Exploring Off-Targets and Off-Systems for Adverse Drug Reactions via Chemical-Protein Interactome — Clozapine-Induced Agranulocytosis as a Case Study

In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs

Relative effects of mutability and selection on single nucleotide polymorphisms in transcribed regions of the human genome

<p>Abstract</p> <p>Motivation</p> <p>Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation in humans. However, the factors that affect SNP density are poorly understood. The goal of this study was to estimate the relative effects of mutability and selection on SNP density in transcribed regions of human genes. It is important for prediction of the regions that harbor functional polymorphisms.</p> <p>Results</p> <p>We used frequency-validated SNPs resulting from single-nucleotide substitutions. SNPs were subdivided into five functional categories: (i) 5' untranslated region (UTR) SNPs, (ii) 3' UTR SNPs, (iii) synonymous SNPs, (iv) SNPs producing conservative missense mutations, and (v) SNPs producing radical missense mutations. Each of these categories was further subdivided into nine mutational categories on the basis of the single-nucleotide substitution type. Thus, 45 functional/mutational categories were analyzed. The relative mutation rate in each mutational category was estimated on the basis of published data. The proportion of segregating sites (PSSs) for each functional/mutational category was estimated by dividing the observed number of SNPs by the number of potential sites in the genome for a given functional/mutational category. By analyzing each functional group separately, we found significant positive correlations between PSSs and relative mutation rates (Spearman's correlation coefficient, at least r = 0.96, df = 9, <it>P </it>< 0.001). We adjusted the PSSs for the mutation rate and found that the functional category had a significant effect on SNP density (F = 5.9, df = 4, <it>P </it>= 0.001), suggesting that selection affects SNP density in transcribed regions of the genome. We used analyses of variance and covariance to estimate the relative effects of selection (functional category) and mutability (relative mutation rate) on the PSSs and found that approximately 87% of variation in PSS was due to variation in the mutation rate and approximately 13% was due to selection, suggesting that the probability that a site located in a transcribed region of a gene is polymorphic mostly depends on the mutability of the site.</p

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies

Background: Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. Methods: We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Results: Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. Conclusions: The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach

DNA repair genes in cancer predisposition: detection of germline pathogenic variants by multigene panel testing

The 5 to 10% diagnosed cancers are linked to an inherited faulty gene. Mutations in distinct DNA repair systems elevate the susceptibility to various cancer types and germline pathogenic (P) variants in DNA damage repair (DDR) genes BRCA1 and BRCA2 explain only 10-20% of these cases. Currently, new DDR genes have been re-lated to of Breast, Ovarian, colorectal and endometrial cancer, but the prevalence of pathogenic variants remains to be explored. The purpose of this study was to investigate the spectrum and the prevalence of pathogenic variants in DDR pathway genes other than BRCA1/2 and to correlate the genotype with the clinical phenotype. A cohort of 416 patients (298 cases were non-BRCA) was analyzed by next-generation sequencing using a multigene panel of the 28 DDR pathways genes related to Breast, Ovarian, colorectal and endometrial cancer. 41 of 416 affected individual were diagnosed with Lynch syndrome. 213 unique variants in 27 of 28 analyzed genes were found, 37 classified as likely pathogenic/ pathogenic and 177 as variants of un-known significance. 10 of 37 LP/P variants were discovered in 10 patients with Lynch syndrome. It was observed a high incidence of deleterious variants in the ATM, MUTYH, CHEK2 and MSH6 gene. These results support the clinical utility of multigene panel to in-crease the detection of P/LP carriers and to identify new actionable pathogenic gene variants useful for preventive and therapeutic approaches

Development and validation of gold nanoprobes for human SNP detection towards commercial application

