Accurate HLA type inference using a weighted similarity graph

Abstract Background The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true. Results In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test. Conclusions Our algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors

Data Cleaning, Preliminary Summary and Evaluation of Diagnostic Criteria of T-Cell Data in a Juvenile Onset Diabetes Cohort

Type 1 diabetes mellitus (T1DM) is an autoimmune disease manifested by an autoimmune attack on pancreatic beta-islet cells. T1DM can occur at any age. However, it is most often diagnosed in children, adolescents, or young adults. My thesis is derived from a large longitudinal study of Juvenile Onset Diabetes (JOD) at Children’s Hospital of Pittsburgh. The objectives are: 1) Data cleaning and preliminary summary of the cohort with respect to T-cell data. 2) Evaluating the T-cell data criteria used for the prediction of the diabetes. An extensive data examination was made for accuracy and consistency. A preliminary summary of the stimulation index (SI) for the test analytes and the number of positive antigens was performed by demographic sub-groups, HLA-DQ serotype, and follow up time. Using the ROC analysis, an evaluation of diagnosis test performance based on two different criteria was performed. The JOD dataset had few errors with an error rate under 0.5%. The accuracy and consistency of the data is good. New onsets and first degree relatives (FDRs) nonconverters had a relatively stable SI as well as positive antigen tests results. The SI level and positive test results are higher in new onsets when compared with FDRs. FDR-converters (those subsequently developing diabetes) prior to using insulin have SIs and number of positive antigens similar to FDR-nonconverters; and FDR-converters after starting insulin have results similar to new onsets. The recommended SI cutoff of 1.5 indicating positive response appears reasonable. However, the cutoff still may be optimized for better prediction. Evidence suggests that a lower cutoff within 1.25 to 1.5 may be better and the number of positive antigens could move from ≥4 to greater than 5 or 6. Public health significance: Development of a better understanding of the pattern of T-cell response in diabetes and non-diabetic children, and those progressing to diabetes, may give us tools to predict the early onset of disease. It is this point in time where therapeutic intervention could be focused to help stem the development of T1DM or to dramatically reduce its severity

What has GWAS done for HLA and disease associations?

The major histocompatibility complex (MHC) is located in chromosome 6p21 and contains crucial regulators of immune response, including human leucocyte antigen (HLA) genes, alongside other genes with nonimmunological roles. More recently, a repertoire of noncoding RNA genes, including expressed pseudogenes, has also been identified. The MHC is the most gene dense and most polymorphic part of the human genome. The region exhibits haplotype-specific linkage disequilibrium patterns, contains the strongest cis- and trans-eQTLs/meQTLs in the genome and is known as a hot spot for disease associations. Another layer of complexity is provided to the region by the extreme structural variation and copy number variations. While the HLA-B gene has the highest number of alleles, the HLA-DR/DQ subregion is structurally most variable and shows the highest number of disease associations. Reliance on a single reference sequence has complicated the design, execution and analysis of GWAS for the MHC region and not infrequently, the MHC region has even been excluded from the analysis of GWAS data. Here, we contrast features of the MHC region with the rest of the genome and highlight its complexities, including its functional polymorphisms beyond those determined by single nucleotide polymorphisms or single amino acid residues. One of the several issues with customary GWAS analysis is that it does not address this additional layer of polymorphisms unique to the MHC region. We highlight alternative approaches that may assist with the analysis of GWAS data from the MHC region and unravel associations with all functional polymorphisms beyond single SNPs. We suggest that despite already showing the highest number of disease associations, the true extent of the involvement of the MHC region in disease genetics may not have been uncovered

The Utilization of Volatile Organic Compounds and Human Leukocyte Antigen Genes for Ethnic-Specific Differentiation within Target Populations

Human scent has been previously defined as a complex mixture of volatile organic compounds (VOCs) detected in the headspace above a scent sample. Humans generate odor from several areas of the body including hair, mouth, hand, axillae, and foot. Due to the novelty of human scent research, human scent evidence has been undervalued in the court of law. However, this type of evidence has significant value when physical evidence is not available at crime scenes. To increase the individualization and differentiation power of human scent evidence, this study aimed to further investigate the identification of chemical signatures within axillae of specific ethnicities (Caucasian, Hispanic, and African American) and determine if ethnic-specific genetic signatures are present among Human Leukocyte Antigen (HLA) genes. During the study, the axillae of 68 participants were investigated. Upon collection, samples were extracted using Headspace Solid Phase Micro extraction (HS-SPME) and solvent extraction. The samples were analyzed using Gas Chromatography- Mass Spectrometry (GC-MS). The utilization of SPME immediately followed by solvent extraction complements the extraction of both semi-volatile and non-volatile compounds, thus filling in the gaps of the compounds that could not be recovered using HS-SPME alone. The samples were evaluated statistically via logistic regression and Receiving Operating Characteristic (ROC) curves to evaluate the performance and prediction power of VOCs for ethnicity inferences. The study concluded that logistic regression served as an efficient model predicting the VOCs capable of class characteristic determination when comparing ethnicities. The HLA gene complex was evaluated to determine its contribution to human scent and the ability to differentiate between ethnicities. Using buccal swabs extracted from 31 subjects, five genes were successfully amplified using Multiplex Polymerase Chain Reaction (Multiplex PCR). The Multiplex PCR products were analyzed using capillary electrophoresis. The genotype frequencies were observed, and linear discriminant analysis (LDA) was performed to assess the ability of predicting ethnicity using genotype frequencies of individuals. Four of the five genes predicted ethnicity at 80% or greater accuracy, which validates that the HLA genes (D6S2925, D6S2937, D6S2917, and D6S2787), coupled to the VOCs, can be used as a biomarker for class characteristic determination of an individual

