254 research outputs found

    Identification of leaf rust susceptibility genes in wheat

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    Master of ScienceDepartment of Plant PathologyDavid E Cook IIILeaf Rust, caused by Puccinia triticina, is a major disease of wheat. Leaf rust has proven to be a resilient pathogen, overcoming resistance genes multiple times. Plants have factors, known as susceptibility genes, that facilitate the ability of pathogens to cause disease. Altering susceptibility genes may provide a more durable defense against infection than resistance genes but have yet to be identified for susceptibility to leaf rust in wheat. By characterizing random mutants created through chemical mutagenesis, mechanisms determining wheat susceptibility to leaf rust can be determined. The susceptible wheat variety Thatcher was mutagenized using ethyl methanesulfonate (EMS). Surviving plants were scored for a reduction in pustule size or quantity when challenged with leaf rust. From these plants, three mutant lines (1995, 2048, and 2348) have been obtained. Mutant lines 1995 and 2048 exhibit a constitutive hypersensitive-like response (HR-like). Mutant line 2348 exhibits no evidence of a HR. Microscopic analysis of the initial 5 days of the infection process revealed the ability of P. triticina to form appressoria, early colonization, and pustule development was altered in 1995 and 2048. In 2348, P. triticina was less able to progress beyond appressoria formation and intercellular hyphae than in Thatcher. Bulked segregant RNAseq analysis of F2:3 pools of tissue originating from a backcross to the wild type parent revealed an induction of the plant defense response in 1995 and 2048. Genotypic studies were conducted to identify regions that may contain the causative mutation. A F4 mapping population was generated by crossing each mutant line with the hard red winter wheat variety KS061705M11 and utilizing single seed descent. Association mapping identified SNPs within each population that associated with the mutant phenotype. Confidence intervals surrounding each identified SNP were created through haplotype blocking. A second genotyping method, exome capture, identified SNPs in M8 lines for each mutant. Using the confidence intervals from association mapping, the list of SNPs from exome capture was narrowed. Mutant line 1995 mapped to a 3.89Mb window on 2D, a 7Mb window on 3A, and a 4.85Mb window on 4B. Mutant line 2048 mapped to a 3.89Mb window on 3B and a 4.52Mb window on 4B. These intervals narrow the region of interest for future fine mapping studies that seek to identify the causative mutation

    Host-parasite genomics and ecology: linking genes and transcriptomes to disease and contemporary selection