Conventional molecular techniques for detection and characterization of relevant nucleic acid (i.e. DNA) sequences are, nowadays, cumbersome, expensive and with reduced portability. The main objective of this dissertation consisted in the optimization and validation of a fast and low-cost colorimetric nanodiagnostic methodology for the detection of single nucleotide polymorphisms (SNPs). This was done considering SNPs associated to obesity of commercial interest for STAB VIDA, and subsequent evaluation of other clinically relevant targets. Also, integration of this methodology into a microfluidic platform envisaging portability and application on points-of-care (POC) was achieved. To warrant success in pursuing these objectives, the experimental work was divided in four sections: i) genetic association of SNPs to obesity in the Portuguese population; ii) optimization and validation of the non-cross-linking approach for complete genotype characterization of these SNPs; iii) incorporation into a microfluidic platform; and iv) translation to other relevant commercial targets. FTO dbSNP rs#:9939609 carriers had higher body mass index (BMI), total body fat mass, waist perimeter and 2.5 times higher risk to obesity. AuNPs functionalized with thiolated oligonucleotides (Au-nanoprobes) were used via the non-cross-linking to validate a diagnostics approach against the gold standard technique - Sanger Sequencing - with high levels of sensitivity (87.50%) and specificity (91.67%). A proof-of-concept POC microfluidic device was assembled towards incorporation of the molecular detection strategy. In conclusion a successful framework was developed and validated for the detection of SNPs with commercial interest for STAB VIDA, towards future translation into a POC device

Doctor of Philosophy

dissertationFunctional vitamin B12 (cobalamin) deficiency is a subtle, progressive clinical disorder affecting 6-23% of elderly adults. Its symptoms, including fatigue, mood disturbances, and decreased strength, are vague and erroneously attributed to aging. Detection of cobalamin deficiency in elderly adults is confounded by clinical heterogeneity and lack of standardization in metabolic tests. Whereas some patients are asymptomatic with slightly altered metabolite profiles, others develop severe clinical outcomes. Better understanding of biologic factors contributing to cobalamin deficiency heterogeneity in older adults is needed. This is a candidate gene association study evaluating the relationship between genetic variation in the cobalamin-transport molecules (transcobalamin II and its receptor) with cobalamin-related outcome parameters in 795 research participants of the Women's Health and Aging 1 and 2 Studies. Research participant DNA was whole genome amplified and genotyped using the iPLEX Sequenom mass spectroscopy platform. Relationships between genotypes and clinical parameters were assessed using two-way analysis of variance and two-way analysis of covariance, on the fixed factors, race and Single Nucleotide Polymorphism genotype. Results of the dissertation research generated several genetic associations that are useful for further hypothesis testing and clinical validation research. In the transcobalamin II gene, two missense variants were associated with homocysteine and methylmalonic acid levels (rs9621049, rs35838082), two intronic variants were associated with serum cobalamin and homocysteine levels (rs4820888, rs4820887), and one missense variant was associated with mean corpuscular volume (rs11801198). A cluster of SNPs in the promoter region of the transcobalamin II gene was associated with the physical performance parameters, hand grip strength, and walking speed. In the transcobalamin II-receptor gene, a missense coding SNP (rs2336573) was associated with mean serum cobalamin concentrations. Scientific advances responsible for the technology used in this dissertation are being incorporated into healthcare. The tailoring of treatment to an individual's genetic make-up is termed Personalized Medicine. To assist nursing professionals in understanding and preparing for use of these technologies, four elements of Personalized Medicine are reviewed, including 1) discovery of novel biology that guides clinical translation mechanisms, 2) genetic risk assessment, 3) molecular diagnostic technology, and 4) pharmacogenetics and pharmacogenomics. Opportunities for nursing profession engagement are addressed

GCAT|Genomes for life: a prospective cohort study of the genomes of Catalonia

PURPOSE: The prevalence of chronic non-communicable diseases (NCDs) is increasing worldwide. NCDs are the leading cause of both morbidity and mortality, and it is estimated that by 2030, they will be responsible for 80% of deaths across the world. The Genomes for Life (GCAT) project is a long-term prospective cohort study that was designed to integrate and assess the role of epidemiological, genomic and epigenomic factors in the development of major chronic diseases in Catalonia, a north-east region of Spain. PARTICIPANTS: At the end of 2017, the GCAT Study will have recruited 20 000 participants aged 40-65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. The GCAT Study has access to the electronic health records of the Catalan Public Healthcare System. Participants will be followed biannually at least 20 years after recruitment. FINDINGS TO DATE: Among all GCAT participants, 59.2% are women and 83.3% of the cohort identified themselves as Caucasian/white. More than half of the participants have higher education levels, 72.2% are current workers and 42.1% are classified as overweight (body mass index ≥25 and <30 kg/m2). We have genotyped 5459 participants, of which 5000 have metabolome data. Further, the whole genome of 808 participants will be sequenced by the end of 2017. FUTURE PLANS: The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites