181 research outputs found
Personalized Medicine: the Future of Health Care
BACKGROUND: Most medical treatments have been designed for the “average patients”. As a result of this “one-size-fits-all-approach”, treatments can be very successful for some patients but not for others. The issue is shifting by the new innovation approach in diseases treatment and prevention, precision medicine, which takes into account individual differences in people\u27s genes, environments, and lifestyles. This review was aimed to describe a new approach of healthcare performance strategy based on individual genetic variants.CONTENT: Researchers have discovered hundreds of genes that harbor variations contributing to human illness, identified genetic variability in patients\u27 responses to different of treatments, and from there begun to target the genes as molecular causes of diseases. In addition, scientists are developing and using diagnostic tests based on genetics or other molecular mechanisms to better predict patients\u27 responses to targeted therapy.SUMMARY: Personalized medicine seeks to use advances in knowledge about genetic factors and biological mechanisms of disease coupled with unique considerations of an individual\u27s patient care needs to make health care more safe and effective. As a result of these contributions to improvement in the quality of care, personalized medicine represents a key strategy of healthcare reform
Reproductive management in dairy cows : the future
Background: Drivers of change in dairy herd health management include the significant increase in herd/farm size, quota removal (within Europe) and the increase in technologies to aid in dairy cow reproductive management.
Main body: There are a number of key areas for improving fertility management these include: i) handling of substantial volumes of data, ii) genetic selection (including improved phenotypes for use in breeding programmes), iii) nutritional management (including transition cow management), iv) control of infectious disease, v) reproductive management (and automated systems to improve reproductive management), vi) ovulation / oestrous synchronisation, vii) rapid diagnostics of reproductive status, and viii) management of male fertility. This review covers the current status and future outlook of many of these key factors that contribute to dairy cow herd health and reproductive performance.
Conclusions: In addition to improvements in genetic trends for fertility, numerous other future developments are likely in the near future. These include: i) development of new and novel fertility phenotypes that may be measurable in milk; ii) specific fertility genomic markers; iii) earlier and rapid pregnancy detection; iv) increased use of activity monitors; v) improved breeding protocols; vi) automated inline sensors for relevant phenotypes that become more affordable for farmers; and vii) capturing and mining multiple sources of "Big Data" available to dairy farmers. These should facilitate improved performance, health and fertility of dairy cows in the future
AN IN VITRO INVESTIGATION INTO THE MECHANISM OF THE CLINICALLY RELEVANT DRUG-DRUG INTERACTION BETWEEN OMEPRAZOLE OR ESOMEPRAZOLE AND CLOPIDOGREL
Clopidogrel is a thienopyridine antiplatelet prodrug that was approved by the US FDA in 1997 and quickly supplanted ticlopidine as the primary drug therapy for reducing atherothrombotic events. It is converted to its pharmacologically active metabolite H4, which irreversibly inactivates the P2Y12 receptor on platelets, through two sequential reactions that are catalyzed mainly by CYP2C19. Common clinical practice involved the coadministration of a proton pump inhibitor (PPI, including omeprazole, esomeprazole, lansoprazole, pantoprazole, and rabeprazole) with clopidogrel to decrease the risk of upper gastrointestinal bleeding. This practice was formalized for high risk patients by the American Heart Association (and others) in 2008. By 2009, numerous publications described an unexpected decrease in clopidogrel efficacy when coadministered with PPIs, prompting both the US Food & Drug Administration (FDA) and European Medicines Agency (EMA) to issue recommendations discouraging the concomitant use of PPIs and clopidogrel. Proton pump inhibitors are also metabolized by CYP2C19. It seemed reasonable to conclude that, despite their relatively short plasma half-lives, PPIs might competitively inhibit CYP2C19, thereby reducing the efficacy of clopidogrel. In 2010, as numerous publications emerged, both regulatory agencies restricted subsequent warnings to only omeprazole and esomeprazole. The interaction between clopidogrel and PPIs, and the potential mechanisms responsible for it, continues to be a subject of much debate in 2015. This dissertation describes research that contributes to the progress made in understanding the basis for the interaction between clopidogrel and PPIs since the time of the initial regulatory statements, and in particular, why only omeprazole and esomeprazole are implicated in this drug interaction. The initial studies in this dissertation identified omeprazole (a racemic mixture of R- and S-enantiomers) and esomeprazole (the S-enantiomer) as not only competitive inhibitors, but more importantly, metabolism-dependent inhibitors (MDIs) of CYP2C19 in human liver microsomes (HLM), human hepatocytes and recombinant CYP2C19. In contrast, lansoprazole and pantoprazole did not cause metabolism-dependent inhibition (MDI) of CYP2C19. In addition to its clinical relevance, these observations are important because they underscore the importance of using a low concentration of enzyme and a short incubation time with the CYP