Farmacogenómica aplicada ao tratamento antirretroviral em Portugal

A SIDA é considerada um problema de saúde pública que resultou na morte de mais de 30 milhões de pessoas. Segundo a ONU, até ao final de 2018, cerca de 37,9 milhões da população estava infetada pelo HIV, dos quais, 973 novos casos surgiram em Portugal no mesmo ano. Ainda sem cura, o objetivo principal da terapêutica contra o HIV passa pelo atraso da progressão da doença bem como no alívio da sintomatologia através de fármacos com atividade antirretrovírica. De facto, a terapêutica farmacológica tem contribuído para um aumento da esperança média de vida; no entanto, todos os indivíduos são portadores de um genoma próprio e pequenas variações na sequência de DNA são responsáveis pela interindividualidade observada na resposta terapêutica do HIV. A farmacogenómica consiste no estudo dos fatores genéticos que afetam a farmacocinética e farmacodinâmica dos fármacos. Deste modo, os polimorfismos mais estudados e com maior impacto no combate do HIV encontram se presentes nos genes responsáveis pelo metabolismo de fármacos, como o CYP3A e UGT, e transporte de influxo e efluxo, como o SLC e a família de genes ABC. Vários estudos têm sido desenvolvidos no sentido de compreender o impacto destes polimorfismos e, consequentemente, desenvolver novas estratégias terapêuticas. Aliado à farmacogenómica, surge o termo “medicina personalizada”, que consiste numa terapia adequada e direcionada de acordo com as características genotípicas de cada indivíduo e que tem vindo a ganhar cada vez mais interesse uma vez que permite melhorar a qualidade de vida dos doentes. Esta abordagem terapêutica tem sido implementada através de testes genómicos cujos resultados permitem selecionar o tratamento mais seguro e eficaz para cada indivíduo.AIDS is considered to be a public health problem that has resulted in the death of more than 30 million people. According to the UN, by the end of 2018, about 37.9 million of the population was infected with HIV, of which, 973 new cases emerged in Portugal in the same year. Still without any cure, the main goal of HIV therapy is to delay the progression of the disease as well as to relieve symptoms through antiretroviral drugs. In fact, pharmacological therapy has contributed to an increase in average life expectancy; however, all individuals have its own genome and small variations in the DNA sequence are responsible for the inter individuality observed in the HIV therapeutic response. Pharmacogenomics consists of studying the genetic factors affecting the pharmacokinetics and pharmacodynamics of drugs. Thus, the most studied polymorphisms and with the greatest impact in combating HIV are present in the genes responsible for drug metabolism, such as CYP3A and UGT, and drug transport, such as SLC and the superfamily ABC. Several studies have been developed in order to understand the impact of these polymorphisms and, consequently, develop new therapeutic strategies. The term “personalized medicine” emerges along with pharmacogenomics, which consists of an appropriate and targeted therapy according to the genotypic characteristics of each individual and which has been gaining more interest, since it allows improving the quality of life of patients. This therapeutic approach has been implemented through genomic tests whose results allow the selection of the safest and most effective treatment for each HIV positive individual