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    Infectious diseases in natural populations are important areas of research in ecology and evolution as they describe how parasites influence the host fitness. A host may undergo adaptive evolution against the parasite by acquiring either resistance or tolerance through developing intricate biochemical and molecular defence strategies. However, the knowledge about the genes associated with these traits remains limited. Furthermore, the strength of ongoing natural selection on transcript abundance has not been studied directly, despite the fact that gene regulation has a major role in adaptive evolution. In this thesis, I applied genomic and transcriptomic approaches to study the host parasite system of anadromous brown trout (Salmo trutta) infected with a myxozoan parasite, Tetracapsuloides bryosalmonae. In salmonids, T. bryosalmonae causes an emerging temperature-dependent disease, proliferative kidney disease (PKD). The parasite infects the kidney and spleen of juvenile fish, and at elevated temperatures, causes strong inflammatory response, anaemia and kidney hypertrophy. In this thesis, I performed one of the first association mapping attempts on parasite resistance and tolerance in brown trout and demonstrated the possibilities as well as limitations of association analysis in natural populations. As there was very limited genomic information available for T. bryosalmonae, I also generated an annotated assembly of the parasite transcriptome. Furthermore, by combining -omic approaches with genetic mark-recapture and classical regression-based selection analysis, I demonstrated the effect of temperature-driven parasite-induced contemporary natural selection on transcript abundance and co-regulated gene networks in this wild vertebrate species. I identified several promising candidate genes involved in PKD resistance and severity in brown trout. I also characterized more than three thousand transcripts of T. bryosalmonae. Among these, I also identified four novel protein drug targets, which can help in curing the infected fish. I also showed that the myxozoan parasite induces massive cell proliferation in the fish host whose variation is associated with the survival selection on the co-regulated gene networks. The directional selection on the individual transcript abundances was weak, similar to the published selection estimates on phenotypic traits. Finally, I also discovered many transcripts exhibiting widespread signal of disruptive selection, related to host immune defence, host–pathogen interactions, cellular repair and maintenance. Overall, my thesis showcases the power of integrating ecological and genomic perspectives to gain novel insights into the functional genomic basis of resistance against a parasite, health damage (i.e., anaemia) caused by the parasite and the ongoing associated natural selection in the wild. Altogether, my thesis combines multiple levels of biological complexities and represents a significant step forward towards understanding the molecular basis of T. bryosalmonae and PKD.IsĂ€ntĂ€-loissuhteen genomiikka ja ekologia: geenien ja transkriptomien yhdistĂ€minen sairauksiin ja valintaan arttuvat taudit luonnonpopulaatioissa ovat tĂ€rkeitĂ€ ekologian ja evoluution tutkimuskohteita koska ne kuvaavat, kuinka loinen vaikuttaa isĂ€ntĂ€nsĂ€ kelpoisuuteen. IsĂ€nnĂ€ssĂ€ voi kehittyĂ€ evolutiivisia adaptaatioita loista vastaan joko resistenssin tai toleranssin kautta. TĂ€llaisten piirteiden voidaan katsoa olevan isĂ€nnĂ€ssĂ€ kehittyneitĂ€ biokemiallisia ja molekyylisiĂ€ puolustusstrategioita loisia vastaan. NĂ€ihin piirteisiin liittyviĂ€ geenejĂ€ tunnetaan heikosti. LisĂ€ksi luonnonvalinnan voimakkuutta transkription mÀÀrÀÀn ei ole tutkittu suoraan, huolimatta siitĂ€, ettĂ€ geenisÀÀtelyllĂ€ on tĂ€rkeĂ€ rooli adaptiivisessa evoluutiossa. TĂ€ssĂ€ vĂ€itöskirjassa kĂ€ytin genomisia ja transkriptomisia lĂ€hestymistapoja Tetracapsuloides bryosalmonae -loistartunnan saaneen anadromisen taimenen (Salmo trutta) isĂ€ntĂ€loisjĂ€rjestelmĂ€n tutkimiseen. Lohikaloissa T. bryosalmonae aiheuttaa lĂ€mpötilasta riippuvan sairauden, proliferatiivisen munuaissairauden (PKD). TĂ€mĂ€ Myxozoa-pÀÀjaksoon kuuluva loinen infektoi nuorien kalojen munuaiset ja pernan, ja aiheuttaa korkeissa lĂ€mpötiloissa voimakkaan tulehdusvasteen, anemiaa ja munuaisten liikakasvua. Tein ns. ”association mapping”-analyysin liittyen taimenen resistenssiin ja toleranssiin T. bryosalmonae-loista kohtaan, ja osoitin sekĂ€ assosiaatioanalyysin kĂ€ytön mahdollisuudet ettĂ€ rajoitukset tutkittaessa luonnonpopulaatioita. Koska T. bryosalmonaesta oli saatavilla hyvin vĂ€hĂ€n genomista tietoa, loin myös annotoidun koosteen loisen transkriptomista. LisĂ€ksi, yhdistĂ€mĂ€llĂ€ kolme metodia: ns. ”-omics”-lĂ€hestymistavan, geneettisen merkintĂ€jĂ€lleenpyynnin ja klassiseen regressioon perustuvan valinta-analyysin, pystyin osoittamaan lĂ€mpötilasta riippuvaisen loisinfektion indusoiman luonnonvalinnan vaikutuksen transkription mÀÀrÀÀn ja yhteissÀÀdeltyihin geeniverkostoihin tĂ€ssĂ€ luonnossa elĂ€vĂ€ssĂ€ selkĂ€rankais-lajissa. Tunnistin useita lupaavia kandidaattigeenejĂ€, jotka liittyvĂ€t PKD-resistenssiin ja taudin vaikeusasteeseen taimenessa. Kuvasin myös yli kolmetuhatta T. bryosalmonaen transkriptia. NĂ€iden joukosta tunnistin myös neljĂ€ uutta proteiinilÀÀke-kohdetta, joiden avulla voidaan parantaa tartunnan saaneita kaloja. LisĂ€ksi osoitin, ettĂ€ tĂ€mĂ€ Myxozoan-pÀÀjaksoon kuuluva loinen indusoi massiivista solujen lisÀÀntymistĂ€ kalaisĂ€nnĂ€ssĂ€, ja jonka vaihtelu liittyy selviytymisvalintaan yhteissÀÀdellyissĂ€ geeniverkostoissa. YksittĂ€isten transkriptien runsauteen kohdistuva suuntaava valinta oli heikkoa, ollen samantasoista kuin kirjallisuudessa esitetyt arviot fenotyyppisiin ominaisuuksiin kohdistuvasta valinnasta. Löysin myös monia transkripteja, joihin kohdistui hajoittavaa valintaa. NĂ€mĂ€ liittyvĂ€t isĂ€nnĂ€n immuunipuolustukseen, isĂ€nnĂ€n ja patogeenin vĂ€liseen vuorovaikutukseen, solujen korjaamiseen ja yllĂ€pitoon. VĂ€itöskirjani tuo hyvin esille ekologisten ja genomisten lĂ€hestymistapojen yhdistĂ€misestĂ€ avautuvat uudet tutkimusmahdollisuudet ja nĂ€kökulmat loisen vastustuskyvyn genomiseen ja toiminnalliseen perustaan, loisen aiheuttamaan terveyshaittaan (eli anemiaan) ja siihen liittyvÀÀn luonnonvalintaan. VĂ€itöskirjani yhdistÀÀ monia biologian tasoja ja on merkittĂ€vĂ€ askel kohti T. bryosalmonaen ja PKD:n molekyyliperustan ymmĂ€rtĂ€mistĂ€

    Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

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    This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

    27th Fungal Genetics Conference

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    Program and abstracts from the 27th Fungal Genetics Conference Asilomar, March 12-17, 2013

    27th Fungal Genetics Conference

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    Program and abstracts from the 27th Fungal Genetics Conference Asilomar, March 12-17, 2013

    Genomics of drug resistance in epilepsy

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    Difficulties identifying drug-resistant epilepsy (DRE) at disease onset and complex temporal patterns of epilepsy represent challenges in research and clinical practice. A better understanding of the underlying mechanisms of DRE is needed to enable biomarker development, early diagnosis, and personalised treatments. This work explores the influence of genomic variation on DRE through genome-wide association (GWAS) and heritability analyses. It is part of a collaborative, European Commission funded project: EpiPGX (Epilepsy Pharmacogenomics: delivering biomarkers for clinical use). Individuals with epilepsy were recruited from specialised clinical centres across Europe. Healthy controls were obtained from several publically available sources. To establish whether common genomic variants are associated with DRE, two GWAS were performed by the Author. The first analysis, comparing individuals with DRE and controls with drug-responsive epilepsy, did not reveal any variants with genome-wide significance. The second analysis, comparing individuals with DRE and healthy controls, revealed several loci with genome-wide significance. The top genome-wide association signal (rs75700350), located at 4q31.1, likely represents an artefact. Other findings include the signals at loci 5p13.2, and 11p13, pointing to potentially significant candidate genes, SLC1A2 and SLC1A3, implicated in glutamate reuptake and excitotoxicity. Furthermore, one of these loci has been linked to an important epilepsy comorbidity, autism. The functional variants driving these signals may represent risk factors for drug resistance, epilepsy susceptibility, or variants affecting pathophysiological pathways common to DRE and its comorbidities. The main limitations of these GWAS analyses were small sample sizes and the lack of replication. To explore if drug resistance in epilepsy has a polygenic inheritance component, a single nucleotide polymorphism (SNP) heritability analysis was performed. This analysis yielded an estimate of DRE SNP heritability of 0.22, showing that drug resistance in epilepsy is heritable

    28th Fungal Genetics Conference

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    Full abstracts from the 28th Fungal Genetics Conference Asilomar, March 17-22, 2015

    Bioinformatics

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    This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

    Mechanism-driven hypothesis generation support for a predictive adverse effect in colorectal cancer treatment