marker substrate in order to detect MDI of CYP enzymes in vitro. In many previous studies of CYP2C19 inhibition by omeprazole or esomeprazole, the concentration of HLM was too high and/or the substrate incubation time was too long to detect MDI. The kinetic parameters for CYP2C19 inactivation by omeprazole, namely kinact and KI, were determined and used in a physiologically based pharmacokinetic (PBPK) model to predict the degree of CYP2C19 inactivation under clinical conditions. Omeprazole and esomeprazole were subsequently shown to be irreversible MDIs of CYP2C19, which explained why the decrease in clopidogrel efficacy could not be prevented in clinical studies by simply separating the doses of clopidogrel from omeprazole or esomeprazole. Subsequent studies demonstrated that, like the parent drug, two of the three major metabolites of omeprazole are also irreversible MDIs of CYP2C19. The kinetic parameters for CYP2C19 inactivation by these metabolites were determined and, along with those for omeprazole and esomeprazole, used in a mechanistic static model to predict the reduction of H4 formation from clopidogrel under clinical conditions. The model slightly overpredicted (by a factor of 2) the ability of omeprazole to block the conversion of clopidogrel to H4, its pharmacologically active metabolites, but otherwise established that inactivation of CYP2C19 is the likely mechanism for the clinical interaction between omeprazole/esomeprazole and clopidogrel. Esomeprazole and its two inhibitory metabolites, namely omeprazole sulfone and 5 O desmethylomeprazole, were subsequently determined to meet several criteria for mechanism-based inhibition (a special case of irreversible MDI). In addition, studies were initiated to test the hypothesis that the mechanism of CYP2C19 inactivation by esomeprazole and its metabolites involves the formation of a benzylic radical (on the 5,,S-methyl group) that binds covalently to the heme moiety. This hypothesis was based on the observation that the 5,,S methyl group is present on the pyridine ring of those compounds that irreversibly inactivate CYP2C19, namely omeprazole, esomeprazole, omeprazole sulfone and 5 O desmethylomeprazole, but absent from those compounds that did not inactivate CYP2C19, namely lansoprazole, pantoprazole and 5,,S-hydroxyomeprazole. Based on this hypothesis, the investigational PPI, tenatoprazole, which contains a 5,,S-methyl group, was correctly predicted to cause MDI of CYP2C19 whereas ilaprazole and rabeprazole, which lack a 5,,S-methyl group, did not cause MDI of CYP2C19. These results suggest that the investigational PPI, tenatoprazole, but not the clinically used PPIs ilaprazole or rabeprazole, may compromise the therapeutic effectiveness of clopidogrel. Finally, studies were performed in an attempt to provide direct evidence for the proposed mechanism of inactivation of CYP2C19 by esomeprazole, namely the formation of a heme adduct. The potential for the formation of a heme adduct in incubations of esomeprazole in HLM was evaluated by UHPLC analysis with UV/VIS detection and high resolution mass spectrometry (HRMS) with post-acquisition mass-defect filtering to identify heme and heme-containing adducts. Incubation of esomeprazole with NADPH-fortified HLM resulted in a substantial decrease in the amount of heme detectable by UHPLC with either UV absorbance or HRMS and appeared to show the formation of a heme adduct based on mass-defect filtering and isotopic distribution. However, the putative heme adduct was subsequently identified as a dimer of esomeprazole sulfone (a metabolite of esomeprazole formed by CYP3A4/5). Although an adduct between heme and a metabolite of esomeprazole was not ultimately identified, the potential for an unusual analytical artifact was revealed; namely, that sulfur-containing drugs can be converted to metabolites that closely resemble a heme adduct based on mass-defect filtering and isotopic distribution. In summary, this dissertation supports the hypothesis that irreversible inactivation of CYP2C19 is the mechanism by which omeprazole and esomeprazole reduce the efficacy of clopidogrel. This property is not shared by lansoprazole, pantoprazole, rabeprazole or ilaprazole. These findings support regulatory agencies¡¦ recommendations that, in order to reduce the risk of gastrointestinal bleeding, clopidogrel should not be coadministered with omeprazole or esomeprazole but should be coadministered with other PPIs that do not inactivate CYP2C19
Bioinformatics assisted breeding, from QTL to candidate genes
Over the last decade, the amount of data generated by a single run of a NGS sequencer outperforms days of work done with Sanger sequencing. Metabolomics, proteomics and transcriptomics technologies have also involved producing more and more information at an ever faster rate. In addition, the number of databases available to biologists and breeders is increasing every year. The challenge for them becomes two-fold, namely: to cope with the increased amount of data produced by these new technologies and to cope with the distribution of the information across the Web. An example of a study with a lot of ~omics data is described in Chapter 2, where more than 600 peaks have been measured using liquid chromatography mass-spectrometry (LCMS) in peel and flesh of a segregating F1apple population. In total, 669 mQTL were identified in this study. The amount of mQTL identified is vast and almost overwhelming. Extracting meaningful information from such an experiment requires appropriate data filtering and data visualization techniques. The visualization of the distribution of the mQTL on the genetic map led to the discovery of