O papel da farmacogenética na terapia do HIV

Dissertação para obtenção do grau de Mestre no Instituto Superior de Ciências da Saúde Egas MonizA pandemia da SIDA já matou cerca de 36 milhões de pessoas em todo o mundo e, hoje em dia, mais de 35.3 milhões vivem com HIV. A HAART tornou, desde a sua implementação, esta doença incurável e devastadora, numa doença crónica gerível. No entanto, o plano terapêutico para a HAART nem sempre é bem sucedido e indivíduos sob esta terapia são obrigados a descontinuar o tratamento ou devido a uma baixa eficácia ou a uma elevada toxicidade levando, potencialmente, a reações de hipersensibilidade severas ou mesmo fatais. Estas diferenças interindividuais residem, em grande parte, no genoma de cada paciente, sendo este o maior responsável pelas grandes variações na resposta terapêutica. A farmacogenética tenta investigar estas relações genéticas com o objetivo de costumizar individualmente cada terapia ainda na sua fase de planeamento, levando assim à escolha da combinação ideal de fármacos antirretrovirais para cada paciente tendo em vista o aumento máximo da eficácia terapêutica com o mínimo de toxicidade. Este trabalho baseia-se numa análise bibliográfica que tem como objetivo relacionar a farmacogenética e a terapia do HIV, abordando os diferentes tipos de fármacos na terapia antirretroviral: os inibidores nucleosídeos da transcriptase reversa, os inibidores não-nucleosídeos da transcriptase reversa e os inibidores da protease, e a forma como as variações em diversos genes modificam a resposta terapêutica. Esta análise debruça-se então sobre as principais isoformas de algumas proteínas de metabolização e a influência destas sobre os principais fármacos utilizados atualmente como parte da HAART. É ainda feita uma, mais breve, menção às três novas classes de antirretrovirais: os inibidores de fusão, os antagonistas CCR5 e os inibidores da integrase. São delineadas as investigações passadas e os mais recentes avanços, bem como quais as possíveis avenidas a percorrer em investigações futuras

Das HLA-Molekül - strukturelle und funktionelle Analyse einer Schlüsselfigur des Immunsystems

Das HLA-Molekül als Schlüsselmolekül im Immunsystem spielt eine wichtige Rolle in der Unterscheidung in Selbst und Fremd sowie in der Initiierung des adaptiven Immunsystems gegen maligne Transformationen von körpereigenen Zellen. Die hohe Variabilität dieses Proteins macht es nicht nur für die Forschung und Transplantationsmedizin interessant, sondern spielt seit einiger Zeit auch in der individuellen Krebstherapie eine wichtige Rolle. Die Auswahl der Peptide für eine Peptid-basierten Immuntherapie kann anhand ihres Potenzials zytotoxische CD8+ T-Zellen in vitro zu induzieren getroffen werden. Um die Peptide in vitro zu testen werden künstliche Zellen mit HLA:Peptid-Komplexen sowie kostimulatorischen Antikörpern (αCD28) beladen, um mittels zusätzlicher Zytokine (IL-2 und IL-12) Antigen-spezifische CD8+ T-Zellen zu generieren. Innerhalb dieser Analysen konnten für das MultiPro-Projekt sieben potentiell immunogene Peptide identifiziert werden. Aus diesen Peptiden wurden Kandidaten anhand ihrer unterschiedlichen HLA-Restriktion und Antigenherkunft ausgewählt, um Teil eines Peptid-Cocktails für Prostatakrebspatienten mit biochemischem Rezidiv zu werden. Um die Anzahl immunogener Peptide zu erhöhen wurden Optimierungen auf kostimulatorischer Ebene mithilfe weiterer Antikörper (OX40) und Zytokine (IL-7, IL-15, IL-21) durchgeführt. Dadurch konnte eine Erhöhung der Lebendzellzahl (OX40) oder Steigerung der intrazellulären Funktion (IL-21) erreicht werden. Die HLA-Typisierung ist nicht nur für Gewebe-oder Zelltransplantationen wichtig, sondern auch im Zusammenhang mit der Peptid-basierten Immuntherapie, da die Allotyp-abhängige Peptidpräsentation für die meisten Tumore und somit Patienten individuell ist. Eine alternative Typisierungsstrategie wurde entwickelt um unabhängig von der molekulargenetischen Typisierung zu werden. Diese Strategie setzt sich anhand der hohen Komplexität der Proben (hoch polymorphe HLA-Genloci und Verunreinigungen) aus einer Kombination der Proteomik wie auch Ligandomik zusammen. Durch die 186 identifizierten enzymatischen HLA Klasse I-Fragmente ist es möglich eine alternative Typisierung auf Proteom-Ebene vorzunehmen. Hierüber besteht zudem die Möglichkeit eine relative Quantifizierung mit isotopenmarkierten Peptiden zu erhalten. Dies kann Aufschluss über Unterschiede der HLA-Moleküle auf Tumorzellen im Vergleich zu gesundem Gewebe geben, aber auch Korrelationen der HLA-Liganden-Ausbeute deutlich machen. Dafür wurden 15 isotopenmarkierte Peptide hergestellt, wovon 14 erfolgreich identifiziert und für einen ersten Versuch der Quantifizierung verwendet werden konnten. Um diese Strategie noch effizienter zu machen, wird in Kooperation mit der Bioinformatik ein Algorithmus entwickelt, der die Auswertung und Handhabung optimieren wird