogation of DNA methylation (DNAm) in blood-derived leukocytes has become feasible with the advent of CpG genotyping arrays. In epithelial ovarian cancer (EOC), one report found substantial DNAm differences between cases and controls; however, many of these disease-associated CpGs were attributed to differences in white blood cell type distributions. We examined blood-based DNAm in 336 EOC cases and 398 controls; we included only high-quality CpG loci that did not show evidence of association with white blood cell type distributions to evaluate association with case status and overall survival.

How To Get The Most From Microarray Data: Advice From Reverse Genomics, Ivan P. Gorlov, Ji-Yeon Yang, Jinyoung Byun, Christopher Logothetis, Olga Y. Gorlova, Kim-Anh Do, Christopher Amos

Dartmouth Scholarship

Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data–derived predictor of known cancer associated genes. We found that the traditional approach of identifying cancer genes—identifying differentially expressed genes—is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results …

Integrated Assessment Of Predicted Mhc Binding And Cross-Conservation With Self Reveals Patterns Of Viral Camouflage, Lu He, Anne S. De Groot, Andres H. Gutierrez, William D. Martin, Lenny Moise, Chris Bailey-Kellogg

Dartmouth Scholarship

Immune recognition of foreign proteins by T cells hinges on the formation of a ternary complex sandwiching a constituent peptide of the protein between a major histocompatibility complex (MHC) molecule and a T cell receptor (TCR). Viruses have evolved means of "camouflaging" themselves, avoiding immune recognition by reducing the MHC and/or TCR binding of their constituent peptides. Computer-driven T cell epitope mapping tools have been used to evaluate the degree to which articular viruses have used this means of avoiding immune response, but most such analyses focus on MHC-facing ‘agretopes'. Here we set out a new means of evaluating the …

Interaction Between Allelic Variations In Vitamin D Receptor And Retinoid X Receptor Genes On Metabolic Traits, Karani S. Vimaleswaran, Alana Cavadino, Diane J. Berry, Massimo Mangino, Peter Andrews, Jason H. Moore

Dartmouth Scholarship

Low vitamin D status has been shown to be a risk factor for several metabolic traits such as obesity, diabetes and cardiovascular disease. The biological actions of 1, 25-dihydroxyvitamin D, are mediated through the vitamin D receptor (VDR), which heterodimerizes with retinoid X receptor, gamma (RXRG). Hence, we examined the potential interactions between the tagging polymorphisms in the VDR (22 tag SNPs) and RXRG (23 tag SNPs) genes on metabolic outcomes such as body mass index, waist circumference, waist-hip ratio (WHR), high- and low-density lipoprotein (LDL) cholesterols, serum triglycerides, systolic and diastolic blood pressures and glycated haemoglobin in the 1958 …

Recurrent Tissue-Specific Mtdna Mutations Are Common In Humans, David C. Samuels, Chun Li, Bingshan Li, Zhuo Song, Eric Torstenson, Hayley Boyd Clay, Antonis Rokas, Tricia A. Thornton-Wells, Jason H. Moore, Tia M. Hughes, Robert D. Hoffman, Jonathan L. Haines, Deborah G. Murdock, Douglas P. Mortlock, Scott M. Williams

Dartmouth Scholarship

Mitochondrial DNA (mtDNA) variation can affect phenotypic variation; therefore, knowing its distribution within and among individuals is of importance to understanding many human diseases. Intra-individual mtDNA variation (heteroplasmy) has been generally assumed to be random. We used massively parallel sequencing to assess heteroplasmy across ten tissues and demonstrate that in unrelated individuals there are tissue-specific, recurrent mutations. Certain tissues, notably kidney, liver and skeletal muscle, displayed the identical recurrent mutations that were undetectable in other tissues in the same individuals. Using RFLP analyses we validated one of the tissue-specific mutations in the two sequenced individuals and replicated the patterns in …

Transcription Factor Binding Profiles Reveal Cyclic Expression Of Human Protein-Coding Genes And Non-Coding Rnas, Chao Cheng, Matthew Ung, Gavin D. Grant, Michael L. Whitfield

Dartmouth Scholarship

Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and …

Chapter 11: Genome-Wide Association Studies, William S. Bush, Jason H. Moore

Dartmouth Scholarship

Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.

Two Boundaries Separate Borrelia Burgdorferi Populations In North America, Gabriele Margos, Jean I. Tsao, Santiago Castillo-Ramirez, Yvette A. Girard, Anne G. Hoen

Dartmouth Scholarship

Understanding the spread of infectious diseases is crucial for implementing effective control measures. For this, it is important to obtain information on the contemporary population structure of a disease agent and to infer the evolutionary processes that may have shaped it. Here, we investigate on a continental scale the population structure of Borrelia burgdorferi, the causative agent of Lyme borreliosis (LB), a tick-borne disease, in North America. We test the hypothesis that the observed d population structure is congruent with recent population expansions and that these were preceded by bottlenecks mostly likely caused by the near extirpation in the 1900s …

Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit

Dartmouth Scholarship

There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.

Development Of Pyrf-Based Genetic System For Targeted Gene Deletion In Clostridium Thermocellum And Creation Of A Pta Mutant, Shital A. Tripathi, Daniel G. Olson, D. Aaron Argyros, Bethany B. Miller, Trisha F. Barrett, Daniel M. Murphy, Jesse D. Mccool, Anne K. Warner, Vineet B. Rajgarhia, Lee R. Lynd, David A. Hogsett, Nicky C. Caiazza

Dartmouth Scholarship

We report development of a genetic system for making targeted gene knockouts in Clostridium thermocellum, a thermophilic anaerobic bacterium that rapidly solubilizes cellulose. A toxic uracil analog, 5-fluoroorotic acid (5-FOA), was used to select for deletion of the pyrF gene. The ΔpyrF strain is a uracil auxotroph that could be restored to a prototroph via ectopic expression of pyrF from a plasmid, providing a positive genetic selection. Furthermore, 5-FOA was used to select against plasmid-expressed pyrF, creating a negative selection for plasmid loss. This technology was used to delete a gene involved in organic acid production, namely pta, which encodes …

Optimization Algorithms For Functional Deimmunization Of Therapeutic Proteins, Andrew S. Parker, Wei Zheng, Karl E. Griswold, Chris Bailey-Kellogg

Dartmouth Scholarship

To develop protein therapeutics from exogenous sources, it is necessary to mitigate the risks of eliciting an anti-biotherapeutic immune response. A key aspect of the response is the recognition and surface display by antigen-presenting cells of epitopes, short peptide fragments derived from the foreign protein. Thus, developing minimal-epitope variants represents a powerful approach to deimmunizing protein therapeutics. Critically, mutations selected to reduce immunogenicity must not interfere with the protein's therapeutic activity.