QTL hotspots on linkage group: 1, 8, 13 and 16. The mQTL hotspot on linkage group 16 was further investigated and mainly contained compounds involved in the phenylpropanoid pathway. The apple genome sequence and its annotation were used to gain insight in genes potentially regulating this QTL hotspot. This led to the identification of the structural gene leucoanthocyanidin reductase (LAR1) as well as seven genes encoding transcription factors as putative candidates regulating the phenylpropanoid pathway, and thus candidates for the biosynthesis of health beneficial compounds. However, this study also indicated bottlenecks in the availability of biologist-friendly tools to visualize large-scale QTL mapping results and smart ways to mine genes underlying QTL intervals. In this thesis, we provide bioinformatics solutions to allow exploration of regions of interest on the genome more efficiently. In Chapter 3, we describe MQ2, a tool to visualize results of large-scale QTL mapping experiments. It allows biologists and breeders to use their favorite QTL mapping tool such as MapQTL or R/qtl and visualize the distribution of these QTL among the genetic map used in the analysis with MQ2. MQ2provides the distribution of the QTL over the markers of the genetic map for a few hundreds traits. MQ2is accessible online via its web interface but can also be used locally via its command line interface. In Chapter 4, we describe Marker2sequence (M2S), a tool to filter out genes of interest from all the genes underlying a QTL. M2S returns the list of genes for a specific genome interval and provides a search function to filter out genes related to the provided keyword(s) by their annotation. Genome annotations often contain cross-references to resources such as the Gene Ontology (GO), or proteins of the UniProt database. Via these annotations, additional information can be gathered about each gene. By integrating information from different resources and offering a way to mine the list of genes present in a QTL interval, M2S provides a way to reduce a list of hundreds of genes to possibly tens or less of genes potentially related to the trait of interest. Using semantic web technologies M2S integrates multiple resources and has the flexibility to extend this integration to more resources as they become available to these technologies. Besides the importance of efficient bioinformatics tools to analyze and visualize data, the work in Chapter 2also revealed the importance of regulatory elements controlling key genes of pathways. The limitation of M2S is that it only considers genes within the interval. In genome annotations, transcription factors are not linked to the trait (keyword) and to the gene it controls, and these relationships will therefore not be considered. By integrating information about the gene regulatory network of the organism into Marker2sequence, it should be able to integrate in its list of genes, genes outside of the QTL interval but regulated by elements present within the QTL interval. In tomato, the genome annotation already lists a number of transcription factors, however, it does not provide any information about their target. In Chapter 5, we describe how we combined transcriptomics information with six genotypes from an Introgression Line (IL) population to find genes differentially expressed while being in a similar genomic background (i.e.: outside of any introgression segments) as the reference genotype (with no introgression). These genes may be differentially expressed as a result of a regulatory element present in an introgression. The promoter regions of these genes have been analyzed for DNA motifs, and putative transcription factor binding sites have been found. The approaches taken in M2S (Chaper 4) are focused on a specific region of the genome, namely the QTL interval. In Chapter 6, we generalized this approach to develop Annotex. Annotex provides a simple way to browse the cross-references existing between biological databases (ChEBI, Rhea, UniProt, GO) and genome annotations. The main concept of Annotex being, that from any type of data present in the databases, one can navigate the cross-references to retrieve the desired type of information. This thesis has resulted in the production of three tools that biologists and breeders can use to speed up their research and build new hypothesis on. This thesis also revealed the state of bioinformatics with regards to data integration. It also reveals the need for integration into annotations (for example, genome annotations, protein annotations, and pathway annotations) of more ontologies than just the Gene Ontology (GO) currently used. Multiple platforms are arising to build these new ontologies but the process of integrating them into existing resources remains to be done. It also confirms the state of the data in plants where multiples resources may contain overlapping. Finally, this thesis also shows what can be achieved when the data is made inter-operable which should be an incentive to the community to work together and build inter-operable, non-overlapping resources, creating a bioinformatics Web for plant research.</p
Systems and chemical biology approaches to study cell function and response to toxins
Toxicity is one of the main causes of failure during drug discovery, and of withdrawal once drugs reached the market. Prediction of potential toxicities in the early stage of drug development has thus become of great interest to reduce such costly failures. Since toxicity
results from chemical perturbation of biological systems, we combined biological and chemical strategies to help understand and ultimately predict drug toxicities.