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    Diese bioinformatische Dissertation beschreibt die tumorbiologische Hypothesengenierung, insbesondere im Kontext des Kolorektalkarzinoms. Hintergrund der Studien ist eine Beobachtung aus der klinischen Praxis. Verschiedene Autoren berichten, dass bei der Behandlung mit Inhibitoren des Epidermalen Wachstumsfaktor Rezeptors (EGFR), speziell des therapeutischen Antikörpers Cetuximab, eine Minderheit der Patienten die ĂŒbliche Nebenwirkung der HauttoxizitĂ€t nicht oder in deutlich verminderter Form zeigt. Bei diesen Patienten wird gleichzeitig eine reduzierte Wirksamkeit der Therapie beschrieben. Das Ausbleiben der Nebenwirkung wird somit als phĂ€notypischer Biomarker genutzt, um gegebenenfalls die Therapie anzupassen. Nachteilig erscheint in diesem Kontext allerdings die prĂ€ventive Hautpflege sowie die Tatsache, dass eine Cetuximab-Behandlung zunĂ€chst gestartet werden muss, um eine Information ĂŒber die Wirksamkeit zu gewinnen. Dadurch, dass der zugrunde liegende molekulare Mechanismus unbekannt ist, kann keine Vorhersage anhand eines klinischen Test getroffen werden. In der vorliegenden Arbeit war es das Ziel, Hypothesen zu generieren, welche Proteine und zellulĂ€ren Signalwege kausal fĂŒr das unterschiedliche Ansprechverhalten der Patientengruppen sein könnten. Ausgehend von der Annahme, dass natĂŒrliche Keimbahnvarianten in der Erbinformation der Individuen im Behandlungskontext diskriminatorisch wirken, baut die Dissertation auf einem kleinen Datensatz von 23 Exomen von Teilnehmern klinischer Studien auf. Diese Sequenzierungsdaten wurden in genomische Varianten ĂŒberfĂŒhrt und auf ihren potentiellen genetisch-mechanistischen Einfluss hin untersucht. Gezielte EinschrĂ€nkungen wurden dabei anhand einer Modellierung des biomedizinischen Kontextes des Anwendungsfalls eingefĂŒhrt, um die reduzierte Datenlage gezielt mit Informationen anzureichern. Die so erhaltenen Kandidatengene, welche in nachfolgenden praktischen Arbeiten validiert werden mĂŒssen, werden im Einzelnen beschrieben und bewertet. Methodisch ist das Ergebnis dieser Dissertation die „Molecular Systems Map“, eine in Cytoscape modellierte Netzwerkstruktur, die funktionelle Interaktionen zwischen Proteinen interaktiv visualisiert und gleichzeitig als Filter auf Basis des biologischen Kontexts dient. Ziel hierbei ist es, einen biomedizinisch ausgebildeten Fachanwender bei der Generierung von Hypothesen zu unterstĂŒtzen, indem im Gegensatz zu sonst hĂ€ufig anzutreffenden tabellarischen Ansichten die Ergebnisse aus der Sequenzanalyse in eben jenem funktionalen Kontext dargestellt werden. DarĂŒber hinaus wird so die Anwendung von Graphenalgorithmen und die Integration weiterer Daten ermöglicht, z.B. solcher aus komplementĂ€ren ‘omics-Experimenten.This bioinformatics thesis describes work and results from a study on a use case in the context of colorectal cancer. Background of the studies is an observation form the clinical practice. Various authors report that upon treatment with inhibitors of the Epidermal Growth Factor Receptor (EGFR), in particular with the therapeutic antibody Cetuximab, a minority of patients does not, or in a clearly reduced form, show common adverse effects of skin toxicity. For these patients, at the same time a reduced efficacy of the therapy is described. The lack of the adverse effect therefore gets used as a phenotypic biomarker for inducing a switch of therapy. However, preventive skin care during treatment, counteracting the biomarker signal, and the necessity to start the therapy first in order to gain the information, appear unfavorable. As the underlying molecular mechanisms remain elusive, predictions ahead of treatment, e.g. by a clinical test, are not possible yet. In the presented work, the aim was to generate hypotheses, which proteins and cellular signaling pathways might be causal for the differentiating response of the patient groups. Starting from the assumption that naturally occurring germline variations functionally discriminate individuals in the context of the treatment, the thesis builds up on a small dataset of 23 exomes of patients from a clinical study context. These sequencing data were processed to genomic variants and analyzed for their potential influence on the mechanistic level. Targeted restrictions were introduced by modeling the biomedical context of the use case in order to enrich the sparse individual data with further information. The obtained candidate genes, which are necessary to be validated in practical studies, are described and evaluated in detail. Methodologically, the result of the thesis is the „Molecular Systems Map“, a network data structure modeled in Cytoscape, interactively visualizing the functional interactions of proteins and simulatenously filtering the called variants upon the biological context. Here, the aim is to enable biomedical domain experts, beyond scrolling tabular information on called variants, to review their experimental data in the functional context and support them in the hypothesis generation process. Additionally, this provides the opportunity to apply graph algorithms and integrate further data, e.g. such from completary ‘omics experiments
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