First, we proposed a systematic strategy to predict and understand the mechanistic interpretation of drug toxicities based on chemical fragments. Fragments frequently found in chemicals with certain toxicities were defined as structural alerts for use in prediction. Some of the predictions were supported with mechanistic interpretation by integrating fragmentchemical,
chemical-protein, protein-protein interactions and gene expression data.
Next, we systematically deciphered the mechanisms of drug actions and toxicities by analyzing the associations of drugs’ chemical features, biological features and their gene expression profiles from the TG-GATEs database. We found that in vivo (rat liver) and in vitro (rat hepatocyte) gene expression patterns were poorly overlapped and gene expression responses in different species (rat and human) and different tissues (liver and kidney) varied widely.
Eventually, for further understanding of individual differences in drug responses, we reviewed how genetic polymorphisms influence the individual's susceptibility to drug toxicity by deriving chemical-protein interactions and SNP variations from Mechismo database. Such a study is also essential for personalized medicine.
Overall, this study showed that, integrating chemical and biological in addition to genetic data can help assess and predict drug toxicity at system and population levels
Molecular Genetics, Genomics and Biotechnology of Crop Plants Breeding
This Special Issue on molecular genetics, genomics, and biotechnology in crop plant breeding seeks to encourage the use of the tools currently available. It features nine research papers that address quality traits, grain yield, and mutations by exploring cytoplasmic male sterility, the delicate control of flowering in rice, the removal of anti-nutritional factors, the use and development of new technologies for non-model species marker technology, site-directed mutagenesis and GMO regulation, genomics selection and genome-wide association studies, how to cope with abiotic stress, and an exploration of fruit trees adapted to harsh environments for breeding purposes. A further four papers review the genetics of pre-harvest spouting, readiness for climate-smart crop development, genomic selection in the breeding of cereal crops, and the large numbers of mutants in straw lignin biosynthesis and deposition
Structural Characterization of Potential Cancer Biomarker Proteins
abstract: Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.Dissertation/ThesisM.S. Biochemistry 201
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An Informatics Roadmap Toward a FAIR Understanding of Mitochondrial Biology and Rare Mitochondrial Disease
Mitochondrial biology is integral to our fundamental understanding of human health and many diseases. They exist in every human cell type except for red blood cells and have critical functions in metabolism, oxidative phosphorylation, oxidation-reduction, and as signaling hubs responsible for mediating protective mechanisms. Rare mitochondrial diseases (RMDs) are devastating and complex, affect multiple organ systems, and disproportionately impact young children. Despite copious existing knowledge and increased public interest, the knowledge is fragmented and difficult to access. Clinical case reports (CCRs) on RMDs contain valuable clinical insights, but they are scarce and lack the metadata necessary to facilitate their discovery among the two million CCRs on PubMed. The unstructured text data of CCRs is also ill-suited to computational approaches, limiting our ability to derive the knowledge contained within.To address these issues, I assembled all available informatics tools and resources with mitochondrial components and used them to contribute to Gene Wiki pages that enable easy access to mitochondrial knowledge for researchers, students, clinicians, and patients. Through these efforts, I made mitochondrial gene, protein, and disease knowledge widely accessible with contributions of over 4MB of content across 541 Gene Wiki pages. Concurrently, I used Gene Wiki as an educational platform to train over 50 students in the biosciences and pre-medical studies in mitochondrial biology and disease, as well as instilling effective research and writing methods in biomedicine.To impose structure on CCRs and render them FAIR (Findable, Accessible, Interoperable, Reusable), I developed and applied a standardized metadata template to RMD CCRs and codified patient symptomology with the International Statistical Classification of Disease and Related Health Problems (ICD) system. I created the open-source, cloud-based MitoCases RMD Knowledge Platform (http://mitocases.org/) to house data on 384 RMD CCRs, including 4,561 instances of 952 unique ICD codes. Supplementing CCRs with structured metadata amplifies machine-readable information content and provides a distinct improvement in searching for CCRs as compared to indexing by title and abstract. Finally, I employed these resources to conduct a thorough review of Barth syndrome and characterized the diversity of presentations, range of genetic etiologies, and treatment paradigms
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